CN108387879B - Clutter map unit median detection method based on adaptive normalized matched filtering - Google Patents

Clutter map unit median detection method based on adaptive normalized matched filtering Download PDF

Info

Publication number
CN108387879B
CN108387879B CN201810033473.XA CN201810033473A CN108387879B CN 108387879 B CN108387879 B CN 108387879B CN 201810033473 A CN201810033473 A CN 201810033473A CN 108387879 B CN108387879 B CN 108387879B
Authority
CN
China
Prior art keywords
unit
data
resolution
clutter
clutter map
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
CN201810033473.XA
Other languages
Chinese (zh)
Other versions
CN108387879A (en
Inventor
许述文
黄盛杰
薛健
水鹏朗
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.)
Xidian University
Original Assignee
Xidian University
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 Xidian University filed Critical Xidian University
Priority to CN201810033473.XA priority Critical patent/CN108387879B/en
Publication of CN108387879A publication Critical patent/CN108387879A/en
Application granted granted Critical
Publication of CN108387879B publication Critical patent/CN108387879B/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
    • G01S7/41—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
    • G01S7/414—Discriminating targets with respect to background clutter

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Radar Systems Or Details Thereof (AREA)

Abstract

The invention discloses a clutter map unit median detection method based on adaptive normalized matched filtering, which mainly solves the problem of poor detection performance in the prior art, and adopts the technical scheme that: transmitting a pulse signal by using a radar transmitter, receiving echo data by using a radar receiver, wherein the echo sequence in each resolution unit of the echo data is Z; calculating a test statistic xi for Z by using a self-adaptive normalized matched filter; processing the median value of the unit by xi to obtain processed data
Figure DDA0001545805410000011
To pair
Figure DDA0001545805410000012
Carrying out clutter map updating processing to obtain a scanning estimation value
Figure DDA0001545805410000013
Estimating a clutter map threshold factor T by using a Monte Carlo experiment; calculating test statistic xi and scan estimation value
Figure DDA0001545805410000014
And comparing the ratio C with the threshold factor T to detect to obtain a detected result. The method improves the performance of target detection under the sea clutter background, improves the robustness of abnormal data resistance, and can be used for sea surface target detection under a moving or static coherent system platform.

Description

Clutter map unit median detection method based on adaptive normalized matched filtering
Technical Field
The invention belongs to the technical field of signal processing, and particularly relates to a clutter map unit median detection method which can be used for target detection under a moving or static coherent system platform.
Background
The target detection technology under the background of sea clutter is a crucial research direction in radar application technology, and has been widely applied in military and civil fields. When the radar works in a sea mode, a scanning scene is complex and the range is large, and radar echoes often contain various types of clutter including sea clutter, ground clutter, island clutter, offshore clutter and the like. The echo intensity of the ground clutter and the island clutter is strong, the target detection under the background of the sea clutter is seriously influenced, and a complex clutter scene and clutter characteristics form a main obstacle for detecting the sea surface target. Because the clutter intensity is influenced by factors such as sea conditions, particularly, the sea clutter intensity in the offshore area is very violently converted in an airspace, and if a traditional airspace CFAR processing method is adopted, only a few reference units can be adopted, so that the false alarm rate loss is very large, and the false alarm rate is not easy to keep constant. In general, although the clutter varies greatly in distance and direction, the clutter intensity of the same range unit varies slowly with time, so that a time domain constant false alarm processing method, namely a clutter map CFAR method, can be adopted.
The document "Nitzberg R. Clutter map CFAR analysis [ J ]. IEEE Transactions on Aerospace and Electronic systems,1986(4): 419-. The method divides the radar space into clutter map units to work, and forms the intensity estimation of clutter background at the detection unit according to the multiple scanning values of the detection unit. The clutter map stores the magnitude of the clutter intensity for each azimuth-range unit, each value being updated on new and previous iterations of scan measurements, and it is used as an estimate of the intensity of the current clutter background. The method proposed by Nitzberg only utilizes the time domain information of the resolution unit, and the document "Shenfumin, Liutowns. clutter map CFAR plane detection technology [ J ]. systematic engineering and electronic technology, 1996,18 (7): 9-14 "propose a clutter map unit average CFAR plane technique suitable for processing sea wave clutter, which firstly carries out unit average on each resolution unit amplitude in the clutter map unit and then carries out iterative processing. The method combines spatial processing and time domain processing, utilizes information of a near resolution unit, and improves detection performance to a certain extent compared with the method proposed by the Nitzberg.
Disclosure of Invention
The invention aims to provide a clutter map unit median detection method based on adaptive normalized matched filtering to improve the abnormal data resistance and the target detection performance aiming at the defects of the prior art.
In order to achieve the technical purpose, the technical scheme of the invention comprises the following steps:
(1) transmitting a pulse signal by using a radar transmitter, receiving echo data formed by sea surface scattering by using a radar receiver, wherein the echo data is a four-dimensional matrix comprising scanning turns, a pulse dimension, a distance dimension and a wave position dimension, a minimum processing unit of each azimuth-distance two-dimensional plane is called a resolution unit, and an echo sequence in each resolution unit is Z:
Z＝[z1,z2,...,zi,...,zN]，
wherein z isiRepresents the ith echo data, and N represents the pulse number;
(2) the echo sequence Z in each resolution unit is subjected to self-adaptive normalized matched filtering processing to obtain the test statistic xi of the echo sequence in each resolution unitn(k)：
Figure BDA0001545805390000021
Where p denotes the normalized steering vector, ξn(k) Denotes the test statistic for the kth resolution element of the nth scan, n ═ 1, 2.., L denotes the number of scan cycles, (·)HRepresenting a conjugate transpose operation, (.)-1It is shown that the inversion operation on the matrix,
Figure BDA0001545805390000022
represents the covariance matrix:
Figure BDA0001545805390000023
wherein M represents the number of reference cells, zmRepresenting reference unit echo data;
(3) test statistic xi for echo sequence in all resolution cellsn(k) Performing unit median processing on an azimuth-distance two-dimensional plane to obtain data processed by the kth resolution unit of the nth scanning
Figure BDA0001545805390000024
From
Figure BDA0001545805390000025
The data processed by the kth resolution unit of the (n-1) th scanning is taken out
Figure BDA0001545805390000026
(4) Data processed by the kth resolution unit of the (n-1) th scanning
Figure BDA0001545805390000027
Updating the clutter map to obtain the estimated value of the kth resolution unit in the nth scanning
Figure BDA0001545805390000028
(5) For test statistic xin(k) Carrying out self-adaptive detection:
(5a) estimating a clutter map threshold factor T by using a Monte Carlo experiment according to the format of the data to be detected and different false alarm rate requirements;
(5b) calculating test statistic xin(k) Estimated value of the nth scanning
Figure BDA0001545805390000029
And comparing C with a threshold factor T:
if C is larger than or equal to T, the detection unit is considered to contain the target, namely the corresponding unit in the detection result is set to be 1;
if C is less than T, the detection unit is determined to have no target, namely, the corresponding unit in the detection result is set to 0.
Compared with the prior art, the invention has the following advantages:
1) according to the invention, the echo data is processed by using the adaptive normalized matched filtering, so that the detection performance is improved under the background of K distributed clutter;
2) the invention utilizes the unit median method to process the data in the detection window, thereby improving the robustness of the abnormal data resistance.
Drawings
FIG. 1 is a flow chart of an implementation of the present invention;
FIG. 2 is a diagram of a selection of reference cells in the estimated covariance matrix according to the present invention;
FIG. 3 is a diagram of a selection of a value processing window in a unit according to the present invention;
FIG. 4 is a comparison of detection probabilities obtained using the present invention and a prior art method.
Detailed Description
The invention will be further described with reference to the accompanying drawings in which:
referring to fig. 1, the implementation steps of the invention are as follows:
step 1, obtaining an echo sequence Z.
Transmitting a pulse signal by using a radar transmitter, receiving echo data formed by sea surface scattering by using a radar receiver, wherein the echo data is a four-dimensional matrix comprising scanning turns, a pulse dimension, a distance dimension and a wave position dimension, and a minimum processing unit of each azimuth-distance two-dimensional plane is called a resolution unit;
the echo sequence in each resolution cell is Z:
Z＝[z1,z2,...,zi,...,zN]，
wherein z isiIndicates the ith echo data and N indicates the number of pulses.
And 2, carrying out self-adaptive normalized matched filtering processing on the echo sequence.
(2a) The selected reference unit:
referring to fig. 2, the embodiment of this step is specifically as follows:
(2a1) respectively taking each echo sequence z as a detection unit, and selecting protection units on two sides of the detection unit in order to prevent a target from entering a reference unit;
(2a2) m resolution units are selected as reference units on both sides of the detection unit, wherein z is shown in FIG. 21,z2,...,zm,...,zMI.e., the selected reference cell, wherein zmRepresenting the echo sequence of the mth reference unit, wherein the value of M is 1, 2.. multidot.M;
(2b) echo sequence z from the mth reference unitmThe covariance matrix of the detection unit is calculated by adopting a normalized sampling covariance matrix NSCM
Figure BDA0001545805390000041
Figure BDA0001545805390000042
Wherein M represents the number of reference cells, zmRepresenting reference unit echo data, (.)HRepresents a conjugate transpose operation;
(2c) determining the Doppler shift f of a targetdCalculating a normalized steering vector p:
Figure BDA0001545805390000043
wherein, (.)TRepresenting a transpose operation;
(2d) the echo sequence Z in each resolution unit is subjected to self-adaptive normalized matched filtering processing to obtain the test statistic xi of the echo sequence in each resolution unitn(k)：
Figure BDA0001545805390000044
Wherein ξn(k) Represents the test statistic for the nth scan of the kth resolution element, n ═ 1, 2.., L represents the number of scan cycles, (·)-1Representing the inversion operation on the matrix.
Step 3For test statistic xin(k) And carrying out unit median processing.
(3a) Selecting a unit median processing window on an azimuth-distance two-dimensional plane:
referring to fig. 3, an embodiment of this step is: to test the statistic xin(k) Selecting X × Y resolution units around the rectangular window as unit median processing window, and processing with xi in the unit median processing windown(k) The center selects a multiplied by b resolution units as a protection window, and the other resolution units as reference units, wherein X represents the length of the window in the direction dimension, and Y represents the length of the window in the distance dimension; a represents the length of the protection window in the azimuth dimension, b represents the length of the protection window in the distance dimension; the example sets X to 7, Y to 7, a to 3, b to 3;
(3b) test statistic xi for echo sequence in all resolution cellsn(k) Unit median processing is performed on a window:
(3b1) for test statistic xin(k) Arranging the reference unit data in all the 7 multiplied by 7 square windows from small to big respectively;
(3b2) taking the median of the sorted results as the data at the center point of the 7 × 7 square window, wherein the data is the data after the median processing in the unit
Figure BDA0001545805390000045
From
Figure BDA0001545805390000046
The data processed by the kth resolution unit of the (n-1) th scanning is taken out
Figure BDA0001545805390000051
And 4, updating the clutter map.
Data processed by the kth resolution unit of the (n-1) th scanning
Figure BDA0001545805390000052
Updating the clutter map to obtain the estimated value of the kth resolution unit in the nth scanning
Figure BDA0001545805390000053
Figure BDA0001545805390000054
Wherein, omega is a forgetting factor, and the value thereof is [0,1]A constant of (d); in the engineering, omega is designed to take the detection performance and the operation speed into consideration, the value of omega is 0.125,
Figure BDA0001545805390000055
the estimate of the kth resolution element for the (n-1) th scan is shown.
And 5, carrying out self-adaptive detection processing on the echo sequence.
(5a) Estimating a clutter map threshold factor T by using a Monte Carlo experiment according to the format of data to be detected and different false alarm rate requirements:
(5a1) determining a needed false alarm rate index, and counting the length of each dimension of the data to be detected;
(5a2) generating corresponding pure clutter data according to the false alarm rate index and the format of the original data;
(5a3) processing the pure clutter data according to the steps (2) to (4) to obtain corresponding test statistic xin(k) Estimated value of the nth scanning
Figure BDA0001545805390000056
(5a4) Since the threshold factor is independent of the position of the reference cell, an initial threshold factor T is calculated based on the adaptive detection criterioniWherein i represents the ith Monte Carlo experiment and is performed by the following formula:
Figure BDA0001545805390000057
(5a5) repeating the test for multiple times independently, and obtaining the initial threshold factor T each timeiSorting in descending order, and taking (5a1) the determined corresponding false alarm rateTaking the lower threshold factor as a final threshold factor T;
(5b) calculating test statistic xin(k) Estimated value of the nth scanning
Figure BDA0001545805390000058
The ratio of (A) to (B):
Figure BDA0001545805390000059
and compares C with a threshold factor T:
if C is larger than or equal to T, the detection unit is considered to contain the target, namely the corresponding unit in the detection result is set to be 1;
if C is less than T, the detection unit is determined to have no target, namely, the corresponding unit in the detection result is set to 0.
And completing clutter map unit median target detection based on adaptive normalized matched filtering based on the steps 1 to 5.
The effect of the present invention will be further explained with the simulation experiment.
1. Simulation parameters
The data used in the simulation experiment is data of a certain motion platform.
2. Content of simulation experiment
Simulation experiment, for a certain motion platform data, the method of the invention and the clutter map detection method based on unit averaging are respectively adopted to carry out target detection, the detection result is shown in fig. 4, the horizontal axis in fig. 4 represents the signal-to-clutter ratio, the vertical axis represents the detection probability, and the solid line in fig. 4 represents the sea surface target detection result obtained by adopting the invention; the dotted line in fig. 4 represents the detection result obtained by the MTD cascade unit average clutter map detection method;
as can be seen from FIG. 4, the detection result obtained by the method of the present invention is significantly better than the detection result obtained by the prior art.
In summary, the clutter map unit median target detection method based on adaptive normalized matched filtering provided by the invention improves the sea surface target detection performance under the condition of a moving or static coherent system platform, and improves the abnormal data resistance performance.

Claims (4)

1. A clutter map unit median detection method based on adaptive normalized matched filtering is characterized by comprising the following steps:
(1) transmitting a pulse signal by using a radar transmitter, receiving echo data formed by sea surface scattering by using a radar receiver, wherein the echo data is a four-dimensional matrix comprising scanning turns, a pulse dimension, a distance dimension and a wave position dimension, a minimum processing unit of each azimuth-distance two-dimensional plane is called a resolution unit, and an echo sequence in each resolution unit is Z:
Z＝[z1,z2,...,zi,...,zN]，
wherein z isiRepresents the ith echo data, and N represents the pulse number;
(2) the echo sequence Z in each resolution unit is subjected to self-adaptive normalized matched filtering processing to obtain the test statistic xi of the echo sequence in each resolution unitn(k)：
Figure FDA0002971149250000011
Where p denotes the normalized steering vector, ξn(k) Denotes the test statistic for the kth resolution element of the nth scan, n ═ 1, 2.., L denotes the number of scan cycles, (·)HRepresenting a conjugate transpose operation, (.)-1It is shown that the inversion operation on the matrix,
Figure FDA0002971149250000012
represents the covariance matrix:
Figure FDA0002971149250000013
wherein M represents the number of reference cells, zmRepresenting reference unit echo data;
(3) test statistic xi for echo sequence in all resolution cellsn(k) In azimuth-distance two-dimensional planePerforming unit median processing on the surface to obtain data processed by the kth resolution unit of the nth scanning
Figure FDA0002971149250000014
From
Figure FDA0002971149250000015
The data processed by the kth resolution unit of the (n-1) th scanning is taken out
Figure FDA0002971149250000016
(4) Data processed by the kth resolution unit of the (n-1) th scanning
Figure FDA0002971149250000017
Updating the clutter map to obtain the estimated value of the kth resolution unit in the nth scanning
Figure FDA0002971149250000018
(5) For test statistic xin(k) Carrying out self-adaptive detection:
(5a) estimating a clutter map threshold factor T by using a Monte Carlo experiment according to the format of the data to be detected and different false alarm rate requirements;
(5b) calculating test statistic xin(k) Estimated value of the nth scanning
Figure FDA0002971149250000021
And comparing C with a threshold factor T:
if C is larger than or equal to T, the distinguishing unit is considered to contain the target, namely the corresponding unit in the detection result is set to be 1;
if C is less than T, the distinguishing unit is determined to have no target, namely the corresponding unit in the detection result is set to 0.
2. The method of claim 1, wherein the examination of the echo sequence in all resolution cells in step (3) is performedStatistic xin(k) Performing unit median processing on an azimuth-distance two-dimensional plane, and performing the following steps:
(3a) setting a window of unit median processing as a 7 multiplied by 7 square window, taking a3 multiplied by 3 protection window at the center point of the square window as a protection unit, and taking the rest units as reference units;
(3b) for test statistic xin(k) All the reference unit data in the 7 multiplied by 7 square window are arranged from small to big respectively;
(3c) taking the median of the sorting result as the data at the center point of the 7 × 7 square window, wherein the data is the data after the median processing in the unit
Figure FDA0002971149250000022
3. The method of claim 1, wherein the data processed at the kth resolution cell for the (n-1) th scan in step (4)
Figure FDA0002971149250000023
Updating the clutter map by the following formula:
Figure FDA0002971149250000024
wherein, omega is a forgetting factor, and the value thereof is [0,1]A constant of (d);
Figure FDA0002971149250000025
the estimate of the kth resolution element for the (n-1) th scan is shown.
4. The method of claim 1, wherein the step (5) of estimating the clutter map threshold factor T using a monte carlo experiment is performed according to the format of the data to be detected and different false alarm rate requirements, and is performed by the steps of:
(5a) generating corresponding pure clutter data according to the false alarm rate requirement and the format of the data to be detected;
(5b) processing the pure clutter data according to the steps (2) to (4) to obtain corresponding test statistic xin(k) Estimated value of the nth scanning
Figure FDA0002971149250000026
(5c) Since the threshold factor is independent of the reference cell position, the initial threshold factor T is obtained according to the adaptive detection criterioni：
Figure FDA0002971149250000027
(5d) Independently repeating the test for multiple times to obtain an initial threshold factor TiAnd performing descending arrangement, and taking the threshold factor under the corresponding false alarm rate as the final threshold factor T.
CN201810033473.XA 2018-01-12 2018-01-12 Clutter map unit median detection method based on adaptive normalized matched filtering Active CN108387879B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810033473.XA CN108387879B (en) 2018-01-12 2018-01-12 Clutter map unit median detection method based on adaptive normalized matched filtering

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201810033473.XA CN108387879B (en) 2018-01-12 2018-01-12 Clutter map unit median detection method based on adaptive normalized matched filtering

Publications (2)

Publication Number Publication Date
CN108387879A CN108387879A (en) 2018-08-10
CN108387879B true CN108387879B (en) 2021-05-18

Family

ID=63076305

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201810033473.XA Active CN108387879B (en) 2018-01-12 2018-01-12 Clutter map unit median detection method based on adaptive normalized matched filtering

Country Status (1)

Country Link
CN (1) CN108387879B (en)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109856608B (en) * 2018-11-28 2021-01-29 重庆秦嵩科技有限公司 Radar target high confidence rate detection method based on recursive clutter map
CN110412536B (en) * 2019-08-15 2022-06-24 南京信息工程大学 Target detection method based on variable-scale median ratio

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103954951A (en) * 2014-05-13 2014-07-30 西安电子科技大学 Power mid value and normalization covariance estimation based sea-surface target detection method
CN104076350A (en) * 2014-07-08 2014-10-01 西安电子科技大学 Method for detecting floating targets on sea based on normalized Doppler spectrum
CN104569948A (en) * 2015-01-21 2015-04-29 西安电子科技大学 Sub-band self-adaptive GLRT-LTD detection method under sea clutter background
CN104569949A (en) * 2015-01-27 2015-04-29 西安电子科技大学 Radar target detection method based on combined adaptive normalized matched filter

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103954951A (en) * 2014-05-13 2014-07-30 西安电子科技大学 Power mid value and normalization covariance estimation based sea-surface target detection method
CN104076350A (en) * 2014-07-08 2014-10-01 西安电子科技大学 Method for detecting floating targets on sea based on normalized Doppler spectrum
CN104569948A (en) * 2015-01-21 2015-04-29 西安电子科技大学 Sub-band self-adaptive GLRT-LTD detection method under sea clutter background
CN104569949A (en) * 2015-01-27 2015-04-29 西安电子科技大学 Radar target detection method based on combined adaptive normalized matched filter

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
基于功率中值和归一化采样协方差矩阵的自适应匹配滤波检测器;刘明 等;《电子与信息学报》;20150630;第37卷(第6期);第1395~1401页 *

Also Published As

Publication number Publication date
CN108387879A (en) 2018-08-10

Similar Documents

Publication Publication Date Title
Kirubarajan et al. Probabilistic data association techniques for target tracking in clutter
CN107861123B (en) Method for real-time tracking of multiple moving targets by through-wall radar in complex environment
CN106772253B (en) Radar clutter suppression method under non-uniform clutter environment
CN107942324B (en) Multi-frame combined small target dual detection method based on Doppler guidance
CN112731307B (en) RATM-CFAR detector based on distance-angle joint estimation and detection method
CN108196238B (en) Clutter map detection method based on adaptive matched filtering under Gaussian background
CN114609626A (en) Multi-target detection method for vehicle-mounted millimeter wave radar
CN108872961B (en) Radar weak target detection method based on low threshold
CN108387879B (en) Clutter map unit median detection method based on adaptive normalized matched filtering
CN107229040B (en) high-frequency radar target detection method based on sparse recovery space-time spectrum estimation
CN106772275A (en) A kind of low-rank matrix based on Alpha Stable distritations recovers detection method
Xue et al. Knowledge-based target detection in compound Gaussian clutter with inverse Gaussian texture
CN115657005A (en) Unmanned aerial vehicle identification method based on Doppler radar multi-information fusion
CN112255608A (en) Radar clutter self-adaptive suppression method based on orthogonal projection
CN107976663A (en) It is a kind of based on the external illuminators-based radar of subspace projection to targeted compression detection method
CN105652256B (en) A kind of high-frequency ground wave radar TBD methods based on polarization information
CN111090088B (en) Leakage target space-time detection method based on active sonar array skew symmetry characteristics
CN114325599B (en) Automatic threshold detection method for different environments
Turley Signal processing techniques for maritime surveillance with skywave radar
Ge et al. Multi-radar hybrid detection algorithm based on information entropy
CN108983227B (en) Polarization MIMO radar detection method based on whitening filtering
Gao et al. An improved TBD algorithm based on dynamic programming for dim SAR target detection
CN111948613A (en) Ship-borne ground wave radar target detection method based on self-adaptive background area selection
CN112835006A (en) Method and system for tracking radar small-target detection on sea based on interframe accumulation
CN113702937B (en) Dual-channel incoherent detection method based on self-adaptive MTI filter

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