CN119030636B - Modulated signal detection method of signal receiver - Google Patents
Modulated signal detection method of signal receiver Download PDFInfo
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Abstract
本发明提供了一种信号接收机的调制信号检测方法，包括以下步骤：信号检测模块依次对第一时频谱数据进行平滑滤波后得到第二时频二维谱数据；然后对第二时频二维谱数据进行估计噪底，得到时频二维谱数据均值；根据时频二维谱数据均值，计算获得判决门限值，对第二时频二维谱数据进行信号判决，得到用于描述信号存在与否的判决矩阵；对判决矩阵在时间帧上的平均值进行二值化处理，得到判决向量F（k）；通过判决向量F（k）中的二值元素的元素值判断是否存在目标信号；根据判决向量F（k）中的二值元素的位置，计算得到目标信号参数。本发明解决了目标信号不容易直接检测到、目标信号的中心频率和带宽的估计准确度降低的技术问题。
The present invention provides a modulation signal detection method for a signal receiver, comprising the following steps: a signal detection module performs smooth filtering on the first time-frequency spectrum data in sequence to obtain second time-frequency two-dimensional spectrum data; then the noise floor of the second time-frequency two-dimensional spectrum data is estimated to obtain the mean of the time-frequency two-dimensional spectrum data; according to the mean of the time-frequency two-dimensional spectrum data, a decision threshold is calculated to make a signal decision on the second time-frequency two-dimensional spectrum data to obtain a decision matrix for describing whether a signal exists; the average value of the decision matrix on the time frame is binarized to obtain a decision vector F(k); the existence of a target signal is judged by the element value of the binary element in the decision vector F(k); and the target signal parameters are calculated according to the position of the binary element in the decision vector F(k). The present invention solves the technical problem that the target signal is not easy to detect directly and the estimation accuracy of the center frequency and bandwidth of the target signal is reduced.
Description
Technical Field
The invention relates to the technical field of communication signal detection, in particular to a modulation signal detection method of a signal receiver.
Background
The modulation signals include analog modulation signals and digital modulation signals, wherein the digital modulation signals include QAM signals, PSK signals, FSK signals, and the like. In the technical field of signal receivers, because the generation and transmission of communication signals are interfered by other signals and communication channels and other unstable factors exist, the waveform of the received signals is unstable, the modulated signals are detected first, whether target signals exist in the received data is judged, then the modulated signals are identified, the modulation type of the signals is judged, and the subsequent signal processing work such as demodulation and analysis is convenient.
At present, in the detection process of modulated signals, the problems that (1) in a complex electromagnetic environment, signals in a real environment are usually submerged by noise and other interference signals, the target signals are not easy to directly detect, the communication quality and the system performance of a receiver are possibly affected, and the receiver cannot effectively analyze and process the signals, so that whether the target signals exist or not is accurately judged, the technical problem to be solved is solved, and (2) because the frequency spectrum environment is complex and changeable, the dynamic change of the frequency and the bandwidth of the signals is large, the estimation accuracy of the center frequency and the bandwidth of the target signals is reduced, the frequency spectrum resource allocation is unreasonable, the frequency spectrum interference is increased, the system efficiency is reduced, and therefore, the problem to be solved is another technical problem at present, namely, the problem to directly set a fixed detection threshold is possibly caused, the false detection or the false detection is possibly caused, meanwhile, a large number of false alarms or the false alarms are possibly caused, the reliability and the accuracy of the signal detection are affected, and the third problem to be solved that the detection threshold is set by accurately estimating the noise level (noise floor).
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a modulation signal detection method of a signal receiver, which can detect modulation signals such as QAM signals, PSK signals, FSK signals and the like, and solves the technical problems that a target signal is not easy to directly detect and the estimation accuracy of the center frequency and bandwidth of the target signal is reduced.
The technical scheme of the invention is as follows:
A modulation signal detection method of a signal receiver comprises a signal conditioning module, an FPGA chip in communication connection with the signal conditioning module and a processor in communication connection with the FPGA chip.
The signal conditioning module is used for filtering, gain adjustment, signal correction and amplification of the received short wave signals and outputting intermediate frequency analog signals to the analog-to-digital conversion module, the FPGA chip is used for receiving intermediate frequency digital signals and converting the intermediate frequency digital signals into time-frequency two-dimensional spectrum data and outputting the time-frequency two-dimensional spectrum data to the processor, the FPGA chip comprises the analog-to-digital conversion module used for converting the intermediate frequency analog signals into intermediate frequency digital signals, the DC removing module used for removing DC components in the intermediate frequency digital signals and the time-frequency conversion module used for converting the intermediate frequency digital signals into time-frequency two-dimensional spectrum data, the processor is used for detecting the received time-frequency two-dimensional spectrum data and comprises the data acquisition module, and the modulation signal detection method comprises the following steps:
after the data acquisition module receives the intermediate frequency digital signal, the direct current removing module removes direct current components in the intermediate frequency digital signal, and the time-frequency conversion module converts the intermediate frequency digital signal into first time-frequency two-dimensional spectrum data and outputs the first time-frequency two-dimensional spectrum data to the processor;
Obtaining first time-frequency two-dimensional spectrum data, sequentially carrying out smooth filtering on the first time-frequency two-dimensional spectrum data to obtain second time-frequency two-dimensional spectrum data;
According to the judgment threshold value, carrying out signal judgment on the second time-frequency two-dimensional spectrum data to obtain a judgment matrix for describing whether the signal exists or not;
The average value of the decision matrix on a time frame is subjected to binarization processing to obtain a decision vector F (k), wherein the decision vector F (k) consists of multi-dimensional column vectors, each dimensional column vector comprises a plurality of binary elements for describing whether a target signal exists or not, and the dimension of the column vector is the frequency frame number of the first time-frequency two-dimensional spectrum data;
Judging whether a target signal exists or not through element values of binary elements in a judgment vector F (k), judging that the target signal is not detected in the first time-frequency two-dimensional spectrum data if the element values of all the binary elements in the judgment vector F (k) are 0, and judging that the target signal is detected in the first time-frequency two-dimensional spectrum data if one or more binary elements exist in the judgment vector F (k) and the element value is 1, wherein the target signal is one or more of an analog modulation signal, a digital modulation signal or a single carrier signal;
and calculating a target signal parameter according to the positions of the binary elements in the decision vector F (k), wherein the target signal parameter comprises a signal center frequency, a signal bandwidth estimated value and the number of signals.
Further, obtaining the first time-frequency two-dimensional spectrum data, and performing smooth filtering on the first time-frequency two-dimensional spectrum data to obtain second time-frequency two-dimensional spectrum data, which specifically comprises the following steps:
Any one time spectrum value in the first time-frequency two-dimensional spectrum data and a time frame and a frequency frame corresponding to the time spectrum value are acquired, the time spectrum value is taken as a current time spectrum value, and the time frame corresponding to the time spectrum value is taken as a current time frame;
Accumulating a plurality of time frames before the current time frame and a plurality of time frames after the current time frame in a plurality of time spectrum values corresponding to the current frequency frame respectively, and dividing the accumulated time spectrum values by the length of a filter to obtain a smooth filtering value of the current time spectrum value;
And processing each time spectrum value in the first time-frequency two-dimensional spectrum data according to the method to obtain smooth filtering values of all the time spectrum values in the first time-frequency two-dimensional spectrum data, wherein the plurality of smooth filtering values form second time-frequency two-dimensional spectrum data.
Further, the noise floor is estimated on the second time-frequency two-dimensional spectrum data to obtain a time-frequency two-dimensional spectrum data average value, which specifically comprises the following steps:
acquiring all time frames in the second time-frequency two-dimensional spectrum data, and sequentially accumulating the time spectrum values corresponding to each time frame to obtain a first time spectrum value;
Acquiring all frequency frames in the second time-frequency two-dimensional spectrum data, and sequentially accumulating time-frequency spectrum values corresponding to each frequency frame to obtain a second time-frequency spectrum value;
and adding the first time spectrum value and the second time spectrum value, dividing the added time spectrum value by the total number of frames to obtain a time-frequency two-dimensional spectrum data average value, wherein the total number of frames is the product of the total number of time frames and the total number of frequency frames.
Further, according to the decision threshold value, signal decision is carried out on the second time-frequency two-dimensional spectrum data to obtain a decision matrix for describing whether the signal exists or not, which comprises the following steps:
Acquiring a time-frequency two-dimensional spectrum data average value, and multiplying the time-frequency two-dimensional spectrum data average value by a super-parameter preset value to obtain a decision threshold value;
Acquiring a time spectrum value in the second time-frequency two-dimensional spectrum data;
Judging the time spectrum value in the second time-frequency two-dimensional spectrum data to be 0 when the time spectrum value is smaller than or equal to a judging threshold value, and judging the time spectrum value in the second time-frequency two-dimensional spectrum data to be 1 when the time spectrum value is larger than the judging threshold value;
And judging all time spectrum values in the second time-frequency two-dimensional spectrum data according to the method to obtain a judgment matrix, wherein the judgment matrix is a binary matrix which is formed by two dimensions of time and frequency and has matrix element values of 0 or 1.
Further, binarizing the average value of the decision matrix on the time frame to obtain a decision vector F (k), which specifically includes:
Acquiring all time frames in a decision matrix and matrix element values corresponding to each time frame, sequentially accumulating the matrix element values corresponding to each time frame, and dividing the accumulated matrix element values by the total number of the time frames to obtain a first matrix element average value;
Judging the matrix element value in the judgment matrix as 0 when the first matrix element average value is smaller than or equal to the super-parameter preset value, and judging the matrix element value in the judgment matrix as 1 when the first matrix element average value is larger than the super-parameter preset value;
And judging all matrix element values in the judgment matrix according to the method to obtain a judgment vector F (k), wherein the judgment vector F (k) is an N-dimensional column vector with element values of 0 or 1.
Further, if one or more binary elements exist in the decision vector F (k) and the element value is 1, determining that the target signal is detected in the first time-frequency two-dimensional spectrum data includes:
If one binary element exists in the decision vector F (k) and the element value is 1, and the other binary elements are 0, judging that 1 target signal is detected;
If a plurality of binary elements with element values of 1 exist in the decision vector F (k) and the binary elements form one or more element segments, it is determined that one or more target signals are detected, wherein each element segment comprises a plurality of binary elements which are arranged continuously and each element segment comprises target signals of the same type.
Further, according to the position of the binary element in the decision vector F (k), the target signal parameter is calculated, which specifically includes:
Acquiring a frequency frame sequence number k of a binary element with an element value of 1 in a decision vector F (k);
Dividing the frequency frame number k by 1, and multiplying the frequency frame number k by half of the signal sampling rate to obtain an estimated value of the center frequency of the target signal, wherein the signal sampling rate is the signal sampling rate when the signal is sampled after the channelizing processing;
Dividing the signal sampling rate by twice the total number of frequency frames to obtain an estimated value of the bandwidth of the target signal.
The method for detecting the modulated signal of the signal receiver realizes the accurate detection of the target signal by carrying out signal judgment on the second time-frequency two-dimensional spectrum data, carrying out binarization processing on the average value of the judgment matrix on a time frame and judging the element value of the binary element in the judgment vector, not only can estimate the number, the center frequency and the bandwidth of the signal, but also can improve the spectrum utilization efficiency, optimize the performance of a communication system, enhance the anti-interference capability, support the dynamic spectrum management, assist the signal analysis and classification, solve the key problems of signal existence detection, signal frequency and bandwidth estimation, noise reference estimation and the like, and further improve the performance and the reliability of the whole communication system.
Compared with the prior art, the invention has the following beneficial effects:
1. According to the invention, through the signal conditioning module, the FPGA chip in communication connection with the signal conditioning module and the processor in communication connection with the FPGA chip, the purposes of converting externally received short wave signals into intermediate frequency analog signals and converting intermediate frequency digital signals into first time-frequency two-dimensional spectrum data and finally carrying out signal detection on the first time-frequency two-dimensional spectrum data are realized.
2. The method comprises the steps of obtaining a judgment matrix by carrying out signal judgment on second time-frequency two-dimensional spectrum data, obtaining a judgment vector by carrying out binarization processing on an average value of the judgment matrix on a time frame, judging whether a target signal exists or not by element values of binary elements in the judgment vector, and calculating to obtain target signal parameters according to positions of the binary elements in the judgment vector, thereby achieving estimation of parameters such as signal center frequency, signal bandwidth estimated value, signal quantity and the like, effectively improving accuracy of signal parameter estimation, solving the technical problems that in the prior art, the frequency spectrum environment is complex and changeable, the dynamic change of the signal frequency and the bandwidth is large, the estimation accuracy of the center frequency and the bandwidth of the target signal is reduced, the frequency spectrum resource allocation is unreasonable, the frequency spectrum interference is increased, and the system efficiency is reduced.
3. The invention obtains the time-frequency two-dimensional spectrum data average value by dividing the added first time-frequency spectrum value and the added second time-frequency spectrum value by the total number of frames, and then calculates and obtains the judgment threshold value according to the time-frequency two-dimensional spectrum data average value, thereby realizing accurate estimation of the noise level (noise floor) to set the detection threshold, and solving the technical problems that the direct setting of the fixed detection threshold possibly causes false detection or missed detection due to the uncertainty of the environmental noise level in the prior art, and simultaneously possibly causes a large number of false alarms or missed alarms to influence the reliability and the accuracy of signal detection.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:
Fig. 1 is a schematic diagram of a signal receiver according to an embodiment of the present invention;
fig. 2 is a flowchart of a signal detection method of a signal receiver according to an embodiment of the present invention.
Detailed Description
Embodiments of the invention are described in detail below with reference to the attached drawings, but the invention can be implemented in a number of different ways, which are defined and covered by the claims.
As shown in figure 1, the method is mainly applied to detection of target signals such as QAM signals, PSK signals and FSK signals, wherein the modulation signals comprise analog modulation signals, digital modulation signals or single carrier signals, and the signal receiver comprises a signal conditioning module, an FPGA chip in communication connection with the signal conditioning module and a processor in communication connection with the FPGA chip.
The FPGA chip is used for receiving the intermediate frequency digital signal, converting the intermediate frequency digital signal into time-frequency two-dimensional spectrum data and outputting the time-frequency two-dimensional spectrum data to the processor, and comprises an analog-to-digital conversion module for converting the intermediate frequency analog signal into the intermediate frequency digital signal, a DC removing module for removing DC components in the intermediate frequency digital signal and a time-frequency conversion module for converting the intermediate frequency digital signal into the time-frequency two-dimensional spectrum data, and the processor is used for detecting the received time-frequency two-dimensional spectrum data.
In this embodiment, when signal detection is performed, the target signal to be detected is an analog modulation signal, a digital modulation signal and a single carrier signal, wherein the analog modulation signal transmits information by modulating the amplitude, frequency or phase of a carrier wave, and features of the modulation signal around the carrier frequency appear on the frequency spectrum when signal detection is performed, the digital modulation signal transmits information by modulating discrete changes of the carrier wave, and generally includes a QAM signal, a PSK signal, an FSK signal and the like, and when signal detection is performed, the frequency spectrum appears as discrete frequency components, and may have significant signals at a plurality of frequency points, and the single carrier signal is concentrated at a specific frequency, and when signal detection is performed, the frequency spectrum appears as one or more concentrated frequency components.
As shown in fig. 2, in this embodiment, the signal detection method includes the following steps:
s01, after the data acquisition module receives the intermediate frequency digital signal, the direct current removing module removes direct current components in the intermediate frequency digital signal, and the time-frequency conversion module converts the intermediate frequency digital signal into first time-frequency two-dimensional spectrum data and outputs the first time-frequency two-dimensional spectrum data to the processor.
S02, acquiring first time-frequency two-dimensional spectrum data, sequentially carrying out smooth filtering on the first time-frequency two-dimensional spectrum data to obtain second time-frequency two-dimensional spectrum data, and then estimating noise floor on the second time-frequency two-dimensional spectrum data to obtain a time-frequency two-dimensional spectrum data average value.
In this step, the first time-frequency two-dimensional spectrum data and the second time-frequency two-dimensional spectrum data are each a set composed of a plurality of time-frequency spectrum values, each corresponding to a time frame and a frequency frame. The first time-frequency two-dimensional spectrum data is a function of time and frequency, can reflect the characteristics of the signal changing along with the time and the frequency, and has the expression of S x (i, k), i is a time frame sequence number, i=1, 2,3. The acquired first time-frequency two-dimensional spectrum data is generally a frame of first time-frequency two-dimensional spectrum data.
Preferably, in step S02, first time-frequency two-dimensional spectrum data is obtained, and the second time-frequency two-dimensional spectrum data is obtained after the first time-frequency two-dimensional spectrum data is smoothed and filtered, which specifically includes:
S201, any one time spectrum value and a time frame and a frequency frame corresponding to the time spectrum value in the first time-frequency two-dimensional spectrum data are obtained, the any one time spectrum value is used as a current time spectrum value, and the time frame corresponding to the time spectrum value is used as the current time frame.
S202, accumulating a plurality of time frames before the current time frame and a plurality of time frames after the current time frame at a current frequency frame respectively corresponding to a plurality of time spectrum values, dividing the accumulated time frames by the filter length to obtain a smooth filtering value of the current time spectrum value, wherein the plurality of time frames are (L-1)/2 time frames, and L is the length of the smooth filter, namely the frequency frame number considered when the smoothing processing is carried out in the frequency dimension.
S203, processing each time spectrum value in the first time-frequency two-dimensional spectrum data according to the method to obtain smooth filtering values of all the time spectrum values in the first time-frequency two-dimensional spectrum data, wherein the plurality of smooth filtering values form second time-frequency two-dimensional spectrum data.
The steps S201 to S203 are steps of smoothing the first time-frequency two-dimensional spectrum data in the present embodiment, and are aimed at reducing the influence of noise, so that the characteristics of the signal are more obvious, specifically, the influence of high-frequency noise is reduced by averaging the first time-frequency two-dimensional spectrum data in the frequency dimension, so as to highlight the main components of the signal. In this embodiment, the smoothing filtering is performed in the frequency dimension, and the expression of the smoothed second time-frequency two-dimensional spectrum data is:
in the formula (1), S x (j, k) is first time-frequency two-dimensional spectrum data representing time spectrum values at a time frame j and a frequency frame k, S y (i, k) is second time-frequency two-dimensional spectrum data, L is the length of a filter, j is a time frame sequence number range for smoothing filtering, and the minimum value thereof is Maximum value is
Preferably, in step S02, the noise floor is estimated for the second time-frequency two-dimensional spectrum data, so as to obtain a time-frequency two-dimensional spectrum data average value, which specifically includes:
s204, acquiring all time frames in the second time-frequency two-dimensional spectrum data, and sequentially accumulating the time spectrum values corresponding to each time frame to obtain a first time spectrum value.
S205, acquiring all frequency frames in the second time-frequency two-dimensional spectrum data, and sequentially accumulating the time-frequency spectrum values corresponding to each frequency frame to obtain a second time-frequency spectrum value.
S206, adding the first time spectrum value and the second time spectrum value, and dividing the added time spectrum value and the added second time spectrum value by the total number of frames to obtain a time-frequency two-dimensional spectrum data average value, wherein the total number of frames is the product of the total number of time frames and the total number of frequency frames.
The purpose of steps S204 to S206 is to estimate the noise floor for enhancing signal detection, recognition and enhancement. The noise floor (simply referred to as "noise floor") refers to the average level of background noise in the absence of significant signals. It is a benchmark for determining whether a signal is present and whether its intensity is large enough to exceed background noise. In the embodiment of the invention, the noise floor sigma can be estimated as follows:
In the formula (2), S y (j, k) is second time-frequency two-dimensional spectrum data, N is the total number of frequency frames, Q is the total number of time frames, i is the time frame number, and k is the frequency frame number.
S03, calculating to obtain a judgment threshold value according to the average value of the time-frequency two-dimensional spectrum data, and carrying out signal judgment on the second time-frequency two-dimensional spectrum data according to the judgment threshold value to obtain a judgment matrix for describing whether the signal exists or not.
Preferably, in step S03, a decision threshold value is obtained by calculation according to the average value of the time-frequency two-dimensional spectrum data, and signal decision is performed on the second time-frequency two-dimensional spectrum data according to the decision threshold value to obtain a decision matrix for describing whether the signal exists or not, which specifically comprises:
s301, acquiring a time-frequency two-dimensional spectrum data average value, and multiplying the time-frequency two-dimensional spectrum data average value by a super-parameter preset value to obtain a judgment threshold value.
S302, acquiring a time spectrum value in the second time-frequency two-dimensional spectrum data.
S303, judging the time spectrum value in the second time-frequency two-dimensional spectrum data to be 0 when the time spectrum value is smaller than or equal to a judging threshold value, and judging the time spectrum value in the second time-frequency two-dimensional spectrum data to be 1 when the time spectrum value is larger than the judging threshold value.
S304, judging all time spectrum values in the second time-frequency two-dimensional spectrum data according to the method to obtain a judgment matrix, wherein the judgment matrix is a binary matrix which is composed of two dimensions of time and frequency and has matrix element values of 0 or 1.
In the above step S301, the decision threshold th is calculated according to the following formula:
th=λσ1 (3)
In the formula (3), σ 1 is a time-frequency two-dimensional spectrum data mean value, λ is a super-parameter, a general default value is 3, and adjustment and optimization are performed according to actual data.
In the step S303, a decision matrix S z (i, k) is constructed by performing 0/1 decision on the smoothed second time-frequency two-dimensional spectrum data according to the decision threshold, so as to achieve the purpose of converting the smoothed second time-frequency two-dimensional spectrum data into a binary matrix. The decision matrix S z (i, k) is a binary matrix having the same dimensions as Sy (i, k), where 1 indicates the presence of a signal and 0 indicates the absence of a signal. The calculation process can be described as follows:
In the formula (4), S y (i, k) is second time-frequency two-dimensional spectrum data, th is a decision threshold value, i is a time frame number, and k is a frequency frame number.
S04, carrying out binarization processing on the average value of the decision matrix on a time frame to obtain a decision vector F (k), wherein the decision vector F (k) is composed of multi-dimensional column vectors, each dimensional column vector comprises a plurality of binary elements for describing whether a target signal exists or not, and the dimension of the column vector is the frequency frame number of the first time-frequency two-dimensional spectrum data.
Preferably, in step S04, binarizing the average value of the decision matrix over the time frame to obtain a decision vector F (k), which specifically includes:
s401, all time frames in a judgment matrix and matrix element values corresponding to each time frame are obtained, matrix element values corresponding to each time frame on the same frequency frame are accumulated in sequence, and then the sum of the time frames is divided, so that a first matrix element average value is obtained.
S402, judging the matrix element value in the judgment matrix to be 0 under the condition that the first matrix element average value is smaller than or equal to a preset threshold value, and judging the matrix element value in the judgment matrix to be 1 under the condition that the first matrix element average value is larger than a super-parameter preset value.
S403, judging all matrix element values in a judgment matrix according to the method to obtain a judgment vector F (k), wherein the judgment vector F (k) is an N-dimensional column vector with element values of 0 or 1.
In the above steps S401 to S403, the decision vector F (k) is an F-dimensional column vector, where F is the number of frequency frames, which is obtained by binarizing the average value of the decision matrix S z (i, k) over the time frame, each element F (k) is 0 or 1, indicating whether a signal is present in the kth frequency frame, and if the average value of the first matrix elements in all the time frames of the kth frequency frame exceeds or is equal to the preset threshold r, F (k) =1 indicates that a signal is present, otherwise F (k) =0 indicates that no signal is present. Specifically, the kth element F (k) of the decision vector is a binary value (0 or 1), and is obtained by comparing the column average value of S z (i, k) with a preset threshold r, and can be described as:
in equation (5), S z (i, k) is a decision matrix, which can be described as a matrix element value when a time frame is i and a frequency frame is k, Q is the total number of time frames, and r is a preset threshold.
S05, judging whether a target signal exists or not through element values of binary elements in the judgment vector F (k), if the element values of all the binary elements in the judgment vector F (k) are detected to be 0, judging that the target signal is not detected in the first time-frequency two-dimensional spectrum data, and if one or more binary elements exist in the judgment vector F (k) and the element value is 1, judging that the target signal is detected in the first time-frequency two-dimensional spectrum data.
Preferably, in step S05, if one or more binary elements exist in the decision vector F (k) and the element value is 1, it is determined that the target signal is detected in the first time-frequency two-dimensional spectrum data, including:
s501, if one binary element exists in the decision vector F (k) and the element value is 1, and the other binary elements are 0, determining that 1 target signal is detected.
S502, if a plurality of binary elements with element values of 1 exist in the decision vector F (k), and the binary elements form one or more element segments, it is determined that one or more target signals are detected, wherein each element segment comprises a plurality of binary elements which are arranged continuously, and each element segment comprises target signals of the same type.
S06, calculating to obtain target signal parameters according to the positions of the binary elements in the decision vector F (k), wherein the target signal parameters comprise signal center frequency, signal bandwidth estimated values and signal quantity.
Preferably, in the step S06, the target signal parameter is calculated according to the position of the binary element in the decision vector F (k), and specifically includes:
S601, acquiring a frequency frame sequence number k of a binary element with an element value of 1 in a decision vector F (k);
S602, dividing the frequency frame number k by 1, and multiplying the frequency frame number k by half of the signal sampling rate to obtain an estimated value of the center frequency of the target signal, wherein the signal sampling rate is the signal sampling rate when the signal is sampled after the channelizing processing.
S603, dividing the signal sampling rate by twice the total number of frequency frames to obtain an estimated value of the bandwidth of the target signal.
In the above steps S601 to S603, the estimated value of the center frequency of the target signal can be calculated and obtained by setting the frequency frame number of the binary element with the element value of 1 in the decision vector F (k) to be the jth element, that is, setting the subscript value of the binary element with the element value of 1 in the decision vector F (k) to be jEstimation of bandwidth of target signalWherein the estimated value of the center frequency of the target signalThe method comprises the following steps:
wherein the estimated value of the bandwidth of the target signal The method comprises the following steps:
In the formulas (6) and (7), f s is the sampling rate after the channelization process, and N is the total number of frequency frames.
The embodiment of the invention firstly converts the received intermediate frequency digital signal into first time-frequency two-dimensional spectrum data through a data acquisition module, then outputs the first time-frequency two-dimensional spectrum data to a signal detection module in a processor, then carries out smooth filtering and noise estimation on the first time-frequency two-dimensional spectrum data through the signal detection module to obtain a time-frequency two-dimensional spectrum data average value and a judgment threshold value, then carries out signal judgment on the second time-frequency two-dimensional spectrum data according to the judgment threshold value to obtain a judgment matrix for describing whether the signal exists or not, finally carries out binarization processing on the average value of the judgment matrix on a time frame to obtain a judgment vector, judges whether a target signal exists or not through element values of binary elements in the judgment vector, and calculates to obtain target signal parameters according to positions of the binary elements in the judgment vector, thereby achieving the estimation of parameters such as signal center frequency, signal bandwidth estimation value, signal quantity and the like, and effectively improving the accuracy of signal parameter estimation.
Meanwhile, the signal conditioning module is connected with the analog-to-digital conversion module in the FPGA chip, and the analog-to-digital conversion module is sequentially connected with the time-frequency conversion module and the signal detection module in a communication way, so that the signal conditioning module outputs an intermediate frequency analog signal after filtering, gain adjustment, signal correction and amplification of a received short wave signal, the FPGA chip converts the intermediate frequency digital signal of the intermediate frequency analog signal into first time-frequency two-dimensional spectrum data, the signal detection module carries out signal detection on the first time-frequency two-dimensional spectrum data, the effects of effectively detecting a target signal and accurately estimating the center frequency and the bandwidth of the target signal are achieved, the technical problems that the target signal is not easy to be directly detected under the condition that the signal is normally submerged by noise and other interference signals in the prior art, signal detection omission or false detection is caused are solved, and the technical problems that the frequency spectrum environment is complex and variable, the dynamic change of the frequency and the bandwidth is large, and the estimation accuracy of the center frequency and the bandwidth of the target signal is reduced are solved.
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