CN115656963B - Clutter suppression method in non-parametric signal space - Google Patents

Clutter suppression method in non-parametric signal space Download PDF

Info

Publication number
CN115656963B
CN115656963B CN202211671774.8A CN202211671774A CN115656963B CN 115656963 B CN115656963 B CN 115656963B CN 202211671774 A CN202211671774 A CN 202211671774A CN 115656963 B CN115656963 B CN 115656963B
Authority
CN
China
Prior art keywords
space
canceller
clutter
signal
dimensional
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
CN202211671774.8A
Other languages
Chinese (zh)
Other versions
CN115656963A (en
Inventor
王梓谦
卢璞
张东洁
马亮
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.)
Nanjing Tianlang Defense Technology Co ltd
Original Assignee
Nanjing Tianlang Defense Technology Co ltd
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 Nanjing Tianlang Defense Technology Co ltd filed Critical Nanjing Tianlang Defense Technology Co ltd
Priority to CN202211671774.8A priority Critical patent/CN115656963B/en
Publication of CN115656963A publication Critical patent/CN115656963A/en
Application granted granted Critical
Publication of CN115656963B publication Critical patent/CN115656963B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

The invention provides a clutter suppression method in a non-parametric signal space, which comprises the following steps: s1, acquiring an echo signal in a radar pulse string detection mode; s2, constructing an N-dimensional canceller space based on the basic canceller space; s3, performing projection calculation on the N-dimensional canceller space to obtain the projection quantity of echo signals on eigenstates corresponding to clutter subspaces in the N-dimensional canceller space; s4, reconstructing Doppler domain signals of clutter subspaces according to the projection quantity to obtain reconstructed signals; s5, obtaining a noise space signal of the echo signal according to the echo signal and the reconstruction signal in S4. The invention does not depend on samples, and has convenient calculation and controllable dimension; the clutter suppression capability is strong, the echo has a deep notch on a low-frequency component which is guided by clutter, the suppression is thorough, and the echo can be flexibly adjusted according to the actual use scene; the passband is flat, the influence on the target echo is small, the sampling structure of the radar echo in the Doppler dimension is not changed, and various subsequent algorithms can be adapted.

Description

Clutter suppression method in non-parametric signal space
Technical Field
The invention belongs to the technical field of radio, in particular to the technical field of radar clutter suppression.
Background
Radar is taken as an all-weather detection technology, and is being regarded as an important guarantee for rapid development of emerging civil fields such as automatic driving, low-altitude control and the like. At this time, the clutter suppression requirement of the radar is becoming more and more urgent, but the conventional radar clutter suppression technology cannot meet the requirement of detecting targets in strong clutter backgrounds such as low altitude, ground and the like, and needs to develop a new clutter suppression technology, and one hopes that the conventional radar clutter suppression technology can meet the following conditions:
1. clutter suppression capability is strong.
2. While suppressing clutter, the original sampling structure of target detection is not changed, and the signal of the target is not destroyed.
3. The clutter suppression technique requires as little computation as possible.
4. The method can be conveniently adapted to various future software-defined detection signal forms.
Clutter has been a signal background generated by real scatterers that is not of interest to detection systems and its suppression method has been of great interest. Targets detected by the air traffic control radar and the automatic driving radar are usually located under a strong clutter background, and clutter formed by reflection radar echoes of the ground, buildings, plants and the like lifts the background of target detection, so that the detection probability of the targets is reduced, and the targets are required to be detected after clutter suppression is carried out on the detection echoes of the radar.
Historically, radar systems employing pulse train detection systems have been invented that attempt to separate target echoes of interest from clutter of no interest, taking advantage of the fact that clutter velocity approaches zero, while the target has a certain radial velocity. The principle is as follows: the radar transmits a pulse train with a certain repetition frequency for detection, and if a received signal comes from clutter, obvious Doppler phase shift does not occur in an echo; if the echo has a significant doppler shift, then it is likely to result from reflection of the transmitted signal by a moving object. In the Doppler domain, a common clutter suppression method involves the following. Firstly, a moving target indication technology (MTI) is adopted, a finite impulse response filter is constructed by cascading a plurality of double pulse cancellers, and low-frequency components of the finite impulse response filter are restrained; in addition, the moving target detection technique (MTD) also has a certain clutter suppression technique; the method of Principal Component Analysis (PCA) is utilized to construct a signal space adapting to the clutter model, and the clutter suppression is performed by utilizing a data dimension reduction mode, so that the method is a parameterized clutter suppression means with good performance.
However, the above methods all have respective drawbacks. MTI clutter suppression performance is strong, but passband loss is large, and the sampling structure of target echoes is influenced, so that subsequent echo accumulation is not facilitated; the MTD technology has limited clutter suppression capability and cannot be independently used for suppressing strong ground clutter; the parameterized clutter suppression method represented by PCA needs to model clutter in advance, has huge calculation amount, is only suitable for a large-sized air-borne radar system with fixed geographic position and strong calculation capability, and cannot exert the advantages of the parameterized method for a small-sized radar with changeable detection scene and limited calculation force.
Disclosure of Invention
In view of the foregoing, the present invention is directed to a technique for performing clutter suppression in a non-parameterized space, including a technique for generating a canceller space of arbitrary dimensions and a technique for performing clutter suppression in the signal space. The method has strong clutter suppression capability and high clutter improvement factor.
The application relates to a clutter suppression method in a non-parametric signal space, comprising the following steps:
s1, acquiring an echo signal in a radar pulse string detection mode;
s2, constructing an N-dimensional canceller space based on the basic canceller space;
s3, performing projection calculation on the N-dimensional canceller space to obtain the projection quantity of the echo signal on the eigen state corresponding to the clutter subspace in the N-dimensional canceller space;
s4, reconstructing Doppler domain signals of the clutter subspace according to the projection quantity to obtain reconstructed signals;
and S5, obtaining a noise space signal of the echo signal according to the echo signal and the reconstruction signal in the S4.
Further, the step S2 includes:
s2.1, selecting a basic canceller space, wherein the basic canceller space comprises a two-dimensional signal space of second-order canceller eigenstates;
s2.2, confirming the dimension of the target space, selecting a pulse string detection mode, and recording the number of pulses as N, namely N is the dimension of the target space;
s2.3, generating N-dimensional canceller space by using basic canceller space through recursive dimension-increasing calculation, wherein each recursive dimension-increasing comprises a new eigenstate generation calculation which is marked as U n And multiple old eigenstate dimension-increasing calculations, denoted as U p 。
Further, step S2.1 is: the two-dimensional signal space comprises second-order canceller eigenstates, and the second-order canceller eigenstates are defined as follows:
Figure 714057DEST_PATH_IMAGE002
，
the two-dimensional signal space also comprises second-order accumulator eigenstates which are marked as follows:
Figure 761648DEST_PATH_IMAGE004
，
the signal space formed by the two eigenstates is the basic canceller space, denoted as W 2 。
Further, step S2.3 is:
defining an N-dimensional canceller space as W N Then the basic canceller space W 2 Obtaining N-dimensional canceller space W through N-2 recursion rising dimensions N ；
In each recursive dimension-increasing process, in the canceller space W K+1 Middle and first K eigenstates pass through W K Each eigenstate of (1) passes through U p Calculated, the canceller space W K+1 The last eigenstate of (1) is represented by W K The last eigenstate in (a) passes U n And (5) calculating to obtain the product.
Further, in the step S3:
the radar echo signal is an N-dimensional column vector, which is marked as S N ；
In the N-dimensional canceller space, clutter subspaces are the 1 st to M th eigenstates of the N-dimensional canceller space, and noise subspaces are the M+1 th to N th eigenstates of the N-dimensional canceller space; wherein the value of M is set according to the clutter intensity.
Further, in step S3, the projection amount of the echo signal on the eigenstates corresponding to the clutter subspace in the N-dimensional canceller space is:
setting a serial number of a certain set eigenstate in the clutter subspace as M, wherein M is not greater than M, and then the projection quantity of the echo signal on the eigenstate
Figure 71406DEST_PATH_IMAGE006
The method comprises the following steps:
Figure 955180DEST_PATH_IMAGE008
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure 8587DEST_PATH_IMAGE010
is the left vector corresponding to the eigenstate.
Further, in step S4, reconstructing the doppler domain signal of the clutter subspace specifically includes:
doppler domain signal C for a clutter subspace N The method comprises the following steps:
Figure 910684DEST_PATH_IMAGE012
g (m) represents the scaling factor of the mth eigenstate, which is a real number, when clutter is reconstructed;
g (m) represents clutter suppression, the energy of the signal from 1-g (m) at the mth eigenstate is preserved.
Further, in step S5, the noise space signal X N According to the formula:
Figure 391343DEST_PATH_IMAGE014
after noise separation, the noise zone signal represents the clutter suppressed signal.
As a preferred embodiment of the present application, m=0 is set for a clutter free scene, i.e. no clutter suppression is adopted;
for a weak clutter scene, setting M as a maximum integer not greater than 20% of the value of N;
for a strong clutter scene, M is set to a maximum integer no greater than 30% of the value of N.
The examples of the invention show that compared with the prior art, the invention has the remarkable advantages that:
(1) The clutter suppression capability is strong, and the clutter improvement factor is high;
(2) The passband is flat, and the target loss outside the clutter is small;
(3) The signal space generating step is quick, and the signal space is a non-parameterized signal space, i.e. the signal space is independent of samples;
(4) The calculated amount is optimized, and the dimension is flexible and changeable.
Drawings
FIG. 1 is a structural flow diagram of an overall implementation of the present application;
FIG. 2a is a flow chart of a simulation experiment according to an embodiment;
FIG. 2b is a flow chart of clutter suppression involved in the simulation;
FIG. 2c is a schematic diagram of clutter eigenstates in the canceller space constructed according to the embodiments;
FIG. 3a is a time domain feature diagram of the raw data, reconstructed clutter signals, and noise spatial projection components involved in an embodiment;
FIG. 3b is a graph of the coherent accumulation of the raw data and the noise spatial projection component;
FIG. 4a is a graph of filter frequency response performance versus in the canceller space;
fig. 4b is a graph of tuning performance versus filter frequency response in the canceller space.
Detailed Description
It is to be readily understood that, according to the technical solution of the present invention, a person skilled in the art can imagine various embodiments of the multiband communication receiver according to the present invention without changing the true spirit of the present invention. Accordingly, the following detailed description and drawings are merely illustrative of the invention and are not intended to be exhaustive or to limit or restrict the invention.
The application relates to a clutter suppression method in a non-parametric signal space, comprising the following steps:
s1, acquiring an echo signal in a radar pulse string detection mode;
s2, constructing an N-dimensional canceller space based on the basic canceller space;
s3, performing projection calculation on the N-dimensional canceller space to obtain the projection quantity of the echo signal on the eigen state corresponding to the clutter subspace in the N-dimensional canceller space;
s4, reconstructing Doppler domain signals of the clutter subspace according to the projection quantity to obtain reconstructed signals;
and S5, obtaining the components of the noise subspace, namely the noise space signal of the echo signal, according to the echo signal and the reconstruction signal in the S4.
As shown in fig. 1, the complete technical embodiment of the present invention comprises two parts: an N-dimensional canceller space architecture. And a radar signal processing flow for clutter suppression in the N-dimensional canceller space.
Further, the step S2 includes three steps of basic signal space selection, target space dimension confirmation, and recursive dimension increase:
s2.1, selecting a basic canceller space, wherein the basic canceller space comprises a two-dimensional signal space of second-order canceller eigenstates:
selecting the eigenstates of the second-order canceller by:
Figure 496834DEST_PATH_IMAGE016
and a second order accumulator eigenstate, noted:
Figure 353931DEST_PATH_IMAGE018
the stretched signal space is taken as a basic signal space and is marked as W 2 Here, " T "stands for transpose.
S2.2, confirming the dimension of the target space, selecting a pulse string detection mode, and recording the number of pulses as N, namely N is the dimension of the target space; under the simulation condition of this embodiment, the doppler dimension of the original echo is N, and thus the target space dimension is also N.
S2.3, generating N-dimensional canceller space by using basic canceller space through recursive dimension-increasing calculation, wherein each recursive dimension-increasing comprises a new eigenstate generation calculation which is marked as U n And multiple old eigenstate dimension-increasing calculations, denoted as U p . Defining an N-dimensional canceller space as W N Then the basic canceller space W 2 Obtaining N-dimensional canceller space W through N-2 recursion rising dimensions N The method comprises the steps of carrying out a first treatment on the surface of the In each recursive dimension-increasing process, in the canceller space W K+1 Middle and first K eigenstates pass through W K Each eigenstate of (1) passes through U p Calculated, the canceller space W K+1 The last eigenstate of (1) is represented by W K The last eigenstate in (a) passes U n And (5) calculating to obtain the product. Under the simulation condition of the embodiment, the basic canceller space is subjected to N-2 recursive dimension-increasing calculations, and finally an N-dimensional canceller space is generated.
And step 3, performing clutter suppression in an N-dimensional canceller space, wherein the radar signal processing flow comprises N-dimensional canceller space projection calculation, namely performing projection calculation on the N-dimensional canceller space to obtain the projection quantity of the echo signal on the eigenstates corresponding to the clutter subspace in the N-dimensional canceller space.
N-dimensional canceller spatial projection calculation:
in this embodiment, the clutter subspace dimension is M. The Doppler dimension of the original echo is N. Then, for N echo data in each distance unit, space projection calculation is performed to calculate the components in clutter subspace. Projection calculation calculates only echoes S within a certain distance unit N The composition in clutter subspace, i.e. S N Projection amounts on the first M eigenstates. Setting the sequence number of a certain eigenstate in clutter subspace as M, wherein M is not greater than M, and the projection quantity of echo on the eigenstate
Figure 110535DEST_PATH_IMAGE020
The method comprises the following steps:
Figure 762096DEST_PATH_IMAGE022
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure 638040DEST_PATH_IMAGE024
is the left vector corresponding to the eigenstate. Here, the right vector is a column vector, and the left vector is a conjugate transpose of the right vector. Namely: for the following right vectors:
Figure 33249DEST_PATH_IMAGE026
the left vector is:
Figure 582042DEST_PATH_IMAGE028
。
step 4, the radar signal processing flow for clutter suppression in the N-dimensional canceller space comprises clutter reconstruction, and the Doppler domain signal of the clutter subspace is reconstructed according to the projection quantity to obtain a reconstructed signal;
clutter reconstruction:
the step uses the result of the projection of the echo in the clutter subspace obtained by the space projection calculation, and simultaneously, the Doppler domain signal C of the clutter is reconstructed by combining the preset scaling coefficient g (m) N . The calculated values are as follows:
Figure 732401DEST_PATH_IMAGE030
g (m) represents the scaling factor of the mth eigenstate, which is a real number, when clutter is reconstructed;
g (m) represents clutter suppression, the energy of the signal from 1-g (m) at the mth eigenstate is preserved.
The value of g (m) is set to 1 for any m, regardless of low-speed target detection in the impurity band.
The radar signal processing flow for performing clutter suppression in the N-dimensional canceller space in step 5 further comprises a noise separation step, and noise space signals of the echo signals are obtained according to the echo signals and the reconstruction signals in step S4.
And (3) noise separation:
the noise separation step involves only vector subtraction. This step calculates the acquired clutter time domain signal C using spatial projection N To reconstruct S N In the noise subspace, i.e. noise space signal X N ：
Figure 999434DEST_PATH_IMAGE032
Thus, all clutter suppression steps are completed, X N Is echo S N The projection components in the noise space, i.e. the echo signals after clutter suppression.
Examples
The effects of the present invention will now be described in a specific embodiment. The embodiment comprises four parts of flow setting and parameter setting of a simulation experiment, specific implementation of space clutter suppression of a canceller, simulation experiment result display and demonstration result analysis.
1. Flow setting and parameter setting of experiments
FIG. 2a is a block diagram of a simulation verification setup of an embodiment of the present invention.
The demonstration verification content of the experiment is to detect a moving target in a clutter background. In the detection process, the clutter suppression method described by the invention is selected.
1.1 The embodiment first determines radar detection parameters, which refer to the 24GHz autopilot radar commonly used today. The parameters are as follows:
probe system: burst detection, number of pulses is 100.
Radar center frequency: 24GHz.
Burst repetition period: 10 microseconds.
1.2 The present embodiment determines clutter background parameters in the simulation. Comprises the following information:
single pulse hetero-noise ratio: not less than 40dB.
Clutter time domain signal: and (5) randomly generating.
1.3 The present embodiment determines the target parameters in the simulation. Comprises the following information:
target radial velocity: 24 m/s.
Target echo monopulse signal-to-noise ratio: -3dB.
1.4 Based on the set parameters, an echo sequence is generated, wherein the echo comprises target echo, clutter and background noise.
1.5 The clutter suppression method is used for performing clutter suppression on the echo sequence containing targets, clutter and noise.
1.6 And demonstrating clutter suppression results. The means for demonstration comprise two types:
first, the frequency response of the clutter suppression technique described by the present invention is calculated.
And secondly, coherent accumulation is carried out on the echo after clutter suppression, and the method is used for demonstrating that the target can be clearly detected after accumulation. The coherent accumulation is a classical method of radar detection and has no substantial relation with the present disclosure.
2. Specific implementation of canceller space clutter suppression
Fig. 2b shows an echo processing procedure according to an embodiment of the present invention. The following is described in detail in connection with fig. 2 b:
2.1 And designing the canceller space according to the detection parameters. This is a 100-dimensional canceller space constructed by recursively increasing dimensions 98 times through the base canceller space.
2.2 Will echo data S 100 And inputting the data into a signal space to perform projection calculation. In the calculation, the number of clutter eigenstates, i.e., m=4, is set. At the same time, the projection components of the signal at the first 4 clutter eigenstates are calculated. The time domain version of these four clutter eigenstates is shown in fig. 2 c.
Fig. 3a and 3b show a typical numerical simulation process according to the present embodiment, in which the raw data S 100 Reconstructed clutter signal C 100 Noise spatial projection component X 100 Time domain features (comprising real and imaginary parts) of (a) are shown in fig. 3 a;
2.3 Reconstructing clutter component C in echo data based on the projection component 100 . The scaling factor at the time of reconstruction is set as follows:
Figure 11384DEST_PATH_IMAGE034
the clutter component reconstruction calculation process is as follows:
Figure 414683DEST_PATH_IMAGE036
2.4 From echo data S 100 Reconstructed clutter component C 100 Calculating the component X of echo in noise space 100 And outputting, and completing clutter suppression. The calculation process is as follows:
Figure 735943DEST_PATH_IMAGE038
2.5 Signal X after clutter suppression 100 And sending the signal to a coherent accumulation and detection module for processing.
3. Clutter suppression results of the invention are displayed.
3.1 demonstrates the effect of the clutter suppression technique of the present invention in the time domain: we have chosen a typical primary simulation result, S in the time domain 100 ，C 100 X is as follows 100 Are shown in figure 3 a.
3.2 The effect of clutter suppression in the present invention is demonstrated from the Doppler domain: for echo component X after clutter suppression 100 After subsequent coherent accumulation and detection, the target can be detected rapidly. The target is located in the doppler channel with sequence number 4, with a corresponding velocity of about 24 m/s. This is consistent with our simulation setup. By contrast, we also provide S 100 And (5) directly coherently accumulating the result without clutter suppression. The effect is shown in fig. 3b.
4. Clutter suppression performance analysis of the present invention
As a preferred embodiment of the present application, m=0 is set for a clutter free scene, i.e. no clutter suppression is adopted; for a weak clutter scene, setting M as a maximum integer not greater than 20% of the value of N; for a strong clutter scene, M is set to a maximum integer no greater than 30% of the value of N. The demonstration results show that: under the clutter background, the invention can construct a signal space in a non-parameterized form under the condition of not depending on samples, and realize the accurate reconstruction of clutter signals in radar echo in the signal space, thereby realizing clutter suppression. The clutter suppression process of the present invention may be equivalent to a filter.
Using the frequency response criteria of the clutter suppression filter to measure the clutter suppression performance of the present invention, as shown in fig. 4a, 4b, it can be found that very deep notches can be realized near low frequencies, which means that the technique can well suppress clutter components in echoes; at the same time, it is also capable of having a flat passband characteristic, which means that the technique can suppress clutter without causing distortion to the target echo signal.
Fig. 4a is a clutter suppression performance analysis in the canceller space: filter frequency response, clutter suppression performance analysis in the canceller space of fig. 4 a: analysis of the filter frequency response performance in the clutter suppression frequency response shown in fig. 4a, we note that under the simulation parameters of the embodiment, the present invention can achieve-98.9 dB suppression of clutter with a velocity spread around 0.5 m/s, while the energy loss is only-0.63 dB near the passband edge, e.g., for target echoes with a radial velocity of 14.79 m/s. The remaining passband losses are smaller, approaching lossless.
The width and depth of the notch of the filter are controlled by adjusting the value of M, and the method can flexibly adjust the frequency response curve by adjusting the number M of clutter eigenstates and the value of the scaling coefficient g (M) in the step of clutter suppression, so that the method is suitable for clutter environments faced by various unmanned radars and low-altitude control radars. The effect is shown in fig. 4b, and fig. 4b is an analysis of clutter congestion performance in the clarifier space: the method comprises the steps of adjusting the frequency response of a filter, and controlling the width and the depth of a notch of the filter by adjusting an M value, wherein the M value is adjusted to be coarse adjustment, and affects the width and the depth of the notch of the frequency response of the whole clutter suppression; the adjustment of the g (m) value is fine-tuned, and each change in the g (m) value can modify the shape of the spurious suppression frequency response notch.
Compared with the traditional technology, the invention does not depend on a sample, namely, the invention is a non-parameterized method, and has convenient calculation and controllable dimension;
secondly, the clutter suppression capability is strong, the echo has a deep notch on a low-frequency component which is guided by clutter, the suppression is thorough, and the echo can be flexibly adjusted according to the actual use situation;
and finally, the passband is flat, the influence on the target echo is small, the sampling structure of the radar echo in the Doppler dimension is not changed, and various subsequent algorithms can be adapted.
Therefore, the invention is an effective solution to the radar clutter suppression performance requirements in the field of aviation control and autopilot.

Claims (7)

1. A method of clutter suppression in non-parameterized signal spaces, the method comprising:
s1, acquiring an echo signal in a radar pulse string detection mode;
s2, constructing an N-dimensional canceller space based on the basic canceller space;
s3, performing projection calculation on the N-dimensional canceller space to obtain the projection quantity of the echo signal on the eigen state corresponding to the clutter subspace in the N-dimensional canceller space;
s4, reconstructing Doppler domain signals of the clutter subspace according to the projection quantity to obtain reconstructed signals;
s5, obtaining a noise space signal of the echo signal according to the echo signal and the reconstruction signal in the S4;
the step S2 includes:
s2.1, selecting a basic canceller space, wherein the basic canceller space comprises a two-dimensional signal space of second-order canceller eigenstates; s2.2, confirming the dimension of the target space, selecting a pulse string detection mode, and recording the number of pulses as N, namely N is the dimension of the target space;
s2.3, generating N-dimensional canceller space by using basic canceller space through recursive dimension-increasing calculation, wherein each recursive dimension-increasing comprises a new eigenstate generation calculation which is marked as U n And multiple old eigenstate dimension-increasing calculations, denoted as U p 。
2. The clutter suppression method in non-parametric signal space according to claim 1, wherein step S2.1 is: the two-dimensional signal space comprises second-order canceller eigenstates, and the second-order canceller eigenstates are defined as follows:
Figure FDA0004081049690000011
the two-dimensional signal space also comprises second-order accumulator eigenstates which are marked as follows:
Figure FDA0004081049690000012
the signal space formed by the two eigenstates is the basic canceller space, denoted as W 2 。
3. The clutter suppression method in non-parametric signal space according to claim 1, wherein step S2.3 is:
defining an N-dimensional canceller space as W N Then the basic canceller space W 2 Obtaining N-dimensional canceller space W through N-2 recursion rising dimensions N ；
In each recursive dimension-increasing process, in the canceller space W K+1 Middle and first K eigenstates pass through W K Each eigenstate of (1) passes through U p Calculated, the canceller space W K+1 The last eigenstate of (1) is represented by W K The last eigenstate in (a) passes U n And (5) calculating to obtain the product.
4. The clutter suppression method in non-parametric signal space according to claim 1, characterized in that in said step S3:
the radar echo signal is an N-dimensional column vector, which is marked as S N ；
In the N-dimensional canceller space, clutter subspaces are the 1 st to M th eigenstates of the N-dimensional canceller space, and noise subspaces are the M+1 th to N th eigenstates of the N-dimensional canceller space; wherein the value of M is set according to the clutter intensity.
5. The clutter suppression method in a non-parametric signal space according to claim 4, wherein in step S3, the projection amount of the echo signal on the clutter subspace corresponding eigenstates in the N-dimensional canceller space is:
setting a serial number of a certain set eigenstate in the clutter subspace as M, wherein M is not greater than M, and then the projection quantity P of the echo signal on the eigenstate N (m) is:
P N (m)＝<m N |S N
wherein, the liquid crystal display device comprises a liquid crystal display device,<m N and I is the left vector corresponding to the eigenstate.
6. The method for clutter suppression in a non-parameterized signal space according to claim 5, wherein the reconstructing of the doppler domain signal of the clutter subspace in step S4 is specifically:
doppler domain signal C for a clutter subspace N The method comprises the following steps:
Figure FDA0004081049690000021
g (m) represents the scaling factor of the mth eigenstate, which is a real number, when clutter is reconstructed;
g (m) represents clutter suppression, the energy of the signal from 1-g (m) at the mth eigenstate is preserved.
7. The method of clutter suppression in non-parameterized signal space of claim 6,
in step S5, the noise space signal X N According to the formula:
X N ＝S N -C N ，
after noise separation, the noise zone signal represents the clutter suppressed signal.
CN202211671774.8A 2022-12-26 2022-12-26 Clutter suppression method in non-parametric signal space Active CN115656963B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202211671774.8A CN115656963B (en) 2022-12-26 2022-12-26 Clutter suppression method in non-parametric signal space

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202211671774.8A CN115656963B (en) 2022-12-26 2022-12-26 Clutter suppression method in non-parametric signal space

Publications (2)

Publication Number Publication Date
CN115656963A CN115656963A (en) 2023-01-31
CN115656963B true CN115656963B (en) 2023-05-09

Family

ID=85022561

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202211671774.8A Active CN115656963B (en) 2022-12-26 2022-12-26 Clutter suppression method in non-parametric signal space

Country Status (1)

Country Link
CN (1) CN115656963B (en)

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5617099A (en) * 1996-01-22 1997-04-01 Hughes Aircraft Company Adaptive filtering of matched-filter data
CN102608587B (en) * 2012-03-07 2013-11-27 中国民航大学 Air mobile target detection method based on nonlinear least square
JP2018205022A (en) * 2017-05-31 2018-12-27 パナソニック株式会社 Radar signal processor and radar signal processing method
CN110221288A (en) * 2019-05-17 2019-09-10 常州第四无线电厂有限公司 A kind of through-wall radar fine motion object detection method
CN111965612B (en) * 2020-07-08 2023-12-22 西安电子科技大学 Clutter suppression method based on subspace projection
CN113671456A (en) * 2021-09-17 2021-11-19 南京国立电子科技有限公司 Radar signal processing simulation platform and simulation method

Also Published As

Publication number Publication date
CN115656963A (en) 2023-01-31

Similar Documents

Publication Publication Date Title
Cuomo et al. Ultrawide-band coherent processing
CN106707258A (en) Multi-parameter estimation method for micro-motion target under non-Gaussian background
Moses et al. An autoregressive formulation for SAR backprojection imaging
Fang et al. Radar maneuvering target detection based on two steps scaling and fractional Fourier transform
CN103969644A (en) Multichannel frequency modulated continuous wave SAR (synthetic aperture radar) imaging method
Yu et al. Ground moving target motion parameter estimation using Radon modified Lv's distribution
DE3120490A1 (en) METHOD AND DEVICE FOR REAL-TIME RADAR SIGNAL PROCESSING
CN109061626B (en) Method for detecting low signal-to-noise ratio moving target by step frequency coherent processing
Wang et al. Prior-guided deep interference mitigation for FMCW radars
CN115616518A (en) Method for estimating weak target motion parameters and reconstructing high-resolution range profile of broadband radar
CN105929397A (en) Displaced phase center antenna imaging method based on lq regularization
Hu et al. Applications of wavelet analysis in differential propagation phase shift data de-noising
CN115656963B (en) Clutter suppression method in non-parametric signal space
CN112162254A (en) Method for estimating radial velocity and radial acceleration of target based on ultra-wideband signal
JP2002131421A (en) Radar signal processing device and method thereof
Perry et al. Planar subarray processing for SAR imaging
Davis et al. Aliasing in recurrently sampled signals with an application to synthetic aperture imaging
CN112731283B (en) High subsonic flight target acoustic direction finding method based on multistage wiener filter
CN114895306A (en) High-resolution wide swath imaging method and device and storage medium
CN114910905A (en) GEO satellite-machine bistatic SAR moving target intelligent imaging method under similarity constraint
Moses et al. Recursive SAR imaging
US6563455B2 (en) Universal two-channel STAP filter
Lu et al. Robust Adaptive Pulse Compression Under Encode-Decoder Network for Synthetic Aperture Radar
Zhang et al. Adaptive sidelobe suppression of sar images with arbitrary doppler centroids and bandwidths
CN108802706A (en) Modulated Frequency Stepped Radar Signal target extract method based on location position

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