[go: up one dir, main page]

CN111060975B - Method for detecting ground penetrating radar target - Google Patents

Method for detecting ground penetrating radar target Download PDF

Info

Publication number
CN111060975B
CN111060975B CN201911214281.XA CN201911214281A CN111060975B CN 111060975 B CN111060975 B CN 111060975B CN 201911214281 A CN201911214281 A CN 201911214281A CN 111060975 B CN111060975 B CN 111060975B
Authority
CN
China
Prior art keywords
matrix
target
dimensional
echo
clutter
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201911214281.XA
Other languages
Chinese (zh)
Other versions
CN111060975A (en
Inventor
雷杰
孙盛远
瞿诗华
吴美武
唐云峰
张勇
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shanghai Institute of Microwave Technology CETC 50 Research Institute
Original Assignee
Shanghai Institute of Microwave Technology CETC 50 Research Institute
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shanghai Institute of Microwave Technology CETC 50 Research Institute filed Critical Shanghai Institute of Microwave Technology CETC 50 Research Institute
Priority to CN201911214281.XA priority Critical patent/CN111060975B/en
Publication of CN111060975A publication Critical patent/CN111060975A/en
Application granted granted Critical
Publication of CN111060975B publication Critical patent/CN111060975B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
    • G01V3/12—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation operating with electromagnetic waves
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/36—Means for anti-jamming, e.g. ECCM, i.e. electronic counter-counter measures
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section

Landscapes

  • Engineering & Computer Science (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Electromagnetism (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Environmental & Geological Engineering (AREA)
  • Geology (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • Geophysics (AREA)
  • Radar Systems Or Details Thereof (AREA)

Abstract

The invention provides a method for detecting a ground penetrating radar target, which comprises the steps of carrying out periodic sampling processing on a received target echo signal, carrying out principal component analysis on an obtained multidimensional observation matrix, carrying out matrix reconstruction after separating a strong direct wave and a strong ground clutter, and comparing the energy difference between a current target echo signal and a historical echo signal through linear prediction to judge the existence state of the target. By adopting a principal component analysis technology, the problem that the detection of target echo signals is influenced by strong direct waves and strong ground clutter faced by the ground penetrating radar is solved; the linear prediction technology is used to replace the traditional detection mode of directly using the target signal intensity to resist the clutter intensity, and the signal-to-noise-and-noise ratio of the target echo under the clutter is greatly improved.

Description

Method for detecting ground penetrating radar target
Technical Field
The invention relates to the technical field of ground penetrating radars, in particular to a method for detecting a target of a ground penetrating radar.
Background
The ground penetrating radar transmits specially designed electromagnetic wave signals to penetrate the ground to irradiate the targets buried underground, and detects various targets buried underground by detecting the electromagnetic wave signals reflected by the targets. However, since the attenuation of electromagnetic waves is much greater when the electromagnetic waves propagate in the soil than when the electromagnetic waves propagate in the air, the reflection signals of targets buried underground are much weaker than those of targets in the air at the same distance. In addition, the receiving and transmitting antennas of the ground penetrating radar are close to each other, and coupling with certain strength exists between the receiving and transmitting antennas, so that the received signals contain direct wave signals with certain strength. Moreover, the electromagnetic wave transmitted by the ground penetrating radar has stronger reflection at the discontinuous part of the air and the ground surface medium, so that the signal received by the ground penetrating radar contains stronger ground surface clutter. Further analysis, due to the uneven distribution characteristic of the soil medium, electromagnetic waves are reflected at discontinuous parts of the soil distribution, clutter is formed by the reflected signals which do not come from targets, and as target signals are always superposed on the clutter, the strength of a plurality of target signals is far from meeting the requirement of resisting the strong clutter, so that the development of a method for effectively detecting the underground targets becomes a very urgent problem.
In the existing ground penetrating radar system, a method of comparing target echoes with clutter to decide the existence of a target is generally adopted, and the following criteria are generally adopted: firstly, the power of a target echo point must be larger than a certain threshold, so that the signal power of the echo point is ensured to be larger than the clutter plus noise power; second, the power of clutter and noise signals around the target echo must be less than a certain threshold, which prevents strong interference signals near the target echo point from leaking into and causing time-varying interference to the target point.
The key to the adoption of the above criterion is that the power of the echo point of the target must be against the clutter plus noise signal power near the target. The traditional processing method has the problem of insufficient effect of strong clutter suppression, and the traditional ground penetrating radar has poor applicability to the detection method of the underground target because the target signal is always received along with the strong clutter and the signal power from the target cannot resist the power of the strong clutter signal in many cases.
The prior art related to the present application is patent document CN109709544A, and discloses a method for removing clutter of a ground penetrating radar, which includes the following steps: step S1: focusing the original data of the ground penetrating radar by using a Stolt offset method to obtain a ground penetrating radar image matrix M; step S2: decomposing the ground penetrating radar image M by using a robust principal component analysis method to obtain a low-rank matrix L and a sparse matrix S, namely a clutter L and a target S; step S3: and removing the clutter L, wherein the reserved target S is the ground penetrating radar image after the clutter is finally removed.
Disclosure of Invention
In view of the defects in the prior art, the invention aims to provide a method for detecting a ground penetrating radar target.
According to the method for detecting the target of the ground penetrating radar, provided by the invention, the received target echo signal is subjected to periodic sampling processing, the obtained multidimensional observation matrix is subjected to principal component analysis, strong direct waves and strong ground clutter are separated and then matrix reconstruction is carried out, and the energy difference between the current target echo signal and the historical echo signal is compared through linear prediction to judge the existence state of the target.
Preferably, the method comprises the following steps:
step 1: arranging N echo signals from a target into N-dimensional vectors according to a time ascending sequence, arranging the N-dimensional vectors which are scanned for P times with respect to the ground into a P multiplied by N-dimensional received data matrix X, and performing principal component analysis on the received data matrix to obtain principal components;
step 2: arranging singular values corresponding to the obtained principal components in a descending order, and separating linear subspaces corresponding to the principal components forming the strong direct waves and the strong ground clutter;
and step 3: reconstructing principal components corresponding to the separated residual linear subspaces into a reconstructed data matrix
Figure BDA0002299058110000021
And 4, step 4: selecting a reconstructed data matrix
Figure BDA0002299058110000022
From the ith, the continuous K groups of data vectors form a K matrix
Figure BDA0002299058110000023
Adjusting the weighting factor AiSo that the mean square error epsilon is obtained by the calculation of formula (1)iObtaining a minimum value:
Figure BDA0002299058110000024
and 5: sequentially adding the vector serial numbers i, and successively calculating the coefficient A of the corresponding continuous k frame reconstruction data matrix under each serial number in the received dataiAnd (3) sorting residual vectors of the k +1 frames under weighting according to the ascending order of the sequence number i into a residual matrix delta [ [ epsilon ] ]1,ε2,…εP-k](ii) a Forming test statistic xi from residual matrix deltai,jA decision is made as to the presence or absence of a target.
Preferably, the step 1 comprises:
step 1.1: at constant time intervals TSSampling the echo to obtain echo data corresponding to the two-way delay of the echo, arranging the data from small to large according to the delay, and sequentially from T to T0Starting, continuously selecting N point data to form a one-dimensional vector containing N elements;
step 1.2: at constant time intervals TmRepeating the method in the step 1.1, and sequentially collecting P groups of vectors to form a P multiplied by N dimension receiving data matrix X;
step 1.3: the SVD singular value decomposition is carried out on the P multiplied by N dimensional receiving data matrix X as follows:
Figure BDA0002299058110000031
wherein, the PxN dimensional matrix U ═ U1,u2,…,uN]N × N dimensional matrix V ═ V1,v2,…,vN]A matrix representing orthogonal bases; sigma ═ σ { (σ)i,iIs a diagonal matrix of singular values in the NxN dimension, the values being arranged in descending order, i.e.
Figure BDA0002299058110000032
Is the principal component of matrix X.
Preferably, the step 3 comprises:
step 3.1: forming a new principal component matrix by the M orthogonal bases after separating the direct wave and the strong earth clutter
Figure BDA0002299058110000033
Figure BDA0002299058110000034
Wherein
Figure BDA0002299058110000035
Is an M × N dimensional matrix;
step 3.2: from the new principal component matrix
Figure BDA0002299058110000036
Estimating a signal matrix
Figure BDA0002299058110000037
The following were used:
Figure BDA0002299058110000038
preferably, the step 4 comprises:
step 4.1: estimating the residual error according to the minimum mean square error criterion
Figure BDA0002299058110000039
Minimum weighting factor Ai＝[ai1,ai2,...,aik]T；
Step 4.2: calculating to obtain the linear prediction residual error of the ith estimation:
Figure BDA00022990581100000310
preferably, the step 5 comprises:
step 5.1: making continuous P-k times linear prediction for vector sequence number from i, and predicting residual error epsiloniArranging a matrix delta according to the ascending order of the serial number i;
step 5.2: on a two-dimensional plane formed by a two-dimensional matrix delta, an inspection statistic xi is formedi,j；
Step 5.3: according to test statistic xii,jA determination is made as to the presence or absence of a target.
Compared with the prior art, the invention has the following beneficial effects:
1. by adopting a principal component analysis technology, the problem that the detection of target echo signals is influenced by strong direct waves and strong ground clutter faced by the ground penetrating radar is solved;
2. the linear prediction technology is used to replace the traditional detection mode of directly using the target signal intensity to resist the clutter intensity, and the signal-to-noise-and-noise ratio of the target echo under the clutter is greatly improved.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a system framework diagram of the present invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
As shown in fig. 1, the method is mainly used in the field of ground penetrating radar target detection, and can greatly suppress the influence of strong direct waves and strong ground clutter on target echo signal detection, and simultaneously, the signal-to-noise ratio of the target echo signal is greatly improved by a linear prediction method. The core content of the invention mainly comprises a principal component analysis method and a linear prediction method. The method mainly comprises signal receiving and sampling, principal component analysis and linear prediction analysis.
The signal receiving and sampling mainly completes the acquisition processing of the periodic received signals. Specifically, the following steps are mainly adopted:
step 1: the received signal is periodically sampled to form an N point data vector, and the N point data vector is calculated to obtain the two-way delay
Figure BDA0002299058110000041
Corresponding data vector
Figure BDA0002299058110000042
Repeating observation for P times for a plurality of observation positions along the scanning direction in sequence to obtain a P multiplied by N dimensional observation matrix: x ═ Xi,xi+1,…xi+P}；
The principal component analysis mainly realizes effective suppression of direct waves and strong ground clutter signals received simultaneously with target echo signals, and specifically, the method can be divided into the following steps:
step 1: and arranging N-dimensional receiving vectors obtained by scanning the ground for P times in sequence into a P multiplied by N-dimensional receiving data matrix X, and performing principal component analysis on the matrix.
Step 2: arranging singular values corresponding to the obtained principal components in a descending order, and separating a linear subspace corresponding to the principal components forming the strong direct wave and the strong ground clutter;
and step 3: reconstructing principal components corresponding to the separated residual linear subspaces into a data matrix
Figure BDA00022990581100000512
In particular, at constant time intervals TSSampling the echo to obtain echo data corresponding to the two-way delay of the echo, arranging the data from small to large according to the delay, and sequentially from T to T0Starting, continuously selecting N point data to form a one-dimensional vector containing N elements;
at a constant timeInterval TmRepeatedly and sequentially collecting P groups of vectors to form a P multiplied by N dimension receiving data matrix X;
the SVD singular value decomposition is carried out on the P multiplied by N dimensional receiving data matrix X as follows:
Figure BDA0002299058110000051
wherein, the PxN dimensional matrix U ═ U1,u2,…,uN]N × N dimensional matrix V ═ V1,v2,…,vN]A matrix representing orthogonal bases; sigma ═ σ { (σ)i,iIs a diagonal matrix of singular values in dimension NxN, with values arranged in descending order, i.e. σi-1,i-1≥σi,i，
Figure BDA0002299058110000052
yi＝σi,ivi，
Figure BDA0002299058110000053
Is the principal component of matrix X.
Forming a new principal component matrix by the M orthogonal bases after separating the direct wave and the strong earth clutter
Figure BDA0002299058110000054
Figure BDA0002299058110000055
Wherein
Figure BDA0002299058110000056
Is an M × N dimensional matrix;
from the new principal component matrix
Figure BDA0002299058110000057
Estimating a signal matrix
Figure BDA0002299058110000058
The following were used:
Figure BDA0002299058110000059
the linear predictive analysis is mainly used for analyzing the energy difference between the observation of the current target echo and the observation of the historical echo, and comprises the following steps:
step 1: selecting matrix
Figure BDA00022990581100000510
The weighting coefficient A is adjusted in the continuous k groups of data vectors from the ithiMinimizing mean square error
Figure BDA00022990581100000511
Step 2: the sequence number i is added, residual quantities of the corresponding continuous k frames to the k +1 frames under each sequence number are calculated in sequence in the received data, and the residual quantities are sorted according to the ascending sequence of the sequence number i into a residual matrix delta [ [ epsilon ] ]1,ε2,…εP-k]；
And step 3: and forming a test statistic according to the CFAR criterion for the two-dimensional residual matrix delta obtained by estimation in the step, and judging whether the target exists or not according to the test statistic to finish the target detection for the ground penetrating radar.
In particular, the residual error is estimated according to a minimum mean square error criterion
Figure BDA0002299058110000061
Minimum weighting factor Ai＝[ai1,ai2,...,aik]T；
Calculating to obtain the linear prediction residual error of the ith estimation:
Figure BDA0002299058110000062
making continuous P-k times linear prediction for vector sequence number from i, and predicting residual error epsiloniArranging a matrix delta according to the ascending order of the serial number i;
on a two-dimensional plane formed by a two-dimensional matrix delta, an inspection statistic xi is formedi,j；
According to test statistic xii,jA determination is made as to the presence or absence of a target.
Those skilled in the art will appreciate that, in addition to implementing the systems, apparatus, and various modules thereof provided by the present invention in purely computer readable program code, the same procedures can be implemented entirely by logically programming method steps such that the systems, apparatus, and various modules thereof are provided in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system, the device and the modules thereof provided by the present invention can be considered as a hardware component, and the modules included in the system, the device and the modules thereof for implementing various programs can also be considered as structures in the hardware component; modules for performing various functions may also be considered to be both software programs for performing the methods and structures within hardware components.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.

Claims (5)

1. A method for detecting a ground penetrating radar target is characterized in that a received target echo signal is subjected to periodic sampling processing, an obtained multidimensional observation matrix is subjected to principal component analysis, strong direct waves and strong ground clutter are separated, then matrix reconstruction is carried out, energy difference between a current target echo signal and a historical echo signal is compared through linear prediction, and the existence state of the target is judged; the principal component analysis realizes effective suppression of direct waves and strong ground clutter signals received simultaneously with target echo signals; the linear prediction is used for analyzing the energy difference between the observation of the current target echo and the historical echo observation;
the method comprises the following steps:
step 1: arranging N echo signals from a target into N-dimensional vectors according to a time ascending sequence, arranging the N-dimensional vectors which are scanned for P times with respect to the ground into a P multiplied by N-dimensional received data matrix X, and performing principal component analysis on the received data matrix to obtain principal components;
step 2: arranging singular values corresponding to the obtained principal components in a descending order, and separating linear subspaces corresponding to the principal components forming the strong direct waves and the strong ground clutter;
and step 3: reconstructing principal components corresponding to the separated residual linear subspaces into a reconstructed data matrix
Figure FDA0003393801160000011
And 4, step 4: selecting a reconstructed data matrix
Figure FDA0003393801160000012
From the ith, the continuous K groups of data vectors form a K matrix
Figure FDA0003393801160000013
Adjusting the weighting factor AiSo that the mean square error epsilon is obtained by the calculation of formula (1)iObtaining a minimum value:
Figure FDA0003393801160000014
and 5: sequentially adding the vector serial numbers i, and successively calculating the coefficient A of the corresponding continuous k frame reconstruction data matrix under each serial number in the received dataiAnd (3) sorting the residual vectors of the k +1 frames under weighting according to the ascending order of the sequence number i into a residual matrix delta [ epsilon ]1,ε2,…εP-k](ii) a Forming a test statistic ξ from a residual matrix Δi,jA decision is made as to the presence or absence of a target.
2. The method for georadar target detection according to claim 1, wherein the step 1 comprises:
step 1.1: at constant time intervals TSSampling the echo to obtain echo data corresponding to the two-way delay of the echo, arranging the data from small to large according to the delay, and sequentially from T to T0Starting, continuously selecting N point data to form a one-dimensional vector containing N elements;
step 1.2: at constant time intervals TmRepeating the method in the step 1.1, and sequentially collecting P groups of vectors to form a P multiplied by N dimension receiving data matrix X;
step 1.3: the SVD singular value decomposition is carried out on the P multiplied by N dimensional receiving data matrix X as follows:
Figure FDA0003393801160000021
wherein, the PxN dimensional matrix U ═ U1,u2,…,uN]N × N dimensional matrix V ═ V1,v2,…,vN]A matrix representing orthogonal bases; sigma ═ σ { (σ)i,iIs a diagonal matrix of singular values in dimension NxN, with values arranged in descending order, i.e. σi-1,i-1≥σi,i，
Figure FDA0003393801160000022
yi＝σi,ivi，
Figure FDA0003393801160000023
Is the principal component of matrix X.
3. The method for georadar target detection according to claim 1, wherein said step 3 comprises:
step 3.1: forming a new principal component matrix by the M orthogonal bases after separating the direct wave and the strong earth clutter
Figure FDA0003393801160000024
Figure FDA0003393801160000025
Wherein
Figure FDA0003393801160000026
Figure FDA0003393801160000027
Is an M × N dimensional matrix;
step 3.2: from the new principal component matrix
Figure FDA0003393801160000028
Estimating a signal matrix
Figure FDA0003393801160000029
The following were used:
Figure FDA00033938011600000210
。
4. the method for georadar target detection according to claim 1, wherein said step 4 comprises:
step 4.1: estimating the residual error according to the minimum mean square error criterion
Figure FDA00033938011600000211
Minimum weighting factor Ai＝[ai1,ai2,...,aik]T；
Step 4.2: calculating to obtain the linear prediction residual error of the ith estimation:
Figure FDA00033938011600000212
。
5. the method for georadar target detection according to claim 1, wherein said step 5 comprises:
step 5.1: making continuous P-k times linear prediction for vector sequence number from i, and predicting residual error epsiloniArranging the matrix delta in ascending order of the serial number i;
step 5.2: forming a test statistic xi on a two-dimensional plane formed by a two-dimensional matrix deltai,j；
Step 5.3: according to test statistic xii,jA determination is made as to the presence or absence of a target.
CN201911214281.XA 2019-12-02 2019-12-02 Method for detecting ground penetrating radar target Active CN111060975B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201911214281.XA CN111060975B (en) 2019-12-02 2019-12-02 Method for detecting ground penetrating radar target

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201911214281.XA CN111060975B (en) 2019-12-02 2019-12-02 Method for detecting ground penetrating radar target

Publications (2)

Publication Number Publication Date
CN111060975A CN111060975A (en) 2020-04-24
CN111060975B true CN111060975B (en) 2022-03-18

Family

ID=70299237

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201911214281.XA Active CN111060975B (en) 2019-12-02 2019-12-02 Method for detecting ground penetrating radar target

Country Status (1)

Country Link
CN (1) CN111060975B (en)

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5483240A (en) * 1994-09-28 1996-01-09 Rockwell International Corporation Radar terrain bounce jamming detection using ground clutter tracking
CN103245966A (en) * 2013-05-24 2013-08-14 成都理工大学 Earthquake early warning method based on Internet of Things multi-sensor information fusion and neutral network technology
CN106355330A (en) * 2016-08-31 2017-01-25 郑州航空工业管理学院 Multi-response parameter optimization method based on radial basis function neural network prediction model
CN108630209A (en) * 2018-04-24 2018-10-09 中国科学院深海科学与工程研究所 A kind of marine organisms recognition methods of feature based fusion and depth confidence network

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5483240A (en) * 1994-09-28 1996-01-09 Rockwell International Corporation Radar terrain bounce jamming detection using ground clutter tracking
EP0704713A1 (en) * 1994-09-28 1996-04-03 Rockwell International Corporation Radar terrain bounce jamming detection using ground clutter tracking
CN103245966A (en) * 2013-05-24 2013-08-14 成都理工大学 Earthquake early warning method based on Internet of Things multi-sensor information fusion and neutral network technology
CN106355330A (en) * 2016-08-31 2017-01-25 郑州航空工业管理学院 Multi-response parameter optimization method based on radial basis function neural network prediction model
CN108630209A (en) * 2018-04-24 2018-10-09 中国科学院深海科学与工程研究所 A kind of marine organisms recognition methods of feature based fusion and depth confidence network

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
主元子空间自适应双边预测探地雷达杂波抑制;高翔等;《电波科学学报》;20100430;第25卷(第2期);第253-258、404页 *
基于小波变换与主成分分析的探地雷达自适应杂波抑制方法研究;覃尧等;《雷达学报》;20150831;第4卷(第4期);第445-451页 *

Also Published As

Publication number Publication date
CN111060975A (en) 2020-04-24

Similar Documents

Publication Publication Date Title
Oliver Information from SAR images
Li et al. Automatic detection of ship targets based on wavelet transform for HF surface wavelet radar
Carrer et al. Automatic enhancement and detection of layering in radar sounder data based on a local scale hidden Markov model and the Viterbi algorithm
Le Bastard et al. A linear prediction and support vector regression-based debonding detection method using step-frequency ground penetrating radar
Dib et al. Ensembles of novelty detection classifiers for structural health monitoring using guided waves
CN110554384A (en) imaging method based on microwave signal
CN115630492B (en) Tunnel lining defect change characteristic intelligent inversion method, system and storage medium
Riaz et al. Principle component analysis and fuzzy logic based through wall image enhancement
CN111060975B (en) Method for detecting ground penetrating radar target
CN109932697B (en) Weak target detection method based on compressed sensing
CN113901878B (en) Three-dimensional ground penetrating radar image underground pipeline identification method based on CNN+RNN algorithm
CN108562877B (en) Deception jamming suppression method based on signal envelope characteristics
CN112989940B (en) Raft culture area extraction method based on high-resolution third satellite SAR image
Gao et al. Fast two‐dimensional subset censored CFAR method for multiple objects detection from acoustic image
Wang et al. A deep learning network to improve tunnel lining defect identification using ground penetrating radar
Yang et al. Array Three-Dimensional SAR Imaging via Composite Low-Rank and Sparse Prior.
CN108196238A (en) Clutter map detection method based on adaptive matched filter under Gaussian background
Zhang et al. Imaging processor for multi-receiver SAS in the presence of partially failed receivers
Ginolhac et al. Derivation of the bias of the normalized sample covariance matrix in a heterogeneous noise with application to low rank STAP filter
Sun et al. Clutter removal in ground-penetrating radar images using deep neural networks
Zhang et al. A deep learning method for rough surface clutter reduction in GPR image
CN111695087B (en) Method and system for detecting composite lightning detection target
Chan et al. Generalized two-sided linear prediction approach for land mine detection
Juan et al. A new Wavelet Prediction method for GPR clutter elimination Based on LSTM network
Zhang et al. Track detection of underwater moving targets based on CFAR

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant