CN111060879B - Joint side lobe suppression method based on two-dimensional matched filtering result - Google Patents

Joint side lobe suppression method based on two-dimensional matched filtering result Download PDF

Info

Publication number
CN111060879B
CN111060879B CN201911158884.2A CN201911158884A CN111060879B CN 111060879 B CN111060879 B CN 111060879B CN 201911158884 A CN201911158884 A CN 201911158884A CN 111060879 B CN111060879 B CN 111060879B
Authority
CN
China
Prior art keywords
matched filtering
target
doppler
pulse
estimation result
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
CN201911158884.2A
Other languages
Chinese (zh)
Other versions
CN111060879A (en
Inventor
吴嗣亮
田静
张彪
崔嵬
宁晨
王烽宇
孔梓丞
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.)
Beijing Institute of Technology BIT
Original Assignee
Beijing Institute of Technology BIT
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 Beijing Institute of Technology BIT filed Critical Beijing Institute of Technology BIT
Priority to CN201911158884.2A priority Critical patent/CN111060879B/en
Publication of CN111060879A publication Critical patent/CN111060879A/en
Application granted granted Critical
Publication of CN111060879B publication Critical patent/CN111060879B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • G01S7/023—Interference mitigation, e.g. reducing or avoiding non-intentional interference with other HF-transmitters, base station transmitters for mobile communication or other radar systems, e.g. using electro-magnetic interference [EMI] reduction techniques
    • 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
    • G01S7/418—Theoretical aspects
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03H—IMPEDANCE NETWORKS, e.g. RESONANT CIRCUITS; RESONATORS
    • H03H17/00—Networks using digital techniques
    • H03H17/02—Frequency selective networks
    • H03H17/0211—Frequency selective networks using specific transformation algorithms, e.g. WALSH functions, Fermat transforms, Mersenne transforms, polynomial transforms, Hilbert transforms
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03H—IMPEDANCE NETWORKS, e.g. RESONANT CIRCUITS; RESONATORS
    • H03H17/00—Networks using digital techniques
    • H03H17/02—Frequency selective networks
    • H03H17/0248—Filters characterised by a particular frequency response or filtering method
    • H03H17/0254—Matched filters
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03H—IMPEDANCE NETWORKS, e.g. RESONANT CIRCUITS; RESONATORS
    • H03H21/00—Adaptive networks
    • H03H21/0012—Digital adaptive filters
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03H—IMPEDANCE NETWORKS, e.g. RESONANT CIRCUITS; RESONATORS
    • H03H21/00—Adaptive networks
    • H03H21/0012—Digital adaptive filters
    • H03H21/0018—Matched filters
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03H—IMPEDANCE NETWORKS, e.g. RESONANT CIRCUITS; RESONATORS
    • H03H21/00—Adaptive networks
    • H03H21/0012—Digital adaptive filters
    • H03H21/0043—Adaptive algorithms
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03H—IMPEDANCE NETWORKS, e.g. RESONANT CIRCUITS; RESONATORS
    • H03H21/00—Adaptive networks
    • H03H21/0012—Digital adaptive filters
    • H03H2021/0085—Applications
    • H03H2021/0094—Interference Cancelling

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Computer Hardware Design (AREA)
  • Algebra (AREA)
  • Computing Systems (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Radar Systems Or Details Thereof (AREA)

Abstract

The invention discloses a combined side lobe suppression method based on a two-dimensional matched filtering result, which is used for solving the problems that a weak target is submerged in a strong target side lobe or multiple target side lobes are superposed to form a false target and the like in the radar distance-Doppler imaging process and quickly realizing the combined estimation of a distance and Doppler dimension two-dimensional image; windowing is carried out on a two-dimensional matched filtering result in a distance dimension and a Doppler dimension simultaneously, distance-Doppler two-dimensional side lobe combined suppression is achieved by using an iterative adaptive method based on weighted least squares, and parameter estimation precision and imaging quality of a target in a multi-target scene are improved; due to the adoption of windowing processing, the covariance matrix is subjected to dimension reduction processing, the calculation complexity can be reduced, and the calculation amount is further reduced by utilizing the structural relationship among the matrixes.

Description

Joint side lobe suppression method based on two-dimensional matched filtering result
Technical Field
The invention belongs to the technical field of radar measurement, and particularly relates to a combined side lobe suppression method based on a two-dimensional matched filtering result.
Background
In the pulse radar distance-Doppler imaging process, the traditional matched filtering algorithm has the problem of high side lobe, when the target distribution is close, a weak target is easily submerged in the side lobe of a strong target, and the parameter estimation effect and the imaging quality are influenced. In order to suppress the side lobe interference and improve the imaging quality, an Adaptive pulse compression algorithm based on the minimum mean square error is proposed in the text "Adaptive pulse compression vision MMSE estimation" published by Blunt et al, from page 572 to page 584 in the 2 nd period of volume 42 of IEEE transmission on Aerospace and Electronic Systems, 2006, so as to obtain a good side lobe suppression effect. However, since the algorithm relies on the hyper-parameter, the hyper-parameter needs to be adjusted during each iteration to ensure that the matrix is invertible. In 2008, "Transmit codes and receive filters for pulse compression radar systems" published by IEEE International Conference on Acoustics, spech and Signal Processing, Li J et al, a tool variable algorithm is proposed, which effectively suppresses the distance dimension side lobe but cannot completely suppress the doppler side lobe compared to the matched filter algorithm. In 2009 IEEE Transactions on Signal Processing 57, vol.3, page 1084 to page 1097, Li J et al in "Range-Doppler imaging via a train of combining pulses" propose a non-parametric iterative adaptive algorithm based on weighted least squares, which can suppress the Range-Doppler side lobe to the noise floor to obtain a high-quality Range-Doppler image. However, the huge calculation amount of the algorithm limits the application of the algorithm in a real-time system.
Disclosure of Invention
In view of the above, the present invention provides a joint side lobe suppression method based on a two-dimensional matched filtering result, so as to solve the problems that a weak target is submerged in a strong target side lobe or a false target is formed by stacking multiple target side lobes in a radar range-doppler imaging process, and quickly achieve joint estimation of a range and doppler dimensional two-dimensional image.
A combined sidelobe suppression method, comprising:
step 1, establishing a moving target echo signal model, and carrying out two-dimensional matched filtering on a received signal, wherein the specific method comprises the following steps:
the pulse radar is assumed to transmit M coherent pulses with the same waveform, and the pulse length is N; the vector of fast time samples of the transmit pulse is denoted as s ═ s0 s1 ... sN-1]T(ii) a Let ymThe echo signal representing the mth pulse, for which L is 0,1,2, …, L-1, in the range bin of interest, the N consecutive samples of the echo signal corresponding to the mth pulse in the mth range bin are represented as:
Figure GDA0003164572350000021
wherein:
Figure GDA0003164572350000022
Figure GDA0003164572350000023
sampling for a number N of consecutive range directions corresponding to the kth radial velocity, x (l, k) representing the target backscatter coefficient at the kth doppler cell of the ith range cell; omegakLet T be the Doppler frequency corresponding to the kth radial velocityrωk＝θkAssuming 2 π (K-K/2)/K- π/K ≦ θk< 2 pi (K-K/2)/K + pi/K, where K is the number of doppler units, K is 0,1, …, K-1; n ism(l) For additive noise, considering intra-pulse doppler,
Figure GDA0003164572350000031
wherein T isr、TsPulse repetition interval and sampling interval, respectively;
equation (1) is thus written as:
Figure GDA0003164572350000032
wherein
Figure GDA0003164572350000033
JnIs an N × N shift matrix and satisfies:
Figure GDA0003164572350000034
the corresponding N consecutive fast time samples of the i-th range profile of the M pulse echoes are:
Y(l)＝[y0(l) y1(l) … yM-1(l)] (5)
two-dimensional matched filtering is carried out on Y (l), and a target estimation result after matched filtering can be obtained
Figure GDA0003164572350000035
Figure GDA0003164572350000036
Wherein
Figure GDA0003164572350000037
N(l)＝[n0(l) n1(l) … nM-1(l)]；
Step 2, adding a processing window to the two-dimensional matched filtering result, and performing adaptive iterative processing on data in the processing window by using an adaptive method to obtain an estimation result of the target complex amplitude, wherein the specific method comprises the following steps:
adding a processing window to the two-dimensional matched filtering result, and defining the size to be (k)r+kd) X 1 filter vector
Figure GDA0003164572350000041
The following were used:
Figure GDA0003164572350000042
kr1and kr2Respectively matched filtering results
Figure GDA0003164572350000043
The number of distance dimension points before and after; k is a radical ofd1And kd2Respectively matched filtering results
Figure GDA0003164572350000044
Number of points in the front and back Doppler dimensions and kr＝kr1+kr2+1，kd＝kd1+kd2+ 1; order:
Figure GDA0003164572350000051
equation (7) is written as:
Figure GDA0003164572350000052
definition of
Figure GDA0003164572350000053
The interference covariance matrix of (a) is:
Figure GDA0003164572350000054
wherein:
Figure GDA0003164572350000055
constructing a weighted least squares cost function:
Figure GDA0003164572350000056
wherein
Figure GDA0003164572350000057
The minimum value of equation (11) is found for x (l, q):
Figure GDA0003164572350000058
using matrix inversion theorem according to equations (9) and (10), we obtain:
Figure GDA0003164572350000061
thus, formula (13) can be substituted for formula (12) to obtain:
Figure GDA0003164572350000062
and 3, performing iterative operation on the step 2 to inhibit side lobe interference to obtain a final target estimation result, wherein the specific method comprises the following steps:
based on the x (l, q) estimation result obtained in the first iteration obtained in the step 2
Figure GDA0003164572350000063
Then, the calculation of the formulas (9) to (14) is sequentially executed to obtain the estimation result of the second iteration
Figure GDA0003164572350000064
Entering next iteration; and repeating the steps until the iteration termination condition is met, and outputting the estimation result of the last iteration
Figure GDA0003164572350000065
Preferably, the iteration termination condition is: estimation result
Figure GDA0003164572350000066
Is smaller than the set value.
The invention has the following beneficial effects:
the invention provides a low-complexity combined side lobe suppression method based on a two-dimensional matched filtering result, which aims to solve the problem of high side lobes of a distance dimension and a Doppler dimension in a pulse Doppler radar. Windowing is carried out on a two-dimensional matched filtering result in a distance dimension and a Doppler dimension simultaneously, distance-Doppler two-dimensional side lobe combined suppression is achieved by using an iterative adaptive method based on weighted least squares, and parameter estimation precision and imaging quality of a target in a multi-target scene are improved; due to the adoption of windowing processing, the covariance matrix is subjected to dimension reduction processing, the calculation complexity can be reduced, and the calculation amount is further reduced by utilizing the structural relationship among the matrixes.
Drawings
FIG. 1 is a schematic view of a process window.
FIG. 2(a) is Rl+1,qAnd Rl,q+1A matrix;
FIG. 2(b) is a matrix Rl+1,q+1And Rl+1,q，Rl,q+1Schematic diagram of the relationship between them.
Detailed Description
The invention is described in detail below by way of example with reference to the accompanying drawings.
Step 1, establishing a moving target echo signal model, and carrying out two-dimensional matched filtering on a received signal, wherein the specific method comprises the following steps:
assume that a pulse radar transmits M coherent pulses of the same waveform and that the pulse length is N. Then the fast time sample vector of the transmit pulse may be expressed as s ═ s0 s1 ... sN-1]T. Let ymRepresenting the echo signal of the mth pulse, then for the range bin of interest L being 0,1,2, …, L-1, the N consecutive samples of the echo signal corresponding to the mth pulse in the mth range bin may be represented as
Figure GDA0003164572350000071
Wherein
Figure GDA0003164572350000072
Figure GDA0003164572350000073
X (l, k) represents the target backscatter coefficients at the kth doppler cell of the ith range cell for N consecutive range direction samples corresponding to the kth radial velocity. OmegakLet T be the Doppler frequency corresponding to the kth radial velocityrωk＝θkIn general, we assume 2 π (K-K/2)/K- π/K ≦ θk< 2 pi (K-K/2)/K + pi/K, where K is the number of doppler units, K is 0,1, …, K-1; n ism(l) For additive noise, considering intra-pulse doppler,
Figure GDA0003164572350000074
wherein T isr、TsRespectively pulse repetition interval and sampling interval.
Equation (1) can thus be written as:
Figure GDA0003164572350000081
wherein
Figure GDA0003164572350000082
JnIs an N × N shift matrix and satisfies:
Figure GDA0003164572350000083
it is noted that when N > N-1,
Figure GDA0003164572350000084
then the corresponding N consecutive fast time samples of the i-th range profile of the M pulse echoes are:
Y(l)＝[y0(l) y1(l) … yM-1(l)] (5)
two-dimensional matched filtering is carried out on Y (l), and a target estimation result after matched filtering can be obtained
Figure GDA0003164572350000085
Figure GDA0003164572350000086
Wherein
Figure GDA0003164572350000087
N(l)＝[n0(l) n1(l) … nM-1(l)]。
From equation (6), the matched filtering result for a given unit
Figure GDA0003164572350000088
Not only x (l, q), but also the interference and noise of the adjacent unit target images. These unwanted interferences can cause inaccurate target parameter estimation and affect the imaging quality, so that a side lobe suppression method is needed to solve the problem.
Step 2, adding a processing window to the two-dimensional matched filtering result, and performing adaptive iterative processing on data in the processing window by using an adaptive method to obtain an estimation result of the target complex amplitude, wherein the specific method comprises the following steps:
adding a processing window to the two-dimensional matched filtering result, and defining the size to be (k)r+kd) X 1 filter vector
Figure GDA0003164572350000091
The following were used:
Figure GDA0003164572350000092
wherein k isrAnd kdAccording to the calculation requirement and the algorithmCan decide; general requirements, k, ensure that the algorithm converges to the noise floorrAnd kdMinimum value of (d); k is a radical ofr1，kr2，kd1，kd2Respectively matched filtering results
Figure GDA0003164572350000093
Distance before and after and the number of points in the Doppler dimension, and kr＝kr1+kr2+1，kd＝kd1+kd2+1. Order to
Figure GDA0003164572350000101
Then equation (7) can be written as:
Figure GDA0003164572350000102
definition of
Figure GDA0003164572350000103
The interference covariance matrix of (a) is:
Figure GDA0003164572350000104
wherein:
Figure GDA0003164572350000105
constructing a weighted least squares cost function:
Figure GDA0003164572350000106
wherein
Figure GDA0003164572350000107
The minimum value of formula (11) is found for x (l, q), andobtaining:
Figure GDA0003164572350000108
from equations (9) and (10), we can obtain, using matrix inversion theorem:
Figure GDA0003164572350000111
thus, formula (13) can be substituted for formula (12) to obtain:
Figure GDA0003164572350000112
obviously, in the formula (12)
Figure GDA0003164572350000113
Can be prepared from
Figure GDA0003164572350000114
And (4) replacing. From the above derivation we find that the computational effort of the proposed algorithm is mainly reflected in the computation of the covariance matrix, the vector according to equation (10)
Figure GDA0003164572350000115
And
Figure GDA0003164572350000116
respectively Rl+1,q，Rl,q+1And Rl+1,q+1。Rl+1,qAnd Rl,q+1The matrix is shown in FIG. 2(a), the matrix Rl+1,q+1And Rl+1,q，Rl,q+1The relationship between them is shown in FIG. 2(b), and is therefore based on the known Rl+1,qAnd Rl,q+1When calculating Rl+1,q+1We only need to calculate Rl+1,qAnd Rl,q+1The non-overlapping portion, i.e., the shaded portion, of (a) is sufficient, whereby the amount of calculation can be further reduced.
And 3, performing iterative operation on the step 2 to inhibit side lobe interference to obtain a final target estimation result, wherein the specific method comprises the following steps:
from the equation (10), the covariance matrix Rl,qIs related to the unknown signal x (l, q), so it is necessary to fit x (l, q) in a weighted least squares manner and to approximate the true x (l, q) in an iterative manner;
repeating the step 2 until the result converges, namely: in the first iteration, the interference covariance matrix is initialized by using the matched filtering result, and the estimation result of x (l, q) obtained in the first iteration is obtained according to the calculation of the formulas (9) to (14)
Figure GDA0003164572350000117
Based on the estimation result of the first iteration, the calculation of the formulas (9) to (14) is sequentially executed to obtain the estimation result of the second iteration
Figure GDA0003164572350000118
Entering next iteration; and repeating the steps until the iteration termination condition is met, and outputting the estimation result of the last iteration
Figure GDA0003164572350000119
In the present invention, when the estimation result is obtained
Figure GDA00031645723500001110
When the variation range of (2) is smaller than the set value, the iteration can be stopped.
In summary, the above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (2)

1. A combined sidelobe suppression method, comprising:
step 1, establishing a moving target echo signal model, and carrying out two-dimensional matched filtering on a received signal, wherein the specific method comprises the following steps:
hypothetical pulse radarTransmitting M coherent pulses with the same waveform, wherein the pulse length is N; the vector of fast time samples of the transmit pulse is denoted as s ═ s0 s1 ... sN-1]T(ii) a Let ymThe echo signal representing the mth pulse, for which L is 0,1,2, …, L-1, in the range bin of interest, the N consecutive samples of the echo signal corresponding to the mth pulse in the mth range bin are represented as:
Figure FDA0003164572340000011
wherein:
Figure FDA0003164572340000012
Figure FDA0003164572340000013
sampling for a number N of consecutive range directions corresponding to the kth radial velocity, x (l, k) representing the target backscatter coefficient at the kth doppler cell of the ith range cell; omegakLet T be the Doppler frequency corresponding to the kth radial velocityrωk＝θkAssuming 2 π (K-K/2)/K- π/K ≦ θk< 2 pi (K-K/2)/K + pi/K, where K is the number of doppler units, K is 0,1, …, K-1; n ism(l) For additive noise, considering intra-pulse doppler,
Figure FDA0003164572340000014
wherein T isr、TsPulse repetition interval and sampling interval, respectively;
equation (1) is thus written as:
Figure FDA0003164572340000015
wherein
Figure FDA0003164572340000016
JnIs an N × N shift matrix and satisfies:
Figure FDA0003164572340000021
the corresponding N consecutive fast time samples of the i-th range profile of the M pulse echoes are:
Y(l)＝[y0(l) y1(l) … yM-1(l)] (5)
two-dimensional matched filtering is carried out on Y (l), and a target estimation result after matched filtering can be obtained
Figure FDA0003164572340000022
Figure FDA0003164572340000023
Wherein
Figure FDA0003164572340000024
N(l)＝[n0(l) n1(l) … nM-1(l)]；
Step 2, adding a processing window to the two-dimensional matched filtering result, and performing adaptive iterative processing on data in the processing window by using an adaptive method to obtain an estimation result of the target complex amplitude, wherein the specific method comprises the following steps:
adding a processing window to the two-dimensional matched filtering result, and defining the size to be (k)r+kd) X 1 filter vector
Figure FDA0003164572340000025
The following were used:
Figure FDA0003164572340000031
kr1and kr2Respectively matched filtering results
Figure FDA0003164572340000032
The number of distance dimension points before and after; k is a radical ofd1And kd2Respectively matched filtering results
Figure FDA0003164572340000033
Number of points in the front and back Doppler dimensions and kr＝kr1+kr2+1，kd＝kd1+kd2+ 1; order:
Figure FDA0003164572340000034
equation (7) is written as:
Figure FDA0003164572340000041
definition of
Figure FDA0003164572340000042
The interference covariance matrix of (a) is:
Figure FDA0003164572340000043
wherein:
Figure FDA0003164572340000044
constructing a weighted least squares cost function:
Figure FDA0003164572340000045
wherein
Figure FDA0003164572340000046
The derivation of x (l, q) takes the minimum value of equation (11) to obtain:
Figure FDA0003164572340000047
using matrix inversion theorem according to equations (9) and (10), we obtain:
Figure FDA0003164572340000048
thus, formula (13) can be substituted for formula (12) to obtain:
Figure FDA0003164572340000049
and 3, performing iterative operation on the step 2 to inhibit side lobe interference to obtain a final target estimation result, wherein the specific method comprises the following steps:
based on the x (l, q) estimation result obtained in the first iteration obtained in the step 2
Figure FDA00031645723400000410
Then, the calculation of the formulas (9) to (14) is sequentially executed to obtain the estimation result of the second iteration
Figure FDA00031645723400000411
Entering next iteration; and repeating the steps until the iteration termination condition is met, and outputting the estimation result of the last iteration
Figure FDA0003164572340000051
2. A combined sidelobe suppression method according to claim 1, whereinIn that, the iteration termination condition is: estimation result
Figure FDA0003164572340000052
Is smaller than the set value.
CN201911158884.2A 2019-11-22 2019-11-22 Joint side lobe suppression method based on two-dimensional matched filtering result Active CN111060879B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201911158884.2A CN111060879B (en) 2019-11-22 2019-11-22 Joint side lobe suppression method based on two-dimensional matched filtering result

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201911158884.2A CN111060879B (en) 2019-11-22 2019-11-22 Joint side lobe suppression method based on two-dimensional matched filtering result

Publications (2)

Publication Number Publication Date
CN111060879A CN111060879A (en) 2020-04-24
CN111060879B true CN111060879B (en) 2021-09-10

Family

ID=70298130

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201911158884.2A Active CN111060879B (en) 2019-11-22 2019-11-22 Joint side lobe suppression method based on two-dimensional matched filtering result

Country Status (1)

Country Link
CN (1) CN111060879B (en)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112630742B (en) * 2020-12-15 2022-08-26 森思泰克河北科技有限公司 Method, device and equipment for processing high sidelobe Doppler stripe and storage medium
CN113702934B (en) * 2021-06-25 2023-12-05 北京理工大学 distance-Doppler-angle three-dimensional side lobe suppression method suitable for MIMO radar
CN115166664B (en) * 2022-06-12 2024-05-10 北京理工大学 Quick self-adaptive sidelobe suppression method based on two-dimensional matched filtering result
CN116626645B (en) * 2023-07-21 2023-10-20 西安电子科技大学 Broadband radar high-speed target coherent accumulation grating lobe inhibition method

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2008241319A (en) * 2007-03-26 2008-10-09 Mitsubishi Electric Corp Pulse radar system
CN101464513A (en) * 2007-12-21 2009-06-24 中国电子科技集团公司第五十研究所 Non-continuous spectrum high-frequency radar range sidelobe suppression apparatus and method thereof
CN102497340A (en) * 2011-12-31 2012-06-13 中国电子科技集团公司第四十一研究所 Time frequency door processing method based on superposition window suppression algorithm
CN103117977A (en) * 2013-01-18 2013-05-22 中南民族大学 Orthogonal frequency division multiplexing (OFDM) cognitive radio system and non-orthogonal active sidelobe suppression method
CN105699947A (en) * 2016-01-25 2016-06-22 电子科技大学 SAR image sidelobe inhibition method
CN108508414A (en) * 2018-03-07 2018-09-07 北京理工大学 A kind of secondary lobe suppressing method of the radar pulse compression output based on windowing process
CN110208757A (en) * 2019-06-05 2019-09-06 西安电子科技大学 A kind of robust adaptive beamforming method and device inhibiting major lobe suppression

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2008241319A (en) * 2007-03-26 2008-10-09 Mitsubishi Electric Corp Pulse radar system
CN101464513A (en) * 2007-12-21 2009-06-24 中国电子科技集团公司第五十研究所 Non-continuous spectrum high-frequency radar range sidelobe suppression apparatus and method thereof
CN102497340A (en) * 2011-12-31 2012-06-13 中国电子科技集团公司第四十一研究所 Time frequency door processing method based on superposition window suppression algorithm
CN103117977A (en) * 2013-01-18 2013-05-22 中南民族大学 Orthogonal frequency division multiplexing (OFDM) cognitive radio system and non-orthogonal active sidelobe suppression method
CN105699947A (en) * 2016-01-25 2016-06-22 电子科技大学 SAR image sidelobe inhibition method
CN108508414A (en) * 2018-03-07 2018-09-07 北京理工大学 A kind of secondary lobe suppressing method of the radar pulse compression output based on windowing process
CN110208757A (en) * 2019-06-05 2019-09-06 西安电子科技大学 A kind of robust adaptive beamforming method and device inhibiting major lobe suppression

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
MIMO雷达中的谱修正旁瓣抑制技术研究;李锐洋 等;《信号处理》;20160430;第30卷(第6期);第642-646页 *
Source Localization and Sensing: A Nonparametric Iterative Adaptive Approach Based on Weighted Least Squares;TARIK YARDIBI等;《IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS》;20100131;第46卷(第1期);第425-437页 *
面向雷达信号处理的脉冲压缩及其旁瓣抑制技术的研究与实现;黄莹;《中国优秀硕士学位论文全文数据库 信息科技辑》;20190215(第2期);正文第1-67页 *

Also Published As

Publication number Publication date
CN111060879A (en) 2020-04-24

Similar Documents

Publication Publication Date Title
CN111060879B (en) Joint side lobe suppression method based on two-dimensional matched filtering result
CN110865344B (en) Rapid side lobe suppression method under pulse Doppler radar system
CN111142105B (en) ISAR imaging method for complex moving target
CN110275166B (en) ADMM-based rapid sparse aperture ISAR self-focusing and imaging method
CN108107430B (en) Ship target ISAR imaging method based on fractional Fourier transform
CN106772253B (en) Radar clutter suppression method under non-uniform clutter environment
CN109669182B (en) Passive bistatic SAR moving/static target joint sparse imaging method
CN110208796B (en) Scanning radar super-resolution imaging method based on singular value inverse filtering
CN111624574A (en) Target detection method, system, storage medium and device for weak target detection
CN109613532B (en) Airborne radar real-time Doppler beam sharpening super-resolution imaging method
CN110146881B (en) Scanning radar super-resolution imaging method based on improved total variation
CN113589287B (en) Synthetic aperture radar sparse imaging method and device, electronic equipment and storage medium
CN112444810B (en) Radar air multi-target super-resolution method
CN111856465A (en) Forward-looking sea surface target angle super-resolution method based on sparse constraint
CN110865345B (en) Rapid self-adaptive pulse compression method
CN110109098B (en) Scanning radar rapid super-resolution imaging method
CN110286375B (en) LS high-order fast motion compensation method and system for near real-time ISAR imaging
CN110850421A (en) Underwater target detection method based on space-time adaptive processing of reverberation symmetric spectrum
CN114780911B (en) Ocean wide swath distance defuzzification method based on deep learning
CN116540196A (en) Reinforced clutter suppression method based on distance compensation and low-rank sparse decomposition
CN116559905A (en) Undistorted three-dimensional image reconstruction method for moving target of bistatic SAR sea surface ship
CN113009464B (en) Robust adaptive pulse compression method based on linear constraint minimum variance criterion
CN113156392B (en) Clutter suppression method based on pitching domain self-adaptive processing
CN110109097B (en) Scanning radar foresight imaging azimuth super-resolution method
CN115097428A (en) NMF active sonar anti-reverberation method based on sparse representation

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