CN104991233A

CN104991233A - Networking radar anti-cheating interference method based on signal level fusion

Info

Publication number: CN104991233A
Application number: CN201510366792.9A
Authority: CN
Inventors: 刘楠; 李升远; 郭玉梅; 张林让; 周宇; 赵珊珊
Original assignee: Xidian University
Current assignee: Xidian University
Priority date: 2015-06-29
Filing date: 2015-06-29
Publication date: 2015-10-21
Anticipated expiration: 2035-06-29
Also published as: CN104991233B

Abstract

The invention provides a networked radar anti-deception jamming method based on signal level fusion, which can effectively identify false targets generated by different deception jamming. Including: step 1, constructing the slow-time random complex envelope sequence of each point target in multiple node radars; step 2, estimating the average power of each point target in each node radar; step 3, combining the same point target in multiple The slow-time random complex envelope sequences of a node radar are combined in pairs to form multiple envelope groups, and the correlation coefficient of each envelope group is estimated; step 4, respectively select the real part of the correlation coefficient of each envelope group as its Correlation measure; Step 5, calculate the inspection threshold of the correlation measure corresponding to each envelope group; Step 6, judge whether the correlation measure is greater than the check threshold, when the correlation measure is less than or equal to the check threshold, determine that the envelope group passes False target inspection; otherwise, the two point targets corresponding to the envelope group are marked as false targets; Step 7, remove the false targets.

Description

Anti-spoofing jamming method for networked radar based on signal level fusion

技术领域technical field

本发明涉及雷达技术领域，特别涉及一种基于信号级融合的组网雷达抗欺骗式干扰方法。The invention relates to the field of radar technology, in particular to a method for anti-spoofing jamming of networked radar based on signal level fusion.

背景技术Background technique

电子欺骗技术致力于在方向、位置、跟踪起点等信息方面对受害雷达进行欺骗或是在真实目标回波周围制造很多假目标以至于真实目标信息不能被提取出来。一种有效的电子欺骗技术类别为欺骗式电子欺骗技术。欺骗的目的是通过调制的发射或转发对雷达接收回波的幅度、相位等信息进行误导。尤其是数字射频存储器(Digital Radio Frequency Memory，DRFM)的出现使得欺骗式干扰技术更加成熟，应用了DRFM的转发式干扰机广泛用于自卫式干扰以及随队干扰中。欺骗式干扰会占用大量的系统资源，严重影响雷达系统的探测和跟踪性能。Electronic spoofing technology is dedicated to deceiving the victim radar in terms of information such as direction, position, and tracking starting point, or creating so many false targets around the real target echo that the real target information cannot be extracted. One effective class of spoofing techniques is deceptive spoofing techniques. The purpose of deception is to mislead the amplitude, phase and other information of the echo received by the radar through modulated transmission or forwarding. In particular, the emergence of digital radio frequency memory (Digital Radio Frequency Memory, DRFM) has made deceptive jamming technology more mature, and transponder jammers using DRFM are widely used in self-defense jamming and team jamming. Deceptive jamming will occupy a large amount of system resources and seriously affect the detection and tracking performance of the radar system.

针对欺骗式假目标干扰，单站雷达由于视角单一，很难对其进行对抗，而组网雷达可利用点迹关联的方法对检测到的目标进行真假判别，并剔除掉假目标，从而实现欺骗式干扰的对抗。但是，由于组网雷达中每个节点雷达均会受到欺骗式干扰，密集假目标会导致每个节点雷达的量测值间进行关联检验的错误率较高，且组网雷达布站位置不理想，也会影响组网雷达对抗欺骗式干扰的能力。For deceptive false target interference, it is difficult for single-station radar to fight against it due to its single viewing angle, while networked radar can use the method of dot trace correlation to distinguish the true and false of the detected target and eliminate the false target, so as to realize Countering deceptive jamming. However, since each node radar in the networked radar will be subject to deceptive interference, dense false targets will lead to a high error rate in the correlation test between the measured values of each node radar, and the location of the networked radar station is not ideal , will also affect the ability of networked radars to resist deceptive jamming.

现有组网雷达都是利用数据级融合对欺骗式干扰进行对抗，在雷达对目标测量的过程中，只利用了目标的点迹信息或者航迹信息，但是其他信息并没有有效的利用，因此，数据级融合抗干扰方法不能完全发挥其抗干扰能力，无法充分利用雷达组网优势。Existing networked radars use data-level fusion to counteract deceptive jamming. In the process of measuring targets, only the point track information or track information of the target is used, but other information is not effectively used. Therefore, , the data-level fusion anti-jamming method cannot fully exert its anti-jamming ability, and cannot make full use of the advantages of radar networking.

发明内容Contents of the invention

针对上述现有方法对抗欺骗式假目标干扰的不足，本发明的目的在于提出一种网络化雷达系统信号级抗欺骗式干扰方法，能够对不同欺骗式干扰产生的假目标进行有效鉴别。In view of the shortcomings of the above-mentioned existing methods for combating deceptive false target jamming, the purpose of the present invention is to propose a signal-level anti-deceptive jamming method for networked radar systems, which can effectively identify false targets generated by different deceptive jamming.

为了达到上述目的，本发明采用以下技术方案予以实现。In order to achieve the above object, the present invention adopts the following technical solutions to achieve.

一种基于信号级融合的组网雷达抗欺骗式干扰方法，所述组网雷达包括多个节点雷达，所述方法包括以下步骤：A networked radar anti-spoofing jamming method based on signal level fusion, the networked radar includes a plurality of node radars, and the method includes the following steps:

步骤1，计算匹配滤波后点目标的回波数据的复幅度，并根据所述复幅度构造每个点目标在所述多个节点雷达的慢时间随机复包络序列；Step 1, calculating the complex amplitude of the echo data of the point target after the matched filtering, and constructing the slow-time random complex envelope sequence of each point target in the multiple node radars according to the complex amplitude;

步骤2，根据所述每个点目标在所述多个节点雷达的慢时间随机复包络序列，估计每个点目标在每个节点雷达的平均功率；Step 2, estimating the average power of each point target in each node radar according to the slow time random complex envelope sequence of each point target in the plurality of node radars;

步骤3，对于同一点目标，将其在所述多个节点雷达的慢时间随机复包络序列两两组合，构成多个包络组，并估计每个包络组的相关系数；Step 3, for the same point target, combine its slow-time random complex envelope sequences in the multiple node radars in pairs to form multiple envelope groups, and estimate the correlation coefficient of each envelope group;

步骤4，对于同一点目标，分别选取所述每个包络组的相关系数的实部作为每个包络组对应的相关性度量；Step 4, for the same point target, respectively select the real part of the correlation coefficient of each envelope group as the correlation measure corresponding to each envelope group;

步骤5，给定组网雷达的真实目标误判概率，并依据所述组网雷达的真实目标误判概率计算所述每个包络组对应的相关性度量的检验门限；Step 5, given the real target misjudgment probability of the networked radar, and calculating the inspection threshold of the correlation measure corresponding to each envelope group according to the real target misjudged probability of the networked radar;

步骤6，将所述相关性度量与所述检验门限进行比较，判断所述相关性度量是否大于所述检验门限，当所述相关性度量小于等于所述检验门限时，判定所述相关性度量对应的包络组通过假目标检验；当所述相关性度量大于所述检验门限时，判定所述相关性度量对应的包络组未通过假目标检验，并将该包络组对应的两个点目标标定为假目标；Step 6, comparing the correlation metric with the inspection threshold, judging whether the correlation metric is greater than the inspection threshold, when the correlation metric is less than or equal to the inspection threshold, determining the correlation metric The corresponding envelope group passes the false target inspection; when the correlation metric is greater than the inspection threshold, it is determined that the envelope group corresponding to the correlation metric fails the false target inspection, and the two corresponding envelope groups are The point target is marked as a false target;

步骤7，剔除所述假目标。Step 7, removing the false target.

优选地，所述步骤1包括以下子步骤：Preferably, said step 1 includes the following sub-steps:

1a)设所述组网雷达包括n个节点雷达，其中n≥2，每个节点雷达接收回波数据，并采用以下公式对所述回波数据进行匹配滤波，得到匹配滤波后点目标的回波数据y(t)：1a) Assuming that the networked radar includes n node radars, where n≥2, each node radar receives the echo data, and uses the following formula to perform matching filtering on the echo data to obtain the echo data of the point target after the matching filtering Wave data y(t):

$y the y ((t t)) = = x x ((t t)) &CircleTimes; &CircleTimes; {x x}^{* *} ((- - t t))$

其中，x(t)为回波数据，为卷积符号，*表示共轭；Among them, x(t) is the echo data, is the convolution symbol, * means conjugate;

1b)采用以下公式对所述匹配滤波后点目标的回波数据进行相干积累，得到相干积累后点目标的回波数据Y(k)：1b) Using the following formula to coherently accumulate the echo data of the point target after the matched filtering, and obtain the echo data Y(k) of the point target after coherent accumulation:

$Y Y ((k k)) = = {Σ Σ}_{m m = = 00}^{Q Q - - 11} y the y ((m m)) {e e}^{- - j j \frac{22 π π}{Q Q} k k m m}$

其中，Q为脉冲积累个数，y(m)为匹配滤波后点目标的回波数据；Among them, Q is the number of accumulated pulses, and y(m) is the echo data of the point target after matched filtering;

1c)设所述相干积累后点目标的回波数据中包括P个点目标的回波数据，对所述相干积累后点目标的回波数据进行恒虚警检测，分别得到P个点目标的回波数据的复幅度；1c) Assuming that the echo data of the point targets after the coherent accumulation includes the echo data of P point targets, the constant false alarm detection is performed on the echo data of the point targets after the coherent accumulation, and the P point targets are obtained respectively. the complex amplitude of the echo data;

1d)将所述P个点目标的回波数据的复幅度组成集合，并将所述集合作为P个点目标的慢时间随机复包络序列 1d) Composing the complex amplitudes of the echo data of the P point targets into a set, and using the set as a slow-time random complex envelope sequence of the P point targets

${X x}_{{p p}_{i i}}^{i i} = = {{{A A}_{{p p}_{i i}}^{i i},, i i = = 11,, 22,, 33 ... ... ... ...,, n no}},, {p p}_{i i} = = 11,, 22,, 33 ... ... . . P P$

其中，n表示所述组网雷达中的节点雷达个数，且n≥2；表示所述n个节点雷达中的第i个节点雷达检测到的第p_i个点目标的回波数据的复幅度；是一个矩阵，行数为每个相干处理周期中所有脉冲数，列数为点目标的个数P。Wherein, n represents the number of node radars in the networked radar, and n≥2; Representing the complex amplitude of the echo data of the p _i th point target detected by the i th node radar in the n node radars; is a matrix, the number of rows is the number of all pulses in each coherent processing cycle, and the number of columns is the number P of point targets.

优选地，所述步骤2包括以下子步骤：Preferably, said step 2 includes the following sub-steps:

2a)设所述组网雷达包括n个节点雷达，其中n≥2，设所述匹配滤波后点目标的回波数据中包括P个点目标，从所述n个节点雷达中选取第i个节点雷达，并选取所述第i个节点雷达检测到的第p_i个点目标的回波数据的复幅度 2a) It is assumed that the networked radar includes n node radars, wherein n≥2, and the echo data of the point target after the matched filtering is assumed to include P point targets, and the i-th node is selected from the n node radars Node radar, and select the complex amplitude of the echo data of the p _i point target detected by the i node radar

2b)根据所述第i个节点雷达检测到的第p_i个点目标的回波数据的复幅度通过以下公式计算所述点目标p_i在第i个节点雷达的平均功率的估计值 2b) According to the complex amplitude of the echo data of the pi point target detected by the _i th node radar Calculate the estimated value of the average power of the point target p _i at the i-th node radar by the following formula

${ζ ζ}_{{p p}_{i i},, i i}^{22} = = \frac{{A A}_{{p p}_{i i}}^{i i}^{H h} {A A}_{{p p}_{i i}}^{i i}}{Q Q},, i i = = 11,, 22,, 33,, ... ...,, n no$

其中，Q为相干处理周期中PRT的个数，H表示矩阵的共轭转置。Among them, Q is the number of PRTs in the coherent processing cycle, and H represents the conjugate transpose of the matrix.

优选地，所述步骤3包括以下子步骤：Preferably, said step 3 includes the following sub-steps:

3a)设所述组网雷达包括n个节点雷达，其中n≥2，设所述匹配滤波后点目标的回波数据中包括P个点目标；从所述n个节点雷达中选取第i个节点雷达和第j个节点雷达，从所述P个点目标中选取第p个点目标，对于所述第i个节点雷达，所述第p个点目标为第p_i个点目标；对于所述第j个节点雷达，所述第p个点目标为第p_j个点目标，将所述第i个节点雷达检测的第p_i个点目标的慢时间随机复包络序列和第j个节点雷达检测的第p_j个点目标的慢时间随机复包络序列进行组合，构成包络组；3a) It is assumed that the networked radar includes n node radars, wherein n≥2, and the echo data of the point target after the matched filtering is assumed to include P point targets; select the i-th point target from the n node radars The node radar and the jth node radar select the p point target from the P point targets, and for the i node radar, the p point target is the p _i point point target; for all The _jth node radar, the pth point target is the pjth point target, and the slow time random complex envelope sequence of the p _i point target detected by the ith node radar and the slow-time random complex envelope sequence of the p _jth point target detected by the jth node radar Combine to form an envelope group;

3b)通过以下公式计算所述和构成的包络组的相关系数 3b) Calculate the described by the following formula and The correlation coefficients of the envelope groups formed

${\overset{^^}{ρ ρ}}_{{p p}_{i i},, {p p}_{j j}} = = {(({X x}_{{p p}_{i i}}^{i i}))}^{H h} {X x}_{{p p}_{j j}}^{j j},, i i &NotEqual; &NotEqual; j j,, i i = = 11,, 22,, 33,, ... ... n no,, j j = = 11,, 22,, 33 ... ... n no$

其中，H表示矩阵的共轭转置；Among them, H represents the conjugate transpose of the matrix;

3c)重复3b)至得到每个包络组的相关系数。3c) Repeat 3b) until the correlation coefficient of each envelope group is obtained.

优选地，所述步骤4包括以下子步骤：Preferably, said step 4 includes the following sub-steps:

根据每个包络组的相关系数通过以下公式选取其实部作为每个包络组对应的相关性度量 According to the correlation coefficient of each envelope group The real part is selected as the correlation measure corresponding to each envelope group by the following formula

${μ μ}_{{p p}_{i i},, {p p}_{j j}} = = r r e e a a l l (({\overset{^^}{ρ ρ}}_{{p p}_{i i},, {p p}_{j j}}))$

其中，real()表示对取实部。Among them, real() means to Take the real part.

优选地，所述步骤5包括以下子步骤：Preferably, said step 5 includes the following sub-steps:

5a)给定组网雷达的真实目标误判概率P_l；5a) The real target misjudgment probability P _l of the given networked radar;

5b)根据所述组网雷达的真实目标误判概率P_l，通过以下公式计算每个包络组对应的相关度量的检验门限 5b) According to the real target misjudgment probability P _l of the networked radar, calculate the inspection threshold of the correlation metric corresponding to each envelope group by the following formula

${ξ ξ}_{{p p}_{i i},, {p p}_{j j}} = = \sqrt{{Qζ Qζ}_{{p p}_{i i},, i i}^{22} {ζ ζ}_{{p p}_{j j},, j j}^{22} / / 22} \cdot &Center Dot; {Φ Φ}^{- - 11} ((11 - - {((11 - - {P P}_{l l}))}^{11 / / P P}))$

其中，Φ()表示标准正态分布，Q为相干处理周期中PRT的个数，P为检测得到的目标个数，表示点目标p_i在第i个节点雷达和第j个节点雷达的平均功率。Among them, Φ() represents the standard normal distribution, Q is the number of PRTs in the coherent processing cycle, P is the number of detected targets, Indicates the average power of the point target p _i in the i-th node radar and the j-th node radar.

优选地，所述步骤6包括以下子步骤：Preferably, said step 6 includes the following sub-steps:

6a)将所述相关性度量与所述检验门限进行比较；6a) Measure the correlation with the inspection threshold Compare;

6b)当时，判定所述相关性度量对应的包络组通过假目标检验；6b) when When , it is determined that the envelope group corresponding to the correlation measure passes the false target test;

6c)当时，判定所述相关性度量对应的包络组未通过假目标检验，并将该包络组对应的两个点目标标定为假目标。6c) when When , it is determined that the envelope group corresponding to the correlation measure fails the false target test, and the two point targets corresponding to the envelope group are marked as false targets.

优选地，所述步骤7包括以下子步骤：Preferably, said step 7 includes the following sub-steps:

7a)查找被标定为假目标的两个点目标所在的包络组对应的相关性度量；7a) Find the correlation measure corresponding to the envelope group where the two point targets marked as false targets are located;

7b)将所述相关性度量对应的点目标回波数据的复幅度置为零。7b) Set the complex amplitude of the point target echo data corresponding to the correlation measure to zero.

本发明与现有技术相比，具有以下优点：Compared with the prior art, the present invention has the following advantages:

第一，本发明利用真实目标回波的复包络相互独立而干扰信号复包络相关的特点，通过步骤3对于同一点目标，将其在所述多个节点雷达的慢时间随机复包络序列两两组合，构成多个包络组，并估计每个包络组的相关系数，由于真实目标的回波相关系数比较小，因此可以利用信号级融合处理，使得目标的信息得到了较高的使用率，最终能够更有效的对抗欺骗式干扰。First, the present invention utilizes the characteristics that the complex envelopes of real target echoes are independent of each other and the complex envelopes of interference signals are correlated. Through step 3, for the same point target, the complex envelopes in the slow time of the multiple node radars are randomized. Sequences are combined in pairs to form multiple envelope groups, and the correlation coefficient of each envelope group is estimated. Since the echo correlation coefficient of the real target is relatively small, signal-level fusion processing can be used to obtain higher target information. The usage rate can eventually be more effective against deceptive interference.

第二，本发明只用到信号包络，未出现任何调制方式，因此能够不依赖于欺骗式干扰的信号调制方式，从而可以对不同欺骗式干扰方式产生的假目标进行有效鉴别。Second, the present invention only uses the signal envelope without any modulation method, so it can not rely on the signal modulation method of deceptive interference, so that false targets generated by different deceptive interference methods can be effectively identified.

第三，本发明可用于网络化雷达系统融合中心，通过对目标的包络进行相关性检验，能够鉴别欺骗式干扰产生的有源假目标，实现网络化雷达系统有效对抗欺骗式干扰。Thirdly, the present invention can be used in the fusion center of the networked radar system. By performing a correlation check on the envelope of the target, active false targets generated by deceptive interference can be identified, and the networked radar system can effectively resist deceptive interference.

附图说明Description of drawings

图1为本发明一种基于信号级融合的组网雷达抗欺骗式干扰方法的流程图；Fig. 1 is a flow chart of a networked radar anti-spoofing jamming method based on signal level fusion in the present invention;

图2是本发明的网络化雷达系统抗欺骗式干扰方法的实现流程图；Fig. 2 is the realization flowchart of the networked radar system anti-spoofing jamming method of the present invention;

图3为TNR＝0dB、3dB、6dB、9dB时，假目标正确鉴别概率P_FT随积累脉冲个数Q的变化曲线图，其中，横坐标为脉冲积累个数Q，纵坐标为鉴别概率P_FT；Fig. 3 is a curve diagram of the change curve of false target correct identification probability P _FT with the accumulated pulse number Q when TNR=0dB, 3dB, 6dB, 9dB, wherein, the abscissa is the pulse accumulation number Q, and the ordinate is the identification probability _PFT ;

图4为M＝2、4、8、14时，假目标正确鉴别概率P_FT随积累脉冲个数Q的变化曲线图，其中，横坐标为脉冲积累个数Q，纵坐标为鉴别概率P_FT；Fig. 4 is a graph showing the change curve of false target correct identification probability P _FT with the accumulated pulse number Q when M=2, 4, 8, 14, wherein, the abscissa is the pulse accumulation number Q, and the ordinate is the identification probability P _FT ;

图5为P_l＝0.01，0.005，0.001时，假目标正确鉴别概率P_FT随积累脉冲个数Q的变化曲线图，其中，横坐标为脉冲积累个数Q，纵坐标为鉴别概率P_FT。Fig. 5 is a graph showing the variation of false target correct identification probability P _FT with the accumulated pulse number Q when P _l =0.01, 0.005, 0.001, where the abscissa is the accumulated pulse number Q, and the ordinate is the identification probability P _FT .

具体实施方式detailed description

为使本发明的上述目的、特征和优点能够更加明显易懂，下面结合附图和具体实施方式对本发明作进一步详细的说明。In order to make the above objects, features and advantages of the present invention more comprehensible, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

实施例一：Embodiment one:

参照图1，示出了本发明实施例一种基于信号级融合的组网雷达抗欺骗式干扰方法的流程图，本实施例具体可以包括以下步骤：Referring to FIG. 1 , it shows a flow chart of a networked radar anti-spoofing jamming method based on signal level fusion according to an embodiment of the present invention. This embodiment may specifically include the following steps:

步骤1，计算匹配滤波后点目标的回波数据的复幅度，并根据所述复幅度构造每个点目标在所述多个节点雷达的慢时间随机复包络序列。Step 1, calculate the complex amplitude of the echo data of the point target after the matched filtering, and construct the slow-time random complex envelope sequence of each point target in the multiple node radars according to the complex amplitude.

本实施例中设所述组网雷达包括n个节点雷达，其中n≥2，设所述匹配滤波后点目标的回波数据中包括P个点目标。In this embodiment, it is assumed that the networked radar includes n node radars, where n≥2, and it is assumed that the echo data of the point target after the matched filtering includes P point targets.

本实施例中所述步骤1包括以下子步骤：Step 1 described in this embodiment includes the following sub-steps:

其中，x(t)为回波数据，为卷积符号，*表示共轭。Among them, x(t) is the echo data, is the convolution symbol, * means conjugate.

需要说明的是，本实施例采用的组网雷达中包含有n个节点雷达，n≥2，每个节点雷达接收回波信号，并对接收的回波信号进行匹配滤波1a)、相干积累1b)和恒虚警检测1c)。本实施例采用上述公式对所述回波数据进行匹配滤波，即采用回波数据与其共轭反转之后的函数卷积的方法进行匹配滤波。需要进一步说明的是，回波数据中包括真实目标、假目标、噪声(真假目标统称信号)，而匹配滤波能够提高信噪比，因此可以更加清晰凸显点目标的回波数据。It should be noted that the networked radar used in this embodiment includes n node radars, n≥2, each node radar receives echo signals, and performs matched filtering 1a) and coherent accumulation 1b on the received echo signals ) and constant false alarm detection 1c). In this embodiment, the above formula is used to perform matched filtering on the echo data, that is, a method of convolution of the echo data and a function after conjugate inversion is used to perform matched filtering. It needs to be further explained that the echo data includes real targets, false targets, and noise (real and false targets are collectively referred to as signals), and matched filtering can improve the signal-to-noise ratio, so the echo data of point targets can be highlighted more clearly.

其中，Q为脉冲积累个数，y(m)为匹配滤波后点目标的回波数据。Among them, Q is the number of accumulated pulses, and y(m) is the echo data of point targets after matched filtering.

需要说明的是，相干积累是指将同一节点雷达不同脉冲重复周期的数据调整相位再相加，目的是提高信噪比，相干积累后点目标的回波数据信噪比更大，本实施例采用上述公式对所述匹配滤波后点目标的回波数据进行相干积累，即采用对同一节点雷达不同脉冲重复周期的数据进行离散傅立叶变换的方式进行相干积累，离散傅立叶变换公式即为上述公式。It should be noted that coherent accumulation refers to the phase adjustment and addition of data of different pulse repetition periods of the same node radar. The purpose is to improve the signal-to-noise ratio. The above formula is used to coherently accumulate the echo data of the point target after the matched filter, that is, to carry out coherent accumulation by discrete Fourier transform on the data of different pulse repetition periods of the same node radar, and the discrete Fourier transform formula is the above formula.

1c)设所述相干积累后点目标的回波数据中包括P个点目标的回波数据，对所述相干积累后点目标的回波数据进行恒虚警检测，分别得到P个点目标的回波数据的复幅度。1c) Assuming that the echo data of the point targets after the coherent accumulation includes the echo data of P point targets, the constant false alarm detection is performed on the echo data of the point targets after the coherent accumulation, and the P point targets are obtained respectively. The complex amplitude of the echo data.

需要说明的是，恒虚警检测是指将回波数据与给定门限进行比较，目的是把真假目标回波数据从噪声背景中提取出来，恒虚警检测后可以得到点目标回波数据的复幅度本实施例上述步骤1a)、1b)、1c)对应步骤1中的“计算匹配滤波后回波数据的复幅度”。It should be noted that the constant false alarm detection refers to the comparison of the echo data with a given threshold, the purpose is to extract the true and false target echo data from the noise background, and the point target echo data can be obtained after the constant false alarm detection complex magnitude of The above steps 1a), 1b) and 1c) of this embodiment correspond to the "complex amplitude calculation of echo data after matched filtering" in step 1.

${X x}_{{p p}_{i i}}^{i i} = = {{{A A}_{{p p}_{i i}}^{i i},, i i = = 11,, 22,, 33 ... ... .. ..,, n no}},, {p p}_{i i} = = 11,, 22,, 33 ... ... . . P P$

需要说明的是，上述为矩阵，所述P个点目标的回波数据的复幅度组成集合中的每个元素都是矩阵。需要说明的是，上述步骤1d)对应步骤1中的根据所述复幅度构造每个点目标的慢时间随机复包络序列，即本实施例将所述匹配滤波后回波数据的复幅度的集合作为每个点目标的慢时间随机复包络序列。本实施例在一个相干处理周期中所有脉冲重复时间(Pulse Recurrence Time，PRT)重复上述1a)、1b)、1c)。It should be noted that the above is a matrix, and the complex amplitudes of the echo data of the P point targets form a set Each element in is a matrix. It should be noted that the above step 1d) corresponds to the construction of the slow-time random complex envelope sequence of each point target according to the complex amplitude in step 1, that is, the complex amplitude of the matched filtered echo data in this embodiment Ensemble as a sequence of slow-time randomized complex envelopes for each point target. In this embodiment, the above 1a), 1b) and 1c) are repeated for all pulse repetition times (Pulse Recurrence Time, PRT) in one coherent processing cycle.

步骤2，根据所述每个点目标在所述多个节点雷达的慢时间随机复包络序列，估计每个点目标在每个节点雷达的平均功率。Step 2: Estimating the average power of each point target in each node radar according to the slow-time random complex envelope sequence of each point target in the plurality of node radars.

本实施例中所述步骤2包括以下子步骤：Step 2 described in this embodiment includes the following sub-steps:

需要说明的是，本实施例2a)、2b)是以根据第i节点雷达检测到的第p_i个点目标的慢时间随机复包络序列计算所述点目标p_i在节点i的平均功率的估计值为例进行说明的，每个点目标平均功率的计算与之类似，本实施例不再赘述。并且步骤2估计得到的每个点目标在每个节点的平均功率在后面的步骤5b)中用到。It should be noted that, the present embodiment 2a), 2b) is based on the slow time random complex envelope sequence of the p _i point target detected by the i node radar Computes an estimate of the average power of the point target p _i at node i As an example for illustration, the calculation of the target average power of each point is similar, and will not be repeated in this embodiment. And the average power of each point target at each node estimated in step 2 is used in the following step 5b).

步骤3，对于同一点目标，将其在所述多个节点雷达的慢时间随机复包络序列两两组合，构成多个包络组，并估计每个包络组的相关系数。Step 3: For the same point target, combine the slow-time random complex envelope sequences of the multiple node radars in pairs to form multiple envelope groups, and estimate the correlation coefficient of each envelope group.

本实施例中所述步骤3包括以下子步骤：Step 3 described in this embodiment includes the following sub-steps:

其中，H表示矩阵的共轭转置。where H represents the conjugate transpose of the matrix.

本实施例可以遍历所有节点，使所有节点的所有点目标重复3b)，最终得到每个包络组的相关系数。In this embodiment, all nodes can be traversed, and 3b) can be repeated for all point targets of all nodes, and finally the correlation coefficient of each envelope group can be obtained.

步骤4，对于同一点目标，分别选取所述每个包络组的相关系数的实部作为每个包络组对应的相关性度量。Step 4, for the same point target, respectively select the real part of the correlation coefficient of each envelope group as the correlation measure corresponding to each envelope group.

需要说明的是，所述每个包络组的相关系数为复数，本实施例中选取相关系数的实部作为相关性度量。It should be noted that the correlation coefficient of each envelope group is a complex number, and the real part of the correlation coefficient is selected as the correlation measure in this embodiment.

本实施例中根据每个包络组的相关系数通过以下公式选取其实部作为每个包络组对应的相关性度量 According to the correlation coefficient of each envelope group in this embodiment The real part is selected as the correlation measure corresponding to each envelope group by the following formula

步骤5，给定组网雷达的真实目标误判概率，并依据所述组网雷达的真实目标误判概率计算所述每个包络组对应的相关性度量的检验门限。Step 5, given the real target misjudgment probability of the networked radar, and calculating the inspection threshold of the correlation measure corresponding to each envelope group according to the real target misjudged probability of the networked radar.

本实施例中所述步骤5包括以下子步骤：Step 5 described in this embodiment includes the following sub-steps:

步骤6，将所述相关性度量与所述检验门限进行比较，判断所述相关性度量是否大于所述检验门限，当所述相关性度量小于等于所述检验门限时，判定所述相关性度量对应的包络组通过假目标检验；当所述相关性度量大于所述检验门限时，判定所述相关性度量对应的包络组未通过假目标检验，并将该包络组对应的两个点目标标定为假目标。Step 6, comparing the correlation metric with the inspection threshold, judging whether the correlation metric is greater than the inspection threshold, when the correlation metric is less than or equal to the inspection threshold, determining the correlation metric The corresponding envelope group passes the false target inspection; when the correlation metric is greater than the inspection threshold, it is determined that the envelope group corresponding to the correlation metric fails the false target inspection, and the two corresponding envelope groups are Point targets are labeled as false targets.

需要说明的是，本实施例将所述相关性度量与所述检验门限进行比较，判断所述相关性度量是否大于所述检验门限的过程，即对所述相关性度量进行假设检验。It should be noted that, in this embodiment, the process of comparing the correlation metric with the verification threshold and judging whether the correlation metric is greater than the verification threshold is to perform a hypothesis test on the correlation metric.

本实施例中所述步骤6包括以下子步骤：Step 6 described in this embodiment includes the following sub-steps:

需要说明的是，若包络组通过假目标检验，则该包络组对应的两个点目标通过假目标检验；It should be noted that if the envelope group passes the false target test, then the two point targets corresponding to the envelope group pass the false target test;

步骤7，剔除所述假目标。Step 7, removing the false target.

本实施例中所述步骤7包括以下子步骤：Step 7 described in this embodiment includes the following sub-steps:

需要说明的是，根据上述步骤3中的描述，任意两个节点的点目标的慢时间随机复包络序列构成包络组，而根据步骤4中的描述，相关性度量是针对包络组来说的，因此上述步骤7a)先找出在6c)中被标定为假目标的两个点目标所在的包络组对应的相关性度量。根据1d)中的描述，每个点目标的慢时间随机复包络序列是点目标回波数据的复幅度的集合，而包络组选取任意两个节点的点目标的慢时间随机复包络序列构成的，包络组又与所述相关性度量，因此可以根据所述相关性度量找到对应的点目标回波数据的复幅度，然后将所述相关性度量对应的点目标回波数据的复幅度置为零。It should be noted that, according to the description in step 3 above, the slow-time random complex envelope sequences of the point objects of any two nodes constitute the envelope group, and according to the description in step 4, the correlation measure is for the envelope group That is to say, so the above step 7a) first finds the correlation measure corresponding to the envelope group where the two point targets marked as false targets in 6c) are located. According to the description in 1d), the slow-time stochastic complex envelope sequence of each point target is the complex amplitude of the point target echo data , and the envelope group is composed of the slow-time random complex envelope sequence of the point target of any two nodes, and the envelope group is related to the correlation measure, so the corresponding point target can be found according to the correlation measure The complex amplitude of the echo data, and then the complex amplitude of the point target echo data corresponding to the correlation measure is set to zero.

需要说明的是，将所述相关性度量对应的点目标回波数据的复幅度置为零，可以使慢时间随机复包络序列中只有真实目标的复包络序列，从而剔除所述假目标。本实施例在步骤6中找出假目标之后，在步骤7中直接删除所述假目标，从而实现抗欺骗式干扰的目的。It should be noted that setting the complex amplitude of the point target echo data corresponding to the correlation measure to zero can make the slow time random complex envelope sequence There is only the complex envelope sequence of the real target, so as to eliminate the false target. In this embodiment, after the false target is found in step 6, the false target is directly deleted in step 7, so as to achieve the purpose of anti-spoofing interference.

需要说明的是，本实施例对每个包络组都进行如下处理：It should be noted that, in this embodiment, each envelope group is processed as follows:

遍历检验第i节点和第j节点雷达中的每一个点目标组合的剔除其假目标。对第i节点雷达中的每一个点目标进行的p_j次检验，一次被列为有源假目标，则剔除该有源假目标，对第j节点雷达中的每一个点目标进行的p_i次检验，一次被列为有源假目标，则剔除有源假目标。即依次对任意两个节点雷达中的全部目标进行步骤2至步骤6的检验，剔除有源假目标，从而实现抗欺骗式干扰的目的。Traverse and check the combination of each point target in the i-th node and j-th node radar Eliminate its false targets. For each point target in the i-th node radar, p _j inspections are performed, and once it is listed as an active false target, then the active false target is eliminated, and p _i is performed on each point target in the j-th node radar If one is listed as an active false target, the active false target will be eliminated. That is, all targets in the radars of any two nodes are checked in turn from step 2 to step 6, and active false targets are eliminated, so as to achieve the purpose of anti-spoofing jamming.

实施例二：Embodiment two:

参照图2，示出了本发明实施例一种基于信号级融合的组网雷达抗欺骗式干扰方法的流程图，Referring to FIG. 2 , it shows a flow chart of a networked radar anti-spoofing jamming method based on signal level fusion according to an embodiment of the present invention.

本实施例具体可以包括以下步骤：This embodiment may specifically include the following steps:

步骤201，取第i个节点的每个目标的慢时间随机复包络序列。Step 201, get the slow-time random complex envelope sequence of each target of the i-th node.

需要说明的是，所述步骤201与实施例一中的步骤1对应，可以参见实施例一中步骤1的相关描述，本实施例在此不做赘述。It should be noted that the step 201 corresponds to the step 1 in the first embodiment, and reference may be made to the related description of the step 1 in the first embodiment, which will not be repeated here in this embodiment.

步骤202，取第j个节点的每个目标的慢时间随机复包络序列。Step 202, get the slow-time random complex envelope sequence of each target of the jth node.

需要说明的是，所述步骤202与实施例一中的步骤1对应，可以参见实施例一中步骤1的相关描述，本实施例在此不做赘述。It should be noted that the step 202 corresponds to the step 1 in the first embodiment, and reference may be made to the relevant description of the step 1 in the first embodiment, and details are not described here in this embodiment.

步骤203，估计平均功率；Step 203, estimating the average power;

需要说明的是，本实施例中步骤203根据步骤201中所取的第i个节点的每个目标的慢时间随机复包络序列，估计其对应的平均功率。步骤203与实施例一中的步骤2对应，估计平均功率的具体内容参见实施例一中步骤2的相关描述，本实施例在此不做赘述。It should be noted that, in step 203 of this embodiment, according to the slow-time random complex envelope sequence of each target of the i-th node obtained in step 201, the corresponding average power is estimated. Step 203 corresponds to step 2 in Embodiment 1. For specific content of estimating the average power, refer to the relevant description of Step 2 in Embodiment 1, which will not be repeated here in this embodiment.

步骤204，估计平均功率；Step 204, estimating the average power;

需要说明的是，本实施例中步骤204根据步骤202中所取的第j个节点的每个目标的慢时间随机复包络序列，估计其对应的平均功率。步骤204与实施例一中的步骤2对应，估计平均功率的具体内容参见实施例一中步骤2的相关描述，本实施例在此不做赘述。It should be noted that, in step 204 of this embodiment, according to the slow-time random complex envelope sequence of each target of the jth node obtained in step 202, the corresponding average power is estimated. Step 204 corresponds to step 2 in Embodiment 1. For specific content of estimating the average power, refer to the relevant description of Step 2 in Embodiment 1, and details are not described here in this embodiment.

步骤205，估计相关系数。Step 205, estimating the correlation coefficient.

需要说明的是，本实施例中步骤205是将步骤201所取的第i个节点的每个目标的慢时间随机复包络序列，和步骤202所取的第j个节点的每个目标的慢时间随机复包络序列构成包络组，然后估计该包络组的相关系数。步骤205与实施例一中的步骤3对应，估计相关系数的具体内容参见实施例一中步骤3的相关描述，本实施例在此不做赘述。It should be noted that, in step 205 of this embodiment, the slow-time random complex envelope sequence of each target of the i-th node taken in step 201 and the slow-time random complex envelope sequence of each target of the j-th node taken in step 202 The slow-time random complex envelope sequence constitutes an envelope group, and then the correlation coefficient of the envelope group is estimated. Step 205 corresponds to step 3 in the first embodiment, and for the specific content of estimating the correlation coefficient, refer to the related description of step 3 in the first embodiment, which will not be repeated here in this embodiment.

步骤206，取实部得到相关性度量。Step 206, get the real part to obtain the correlation measure.

需要说明的是，步骤206是对相关系数取实部得到相关性度量步骤206与实施例一中的步骤4对应，取实部的具体内容参见实施例一中步骤4的相关描述，本实施例在此不做赘述。It should be noted that step 206 is for the correlation coefficient Take the real part to get the correlation measure Step 206 corresponds to Step 4 in Embodiment 1. For the specific content of the real part, refer to the relevant description of Step 4 in Embodiment 1, and this embodiment will not repeat it here.

步骤207，计算检验门限。Step 207, calculating the inspection threshold.

需要说明的是，步骤207是计算检验门限步骤207与实施例一中的步骤5对应，计算检验门限的具体内容参见实施例一中步骤5的相关描述，本实施例在此不做赘述。It should be noted that step 207 is to calculate the inspection threshold Step 207 corresponds to step 5 in the first embodiment. For the specific content of calculating the verification threshold, refer to the relevant description of step 5 in the first embodiment, which will not be repeated here in this embodiment.

步骤208，判断相关性度量是否大于检验门限。Step 208, judging whether the correlation measure is greater than the inspection threshold.

需要说明的是，步骤208将相关性度量与检验门限进行比较，判断相关性度量是否大于检验门限当时，判定所述相关性度量对应的包络组未通过假目标检验，并将该包络组对应的两个点目标标定为有源假目标，即当时，执行步骤209。步骤208与实施例一中的步骤6对应，具体内容参见实施例一中步骤6的相关描述，本实施例在此不做赘述。It should be noted that, in step 208, the correlation measure and inspection threshold Make comparisons, judge correlation measures Is it greater than the inspection threshold when When , it is determined that the envelope group corresponding to the correlation measure has not passed the false target test, and the two point targets corresponding to the envelope group are marked as active false targets, that is, when , execute step 209. Step 208 corresponds to step 6 in Embodiment 1. For details, refer to the relevant description of Step 6 in Embodiment 1, which will not be repeated here in this embodiment.

步骤209，得出有源假目标位置。Step 209, obtain the position of the active false target.

需要说明的是，当时，判定相关性度量对应的包络组未通过假目标检验，并将该包络组对应的两个点目标标定为假目标，即得出了有源假目标的位置。步骤209与实施例一中的步骤6c)对应，具体内容参见实施例一中步骤6c)的相关描述，本实施例在此不做赘述。It should be noted that when When , it is determined that the envelope group corresponding to the correlation measure fails the false target test, and the two point targets corresponding to the envelope group are marked as false targets, that is, the position of the active false target is obtained. Step 209 corresponds to step 6c) in Embodiment 1. For details, refer to the relevant description of Step 6c) in Embodiment 1, and this embodiment will not repeat it here.

步骤210，剔除所述假目标。Step 210, removing the false target.

需要说明的是，步骤210与实施例一中的步骤7对应，具体内容参见实施例一中步骤7的相关描述，本实施例在此不做赘述。It should be noted that step 210 corresponds to step 7 in the first embodiment, and for specific content, refer to the relevant description of step 7 in the first embodiment, which will not be repeated here in this embodiment.

本发明对抗欺骗式干扰的能力可通过以下仿真进一步验证。The ability of the present invention to resist deceptive interference can be further verified by the following simulation.

(1)实验场景(1) Experimental scene

以两部节点雷达组成的组网雷达为例进行仿真实验，第一个节点雷达工作在发射状态，第二个节点雷达工作在接收状态，对同一空间区域进行探测。在共同探测区域中只有一个真实目标。Taking the networked radar composed of two node radars as an example, the simulation experiment is carried out. The first node radar works in the transmitting state, and the second node radar works in the receiving state to detect the same space area. There is only one real target in the common detection area.

假设两个节点雷达的接收信号进行脉冲压缩以后，得到真实目标的复包络序列的目标噪声比TNR相等，有源假目标的复包络序列的目标噪声比TNR相等。Assuming that the received signals of the two node radars undergo pulse compression, the target-to-noise ratio TNR of the complex envelope sequence of the real target is equal, and the target-to-noise ratio TNR of the complex envelope sequence of the active false target is equal.

(2)实验内容与结果分析(2) Experimental content and result analysis

实验一：本发明提出的有源假目标鉴别方法的鉴别性能与目标噪声比TNR、假目标个数M、真实目标误判概率P_l及不同积累脉冲个数Q有关。设P_l＝0.001，M＝2，TNR分别取为0dB、3dB、6dB、9dB，并对每一个TNR的取值，均在不同积累脉冲个数Q下，对本发明提出的有源假目标鉴别方法进行10万次Moto Carlo蒙特卡罗实验，统计得到假目标正确鉴别概率P_FT，如图3所示。其中，积累脉冲个数Q的变化范围为4～64。Experiment 1: The discriminative performance of the active false target identification method proposed by the present invention is related to the target-to-noise ratio TNR, the number of false targets M, the misjudgment probability of real targets _P1 and the number Q of different accumulated pulses. Suppose P _l =0.001, M=2, TNR is taken as 0dB, 3dB, 6dB, 9dB respectively, and to the value of each TNR, all under different accumulation pulse number Q, to the active false target identification that the present invention proposes Methods 100,000 times of Moto Carlo Monte Carlo experiments were performed, and the correct identification probability P _FT of false targets was obtained statistically, as shown in Figure 3. Wherein, the variation range of the accumulated pulse number Q is 4-64.

从图3中可以看到，随着积累脉冲个数不断增加，P_FT不断增加，这是由于积累脉冲数越多，相关性度量对相关系数的估计结果更有效，对假目标的鉴别性能越好；TNR越大，P_FT越高，这是由于TNR越大，和对应同一欺骗式干扰信号产生的假目标条件下的理论相关系数越大，检验统计量的差异增大，则鉴别性能越好。积累脉冲个数Q在40～60之间判别效果较好。It can be seen from Figure 3 that as the number of accumulated pulses increases, the _PFT increases continuously. This is because the more the number of accumulated pulses, the more effective the correlation measure is for estimating the correlation coefficient, and the better the identification performance for false targets is. Good; the larger the _TNR , the higher the PFT, because the larger the TNR, and The greater the theoretical correlation coefficient under the false target condition corresponding to the same deceptive interference signal, the greater the difference of test statistics, and the better the discrimination performance. When the number of accumulated pulses Q is between 40 and 60, the discrimination effect is better.

实验二：设TNR＝0dB，P_l＝0.001，M分别取为2、4、8、14，并对每一个M的取值，均在不同积累脉冲个数Q下，对本发明提出的有源假目标鉴别方法进行10万次Moto Carlo蒙特卡罗实验，统计得到假目标正确鉴别概率P_FT，如图4所示。其中，积累脉冲个数Q的变化范围为4～64。Experiment 2: Set TNR=0dB, P _l =0.001, M is taken as 2, 4, 8, 14 respectively, and the value of each M is all under different accumulation pulse numbers Q, to the active active that the present invention proposes False target identification method conducts 100,000 Moto Carlo Monte Carlo experiments, and obtains the correct identification probability P _FT of false targets, as shown in Figure 4 . Wherein, the variation range of the accumulated pulse number Q is 4-64.

从图4中可以看到，随着积累脉冲个数不断增加，P_FT不断增加，这是由于积累脉冲数越多，相关性度量对相关系数的估计结果更有效，对假目标的鉴别性能越好；随着有源假目标个数不断增多，P_FT缓慢下降，在积累脉冲个数Q为在40～60之间判别效果较好。It can be seen from Figure 4 that as the number of accumulated pulses increases, the _PFT increases continuously. This is because the more the number of accumulated pulses, the more effective the correlation measurement is for the estimation of the correlation coefficient, and the better the identification performance for false targets is. Good; as the number of active false targets continues to increase, _PFT decreases slowly, and the discrimination effect is better when the number of accumulated pulses Q is between 40 and 60.

实验三：设TNR＝0dB，M＝2，P_l＝0.01，0.005，0.001，并对每一个P_l的取值，均在不同积累脉冲个数Q下，对本发明提出的有源假目标鉴别方法进行10万次Moto Carlo蒙特卡罗实验，统计得到假目标正确鉴别概率P_FT，如图5所示。其中，积累脉冲个数Q的变化范围为4～64。Experiment three: set TNR=0dB, M=2, P1= _{0.01,0.005,0.001} , and to the value of each _P1 , all under different accumulation pulse number Q, to the active false target identification that the present invention proposes Methods 100,000 Moto Carlo Monte Carlo experiments were carried out, and the correct identification probability P _FT of false targets was obtained statistically, as shown in Figure 5. Wherein, the variation range of the accumulated pulse number Q is 4-64.

从图5中可以看到，随着积累脉冲个数不断增加，P_FT不断增加，这是由于积累脉冲数越多，相关性度量对相关系数的估计结果更有效，对假目标的鉴别性能越好；随着真实目标误判概率P_l增加，P_FT缓慢上升，在积累脉冲个数Q为在40～60之间判别效果较好。It can be seen from Figure 5 that as the number of accumulated pulses increases, the _PFT increases continuously. This is because the more the number of accumulated pulses, the more effective the correlation measurement is for the estimation of the correlation coefficient, and the better the identification performance for false targets is. Good; as the real target misjudgment probability P _l increases, P _FT rises slowly, and the discrimination effect is better when the accumulated pulse number Q is between 40 and 60.

对于前述的各方法实施例，为了简单描述，故将其都表述为一系列的动作组合，但是本领域技术人员应该知悉，本发明并不受所描述的动作顺序的限制，因为依据本发明，某些步骤可以采用其他顺序或者同时进行。其次，本领域技术人员也应该知悉，说明书中所描述的实施例均属于优选实施例，所涉及的动作和模块并不一定是本发明所必须的。For the aforementioned method embodiments, for the sake of simple description, they are expressed as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, Certain steps may be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification belong to preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

本说明书中的各个实施例均采用递进的方式描述，每个实施例重点说明的都是与其他实施例的不同之处，各个实施例之间相同相似的部分互相参见即可。Each embodiment in this specification is described in a progressive manner, each embodiment focuses on the difference from other embodiments, and the same and similar parts of each embodiment can be referred to each other.

本发明可以在由计算机执行的计算机可执行指令的一般上下文中描述，例如程序模块。一般地，程序模块包括执行特定任务或实现特定抽象数据类型的例程、程序、对象、组件、数据结构等等。也可以在分布式计算环境中实践本发明，在这些分布式计算环境中，由通过通信网络而被连接的远程处理设备来执行任务。在分布式计算环境中，程序模块可以位于包括存储设备在内的本地和远程计算机存储介质中。The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including storage devices.

最后，还需要说明的是，在本文中，诸如第一和第二等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来，而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。而且，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、商品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、商品或者设备所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括所述要素的过程、方法、商品或者设备中还存在另外的相同要素。Finally, it should also be noted that in this text, relational terms such as first and second etc. are only used to distinguish one entity or operation from another, and do not necessarily require or imply that these entities or operations, any such actual relationship or order exists. Furthermore, the term "comprises", "comprises" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus comprising a set of elements includes not only those elements, but also includes elements not expressly listed. other elements of or also include elements inherent in such a process, method, commodity, or apparatus. Without further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article or apparatus comprising said element.

以上对本发明所提供的一种基于信号级融合的组网雷达抗欺骗式干扰方法，进行了详细介绍，本文中应用了具体个例对本发明的原理及实施方式进行了阐述，以上实施例的说明只是用于帮助理解本发明的方法及其核心思想；同时，对于本领域的一般技术人员，依据本发明的思想，在具体实施方式及应用范围上均会有改变之处，综上所述，本说明书内容不应理解为对本发明的限制。Above, a kind of networked radar anti-spoofing jamming method based on signal level fusion provided by the present invention has been introduced in detail. In this paper, specific examples have been used to illustrate the principle and implementation of the present invention. The description of the above embodiments It is only used to help understand the method of the present invention and its core idea; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation and scope of application. In summary, The contents of this specification should not be construed as limiting the present invention.

Claims

1., based on the anti-Deceiving interference method of radar network that signal level merges, described radar network comprises multiple node radar, it is characterized in that, said method comprising the steps of:

Step 1, the complex magnitude of the echo data of point target after calculating matched filtering, and construct the slow time random complex envelope sequence of each point target at described multiple node radar according to described complex magnitude;

Step 2, according to the slow time random complex envelope sequence of described each point target at described multiple node radar, estimates the average power of each point target at each node radar;

Step 3, for same point target, by its random complex envelope sequence combination of two of slow time at described multiple node radar, forms multiple envelope group, and estimates the related coefficient of each envelope group;

Step 4, for same point target, the real part choosing the related coefficient of described each envelope group is respectively as relativity measurement corresponding to each envelope group;

Step 5, the real goal probability of miscarriage of justice of given radar network, and the inspection thresholding calculating relativity measurement corresponding to described each envelope group according to the real goal probability of miscarriage of justice of described radar network;

Step 6, described relativity measurement and described inspection thresholding are compared, judge whether described relativity measurement is greater than described inspection thresholding, when described relativity measurement is less than or equal to described inspection thresholding, judge that the envelope group that described relativity measurement is corresponding is checked by decoy; When described relativity measurement is greater than described inspection thresholding, judge that the envelope group that described relativity measurement is corresponding is not checked by decoy, and two corresponding for this envelope group point targets are demarcated as decoy;

Step 7, rejects described decoy.

2. the anti-Deceiving interference method of radar network merged based on signal level according to claim 1, it is characterized in that, described step 1 comprises following sub-step:

1a) establish described radar network to comprise n node radar, wherein n >=2, each node radar receives echo data, and adopts following formula to carry out matched filtering to described echo data, obtains echo data y (t) of point target after matched filtering:

y (t) = x (t) &CircleTimes; x * (- t)

Wherein, x (t) is echo data, for convolution symbol, * represents conjugation;

1b) adopt following formula to carry out coherent accumulation to the echo data of point target after described matched filtering, obtain echo data Y (k) of point target after coherent accumulation:

Y (k) = Σ_{m = 0}^{Q - 1} y (m) e^{- j \frac{2 π}{Q} k m}

Wherein, Q is pulse accumulation number, and y (m) is the echo data of point target after matched filtering;

After 1c) establishing described coherent accumulation, the echo data of point target comprises the echo data of P point target, carries out CFAR detection, obtain the complex magnitude of the echo data of P point target respectively to the echo data of point target after described coherent accumulation;

1d) by the set of the complex magnitude of the echo data of described P point target composition, and using the slow time random complex envelope sequence of described set as P point target

X_{p_{i}}^{i} = {A_{p i}^{i}, i = 1, 2, 3....., n}, p_{i} = 1, 2, 3.... P

Wherein, n represents the node radar number in described radar network, and n>=2; represent the p that i-th node detections of radar in described n node radar arrives _ithe complex magnitude of the echo data of individual point target; be a matrix, line number is all umber of pulses in each Coherent processing cycle, and columns is the number P of point target.

3. the anti-Deceiving interference method of radar network merged based on signal level according to claim 1, it is characterized in that, described step 2 comprises following sub-step:

Described radar network 2a) is established to comprise n node radar, wherein n>=2, if the echo data of point target comprises P point target after described matched filtering, from described n node radar, choose i-th node radar, and choose the p that described i-th node detections of radar arrive _ithe complex magnitude of the echo data of individual point target

2b) according to the p that described i-th node detections of radar arrives _ithe complex magnitude of the echo data of individual point target by point target p described in following formulae discovery _iin the estimated value of the average power of i-th node radar

ζ_{p_{i}, i}^{2} = \frac{{A_{p_{i}}^{i}}^{H} A_{p_{i}}^{i}}{Q}, i = 1, 2, 3, ..., n

Wherein, Q is the conjugate transpose of the number of PRT in the Coherent processing cycle, H representing matrix.

4. the anti-Deceiving interference method of radar network merged based on signal level according to claim 1, it is characterized in that, described step 3 comprises following sub-step:

Described radar network 3a) is established to comprise n node radar, wherein n>=2, if the echo data of point target comprises P point target after described matched filtering; From described n node radar, choose i-th node radar and a jth node radar, from a described P point target, choose p point target, for described i-th node radar, described p point target is p _iindividual point target; For a described jth node radar, described p point target is p _jindividual point target, by the p of described i-th node detections of radar _ithe random complex envelope sequence of slow time of individual point target with the p of a jth node detections of radar _jthe random complex envelope sequence of slow time of individual point target combine, form envelope group;

3b) by described in following formulae discovery with the related coefficient of the envelope group formed

i≠j，i＝1，2，3…n，j＝1，2，3…n

Wherein, the conjugate transpose of H representing matrix;

3c) repeat 3b) to the related coefficient obtaining each envelope group.

5. the anti-Deceiving interference method of radar network merged based on signal level according to claim 1, it is characterized in that, described step 4 comprises following sub-step:

According to the related coefficient of each envelope group its real part is chosen as relativity measurement corresponding to each envelope group by following formula

μ_{p_{i}, p_{j}} = r e a l ({\hat{ρ}}_{p_{i}, p_{j}})

Wherein, real () represents right get real part.

6. the anti-Deceiving interference method of radar network merged based on signal level according to claim 1, it is characterized in that, described step 5 comprises following sub-step:

5a) the real goal probability of miscarriage of justice P of given radar network _l;

5b) according to the real goal probability of miscarriage of justice P of described radar network _l, by the inspection thresholding of calculation of correlation corresponding to following formulae discovery each envelope group

ξ_{p_{i}, p_{j}} = \sqrt{{Qζ}_{p_{i}, i}^{2} ζ_{p_{j}, j}^{2} / 2} \cdot Φ^{- 1} (1 - {(1 - P_{l})}^{1 / P})

Wherein, Φ () represents standardized normal distribution, and Q is the number of PRT in the Coherent processing cycle, and P detects the target number obtained, represent point target p _iin the average power of i-th node radar and a jth node radar.

7. the anti-Deceiving interference method of radar network merged based on signal level according to claim 1, it is characterized in that, described step 6 comprises following sub-step:

6a) by described relativity measurement with described inspection thresholding compare;

6b) when time, judge that the envelope group that described relativity measurement is corresponding is checked by decoy;

6c) when time, judge that the envelope group that described relativity measurement is corresponding is not checked by decoy, and two corresponding for this envelope group point targets are demarcated as decoy.

8. the anti-Deceiving interference method of radar network merged based on signal level according to claim 1, it is characterized in that, described step 7 comprises following sub-step:

7a) search the relativity measurement that the envelope group at two the point target places being demarcated as decoy is corresponding;

7b) complex magnitude of point target echo data corresponding for described relativity measurement is set to zero.