CN106249241B - A kind of self-adapting clutter power statistic algorithm - Google Patents
A kind of self-adapting clutter power statistic algorithm Download PDFInfo
- Publication number
- CN106249241B CN106249241B CN201610640824.4A CN201610640824A CN106249241B CN 106249241 B CN106249241 B CN 106249241B CN 201610640824 A CN201610640824 A CN 201610640824A CN 106249241 B CN106249241 B CN 106249241B
- Authority
- CN
- China
- Prior art keywords
- data
- clutter
- unit
- azran
- clutter power
- 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
Links
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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/95—Radar or analogous systems specially adapted for specific applications for meteorological use
-
- 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
-
- 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 discloses a kind of self-adapting clutter power statistic algorithms, include the following steps：S1：Pass through radar original video signal input data, generate the two-dimensional clutter power statistical data of azran, the clutter power statistical data generated is iterated update between scanning with scanning room respectively, the data that can describe sea clutter and sexual intercourse clutter power statistics for ultimately generating gradualization, to establish the self-adapting clutter power statistic figure based on radar video signal；S2：Storage is managed and compressed to the obtained statistical charts of step S1.The object of the invention is to solve to count the clutter power under the complex scenes such as the sea clutter occurred in extra large radar video detection process and sexual intercourse meteorological clutter, provides complete set effective solution scheme；And a kind of efficient data compression decompression data structure is given, solve the problems, such as that memory overhead is big when the storage and use of a large amount of real-time datas such as storage and use clutter power statistical chart, the whole frame data of scanning room.
Description
Technical field
The present invention relates to radar video signal treatment technologies, more particularly to a kind of self-adapting clutter power statistic algorithm.
Background technology
Radar clutter is defined as the echo-signal for the various objects reflection for being not intended to detection that radar receives.These are not required to
The work of echo-signal " upset " radar wanted, makes the detection to interesting target echo become difficult.Radar clutter includes
From land, weather (especially rain), ocean, insect and flock of birds echo.Sea clutter refer in addition to interested target it
Outside, the radar return from sea.Clutter is the intrinsic environment that radar carries out target detection, accurate and efficient under clutter background
Detection target in ground is the basic task of Radar Signal Processing.Target detection technique under sea clutter background is in science and technology, military affairs
And civil field is an important issue always project and research direction.
Sea clutter drastically influences performance of the radar to sea detection.For a long time, the research of radar sea clutter characteristic by
To great attention, it is considered as one of the key technology of the detection of radar sea-surface target, tracking, identification.And it is miscellaneous accurately to estimate to go to sea
Wave, sexual intercourse clutter power can give subsequently to clutter recognition provide foundation.
In practical engineering project application, sea clutter and sexual intercourse clutter bring very greatly the discovery of target, tenacious tracking
Interference.Target detection and tenacious tracking ability need of the raising in clutter are stronger.It is directed to clutter in target tracking domain
Devise the effective association algorithm such as a variety of associations and means.And for the current detection of a large amount of False Intersection Points marks generated in clutter
Means do not solve the problems, such as that false-alarm is more well.False alarm rate is reduced while improving Methods for Target Detection Probability in clutter to radar system
The performance indicator promotion of system is of great importance.And existing various CFAR (constant false alarm) detector assumes that clutter (or noise) is carried on the back
Scape obeys certain statistical distribution pattern.And practical clutter amplitude once deviates from the statistical distribution pattern of hypothesis, CFAR detectors
Detection performance necessarily will receive influence in addition its constant false alarm characteristic may also be difficult to ensure.Therefore adaptive polo placement clutter work(
Rate has important practical significance.
Invention content
Goal of the invention：The object of the present invention is to provide a kind of self-adapting clutters that can solve defect of the existing technology
Power statistic algorithm.
Technical solution：Self-adapting clutter power statistic algorithm of the present invention, includes the following steps：
S1：By radar original video signal input data, the two-dimensional clutter power statistical data of azran is generated, it is raw
At the clutter power statistical data be iterated update with scanning room between scanning respectively, ultimately generate can retouching for gradualization
The data for stating sea clutter and sexual intercourse clutter power statistics, to establish the self-adapting clutter power statistic based on radar video signal
Figure；
S2：The obtained statistical charts of step S1 are managed and are stored.
Further, the process for generating the two-dimensional clutter power statistical data of azran uses sort method,
It randomly selects N number of sample to be ranked up, takes the clutter power estimated value for being ordered as the value of k as current region.
Further, the k meets：N/2<k<N.
Further, k values when k values when target is few in investigative range are more than target are big.
Further, during the two-dimensional clutter power statistical data of generation azran, by radar detection area
Multiple azran units are divided into, the method calculated the clutter power amplitude in orientation range cell is：Acquisition with
The data of L × L neighbouring azran unit of azran unit to be calculated, each azran unit is by apart from upper
N orientation quantifying units of the m in quantifying unit and orientation constitute, each neighbouring azran unit is from its m
The statistical sample of the value azran unit neighbouring as this of a position is randomly selected in × n unit, and to L × L
Neighbouring azran cell data is ranked up, and the corresponding azran unit of the maximum statistical sample of range value is target institute
In unit, clutter average amplitude value is the system for the azran unit that K is ordered as in L × L neighbouring azran units
Meter sample magnitude, 0<K<1, K values are adjusted according to radar application scene.
Further, it is described be iterated it is newer during use interframe iterative algorithm, include the following steps：
S1.1：To present frameIt is counted：Radar detection area is divided into multiple azran units, each orientation
Range cell is constituted by m apart from quantifying unit and n orientation quantifying unit；Wherein, it is apart from the dividing mode of quantifying unit：
By in investigative range range averaging or it is non-be divided equally into M unit, the dividing mode of orientation quantifying unit is：By 360 degree
Averagely it is divided into N number of orientation quantifying unit；A final whole circle radar data has been divided into M × N number of azran unit；Then
Determine search range；
S1.2：It is iterated using formula (1)：
In formula (1),For the mean power of clutter and noise in kth frame data at i-th of azran unit,For the average power content of clutter and noise at ith sample unit in -1 data of kth,For i-th of kth frame data
Sampling unit is in the clutter of present frame and the mean power of noise, and a is Forgetting coefficient.
Further, in the step S2, storage is carried out using compression storage mode to statistical chart, compression and storage method is：
Data deposit input block will be scanned to scan in queue, scanned in queue caching after data reach l when input and be sent into data pressure
Contracting module, in data compressing module using LZ4 data compression algorithms to data carry out lossless compression, compressed data according to
The structure of annular data compression blocks queue is put into memory address.
Further, the method decompressed to data is：It is scanned in queue in output block first and searches data to be decompressed,
Directly exported if finding data to be decompressed, otherwise if search data pair to be decompressed from annular data compression blocks queue
The call number answered, and a compressed data block is decompressed, it is put into output block and scans queue and read data.
Advantageous effect：Compared with prior art, the present invention has following advantageous effect：
(1) for miscellaneous under the complex scenes such as the sea clutter occurred in extra large radar video detection process and sexual intercourse meteorological clutter
Wave power counts, and provides complete set effective solution scheme；
(2) a kind of efficient data compression decompression data structure is given, storage is solved and is counted with using clutter power
Memory overhead big problem when the storage and use of a large amount of real-time datas such as figure, the whole frame data of scanning room.
(3) input that self-adapting clutter power statistic arithmetic result is accumulated as combined type scanning room iteration makes with reference to thresholding
With, avoid single thresholding cause calculate scanning room iterative product for a long time cause target lose or cause false-alarm excessive, realize sea it is miscellaneous
The automation of wave and sexual intercourse clutter recognition.
Description of the drawings
Fig. 1 is schematic diagram of the inventive algorithm the location of in sea clutter and sexual intercourse Clutter suppression algorithm；
Fig. 2 is the division methods schematic diagram apart from quantifying unit of the present invention；
Fig. 3 is the division methods schematic diagram of the orientation quantifying unit of the present invention；
Fig. 4 is the process schematic of the acquisition statistical sample of the present invention；
Fig. 5 is the process schematic of the present invention being ranked up to orientation range cell；
Fig. 6 is the process schematic of the data compression and decompression of the present invention.
Specific implementation mode
The invention discloses a kind of self-adapting clutter power statistic algorithms, include the following steps：
S1：By radar original video signal input data, the two-dimensional clutter power statistical data of azran is generated, it is raw
At the clutter power statistical data be iterated update with scanning room between scanning respectively, ultimately generate can retouching for gradualization
The data for stating sea clutter and sexual intercourse clutter power statistics, to establish the self-adapting clutter power statistic based on radar video signal
Figure；
S2：The obtained statistical charts of step S1 are managed and are stored.
The present invention uses the methods of azran two-dimensional sampling and sequence when carrying out statistics and calculating and improves computational efficiency,
Interference effect of the target to the two-dimentional clutter map of generation is removed simultaneously.The thought that clutter and noise power are counted using ranking method is ginseng
The algorithm based on ordered statistics in OS-CFAR is examined, OS-CFAR is to be ordered as k in the N number of sample selection being detected near unit
Estimation of the value as interference power level.
The process that the two-dimensional clutter power statistical data of azran is generated in step S1 uses sort method, is small
N number of sample is randomly selected in region to be ranked up, and takes the clutter power estimated value for being ordered as the value of k as current region.Therefore,
When jamming target number is less than (N-k) in statistical sample, target echo can be effectively prevent to be counted, and work as target sample number
When more than (N-k), the influence for the target that as a result will be interfered.Through experiment, k<Power statistic is relatively low when N/2, can cause to count
Calculate error, therefore the value general satisfaction N/2 of k<k<The condition of N.When target is less in investigative range k values can value it is bigger than normal
A bit, k values value wants less than normal when target comparatively dense.Its process is as shown in Figure 5.Self-adapting clutter power statistic is in sea clutter and cloud
Rain clutter inhibits location in processing procedure as shown in Figure 1.
During generating the two-dimensional clutter power statistical data of azran, radar detection area is divided into multiple sides
Position range cell, the method calculated the clutter power amplitude in orientation range cell are：As shown in figure 4, acquiring and waiting for
The data of the L × L neighbouring azran unit of azran unit of calculating, each neighbouring azran unit be by away from
The n orientation quantifying unit in quantifying unit and orientation with a distance from upper m is constituted, each neighbouring azran unit from
The statistical sample of the value azran unit neighbouring as this of a position is randomly selected in its m × n unit, and to L × L
A neighbouring azran cell data is ranked up, and the corresponding azran unit of the maximum statistical sample of range value is target
Place unit, clutter average amplitude value are that the azran unit of K is ordered as in L × L neighbouring azran units
Statistical sample amplitude, 0<K<1, K values are adjusted according to radar application scene.
Be iterated it is newer during use interframe iterative algorithm, include the following steps：
S1.1：To present frameIt is counted：Radar detection area is divided into multiple azran units, azran
Unit is constituted by m apart from quantifying unit and n orientation quantifying unit；Wherein, apart from the dividing mode of quantifying unit such as Fig. 2 institutes
Show, is：By in investigative range range averaging or non-be divided equally into M unit, dividing mode such as Fig. 3 of orientation quantifying unit
It is shown, be：It is averagely divided into N number of orientation quantifying unit by 360 degree；Then search range is determined：With target during tracking
Centered on position, search radius r is related with the radar antenna period, and the period, more long corresponding search radius was bigger, generally set
It sets between 1~5Km.
S1.2：It is iterated using formula (1)：
In formula (1),For the mean power of clutter and noise in kth frame data at i-th of azran unit,
For the average power content of clutter and noise at ith sample unit in -1 data of kth,For kth frame data ith sample list
Member is in the clutter of present frame and the mean power of noise, and a is Forgetting coefficient.
Usual radar video data scan (circle) at one, and the data volume in the period is larger, and needs to store in this patent
Multiple whole circle radar video data, can cause memory space expense larger, therefore step S2 devises a kind of radar video data
Compression storage solve the problems, such as that storage overhead is larger with decompression read method, as shown in Figure 6.Storage use is carried out to statistical chart
Storage mode is compressed, compression and storage method is：Data deposit input block will be scanned to scan in queue, when queue caching is scanned in input
Middle data are sent into data compressing modules after reaching l, in data compressing module using LZ4 data compression algorithms to data into
Row lossless compression, compressed data are put into according to the structure of annular data compression blocks queue in memory address.Data are carried out
The method of decompression is：It is scanned in queue in output block first and searches data to be decompressed, it is straight if finding data to be decompressed
Output is connect, the corresponding call number of data to be decompressed otherwise then is searched from annular data compression blocks queue, and decompress a number
According to compression blocks, it is put into output block and scans queue and read data.Read data buffer storage formula design advantage be normally to read orientation/
It is that sequence is read to scan data mode, and i.e. solution extrudes l orientation/scan number after solution extrudes a certain index position compressed data
According to follow-up l-1 azimuth sweep is then not necessarily to solve from annular data compression blocks queue again.Self-adapting clutter power statistic
Notebook data storage mode can be used in data result, scanning room accumulation algorithm intermediate result, has been greatly saved opening for memory space
Pin.
Claims (5)
1. a kind of self-adapting clutter power statistic algorithm, it is characterised in that：Include the following steps：
S1：By radar original video signal input data, the two-dimensional clutter power statistical data of azran is generated, generation
The clutter power statistical data is iterated update between scanning with scanning room respectively, and ultimately generate gradualization can describe sea
The data of clutter and sexual intercourse clutter power statistics, to establish the self-adapting clutter power statistic figure based on radar video signal；
The process for generating the two-dimensional clutter power statistical data of azran uses sort method, namely randomly selects N
A sample is ranked up, and takes the clutter power estimated value for being ordered as the value of k as current region；
During the two-dimensional clutter power statistical data of generation azran, radar detection area is divided into multiple sides
Position range cell, the method calculated the clutter power amplitude in orientation range cell are：Acquisition and orientation to be calculated
The data of L × L neighbouring azran unit of range cell, each azran unit are by apart from m upper distance measurements
The n orientation quantifying unit changed on unit and orientation is constituted, and each neighbouring azran unit is from its m × n unit
Randomly select the statistical sample of the value azran unit neighbouring as this of a position, and to L × L neighbouring orientation away from
It is ranked up from cell data, the corresponding azran unit of the maximum statistical sample of range value is unit where target, clutter
Average amplitude value is the statistical sample amplitude for the azran unit that K is ordered as in the neighbouring azran units of L × L, 0
<K<1, K values are adjusted according to radar application scene；
It is described be iterated it is newer during use interframe iterative algorithm, include the following steps：
S1.1：To present frameIt is counted：Radar detection area is divided into multiple azran units, each azran
Unit is constituted by m apart from quantifying unit and n orientation quantifying unit；Wherein, it is apart from the dividing mode of quantifying unit：It will visit
Survey range in range averaging or it is non-be divided equally into M unit, the dividing mode of orientation quantifying unit is：It is average by 360 degree
It is divided into N number of orientation quantifying unit；A final whole circle radar data has been divided into M × N number of azran unit；Then it determines
Search range；
S1.2：It is iterated using formula (1)：
In formula (1),For the mean power of clutter and noise in kth frame data at i-th of azran unit,For
The average power content of clutter and noise in -1 data of kth at ith sample unit,For kth frame data ith sample unit
It is in the clutter of present frame and the mean power of noise, a is Forgetting coefficient；
S2：The obtained statistical charts of step S1 are managed and are stored.
2. self-adapting clutter power statistic algorithm according to claim 1, it is characterised in that：The k meets：N/2<k<N.
3. self-adapting clutter power statistic algorithm according to claim 1, it is characterised in that：When target is few in investigative range
K values more than target when k values it is big.
4. self-adapting clutter power statistic algorithm according to claim 1, it is characterised in that：In the step S2, to system
Meter figure carries out storage using compression storage mode, and compression and storage method is：Data deposit input block will be scanned to scan in queue, when
Input scans data in queue caching and is sent into data compressing module after reaching l, and LZ4 data are used in data compressing module
Compression algorithm to data carry out lossless compression, compressed data according to annular data compression blocks queue structure with being put into memory
In location.
5. self-adapting clutter power statistic algorithm according to claim 4, it is characterised in that：The side that data are decompressed
Method is：It is scanned in queue in output block first and searches data to be decompressed, directly exported if finding data to be decompressed, it is no
The corresponding call number of data to be decompressed then is searched from annular data compression blocks queue, and decompresses a compressed data block,
Output block is put into scan queue and read data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610640824.4A CN106249241B (en) | 2016-08-05 | 2016-08-05 | A kind of self-adapting clutter power statistic algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610640824.4A CN106249241B (en) | 2016-08-05 | 2016-08-05 | A kind of self-adapting clutter power statistic algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106249241A CN106249241A (en) | 2016-12-21 |
| CN106249241B true CN106249241B (en) | 2018-08-24 |
Family
ID=58078973
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610640824.4A Active CN106249241B (en) | 2016-08-05 | 2016-08-05 | A kind of self-adapting clutter power statistic algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106249241B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110161473B (en) * | 2019-05-08 | 2021-04-30 | 南京莱斯电子设备有限公司 | Clutter map detection and updating method based on primitive clutter map and map layer |
| CN110426691A (en) * | 2019-07-02 | 2019-11-08 | 中国航空工业集团公司雷华电子技术研究所 | A kind of CFAR detection method under rain clutter environment |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101452074B (en) * | 2007-12-07 | 2011-06-08 | 南京理工大学 | Strong clutter self-adapting cancellation apparatus for analog-digital combined treatment |
| US8330650B2 (en) * | 2010-05-07 | 2012-12-11 | The United States Of America, As Represented By The Secretary Of The Army | Radar system and antenna with delay lines and method thereof |
| CN102540162B (en) * | 2011-12-12 | 2015-08-19 | 中国船舶重工集团公司第七二四研究所 | Based on the low-altitude electromagnetic wave propagation characteristic method of estimation of sea clutter |
| CN104849707B (en) * | 2015-04-27 | 2017-03-22 | 中国电子科技集团公司第二十八研究所 | Sea clutter suppression method based on multi-radar distributed detection |
| CN105044686B (en) * | 2015-08-03 | 2017-05-10 | 中国电子科技集团公司第二十八研究所 | Radar dense false target interference inhibition method |
-
2016
- 2016-08-05 CN CN201610640824.4A patent/CN106249241B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN106249241A (en) | 2016-12-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107861107B (en) | Double-threshold CFAR (computational fluid dynamics) and trace point agglomeration method suitable for continuous wave radar | |
| CN106249211B (en) | A kind of sea clutter and sexual intercourse Clutter suppression algorithm | |
| US8405540B2 (en) | Method for detecting small targets in radar images using needle based hypotheses verification | |
| US9075143B2 (en) | Sparse array RF imaging for surveillance applications | |
| CN109471072B (en) | FPGA-based monopulse radar two-dimensional CFAR detection method and system | |
| CN111624567B (en) | Constant false alarm detection method and device | |
| CN110297218B (en) | Method for detecting unknown modulation mode of radar signal based on generation countermeasure network | |
| EP1782336A1 (en) | Method of detecting a target | |
| CN107561509B (en) | Airborne millimeter wave radar power line detection method | |
| CN110599800A (en) | Parking lot parking space state monitoring system and monitoring method | |
| CN106249241B (en) | A kind of self-adapting clutter power statistic algorithm | |
| CN115311531A (en) | Ground penetrating radar underground cavity target automatic detection method of RefineDet network model | |
| CN104881670B (en) | A kind of fast target extracting method for SAR orientation angular estimation | |
| Zhu et al. | A rain-type adaptive optical flow method and its application in tropical cyclone rainfall nowcasting | |
| CN111175709B (en) | Error suppression-based large-range meteorological radar-oriented jigsaw method | |
| CN112327265A (en) | Division and treatment detection method based on semantic segmentation network | |
| CN112649791B (en) | Radar echo processing method and device | |
| CN115311532A (en) | Ground penetrating radar underground cavity target automatic identification method based on ResNet network model | |
| CN110632592B (en) | False alarm eliminating method for handheld through-wall radar | |
| CN110211124B (en) | Infrared imaging frozen lake detection method based on MobileNet V2 | |
| CN113534084A (en) | Radar false track suppression method based on correlation topological graph | |
| CN114066815A (en) | SAR image change detection method based on fuzzy c-means clustering | |
| CN112666528A (en) | Multi-station radar system interference identification method based on convolutional neural network | |
| CN111948613A (en) | Ship-borne ground wave radar target detection method based on self-adaptive background area selection | |
| CN116224280B (en) | Radar target detection method, radar target detection device, radar equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |