CN106249241A - A kind of self-adapting clutter power statistic algorithm - Google Patents
A kind of self-adapting clutter power statistic algorithm Download PDFInfo
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- CN106249241A CN106249241A CN201610640824.4A CN201610640824A CN106249241A CN 106249241 A CN106249241 A CN 106249241A CN 201610640824 A CN201610640824 A CN 201610640824A CN 106249241 A CN106249241 A CN 106249241A
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- 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
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- 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
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- 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
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Abstract
The invention discloses a kind of self-adapting clutter power statistic algorithm, comprise the following steps: S1: input data by radar original video signal, generate the clutter power statistical data of azran two dimension, this clutter power statistical data generated is iterated with scanning room updating respectively between scanning, ultimately generate the data that can describe sea clutter and sexual intercourse clutter power statistics of gradualization, thus set up self-adapting clutter power statistic figure based on radar video signal；S2: the cartogram obtaining step S1 is managed and compresses storage.The object of the invention is to solve the clutter power statistics under the complex scenes such as the sea clutter occurred in the radar video detection process of sea and sexual intercourse meteorological clutter, it is provided that the effective solution of complete set；And give a kind of efficient data compression decompression data structure, solve the problem that the storage stored and use a large amount of real-time data such as clutter power cartogram, the whole frame data of scanning room is big with memory cost during use.
Description
Technical field
The present invention relates to radar video signal treatment technology, particularly relate to a kind of self-adapting clutter power statistic algorithm.
Background technology
Radar clutter is defined as the echo-signal of the various objects reflection being not intended to detection that radar receives.These are not required to
The echo-signal wanted " upsets " work of radar, makes the detection of interesting target echo becomes difficulty.Radar clutter includes
From land, weather (particularly rain), ocean, insecticide and the echo of flock of birds.Sea clutter refer to except target interested it
Outward, from the radar return on 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 scientific and technical, military
And civil area is always an important problem and research direction.
Sea clutter drastically influence the performance that sea is detected by radar.For a long time, the research of radar sea clutter characteristic is subject to
To paying much attention to, be considered Radar Sea Area Objects detect, follow the tracks of, one of the key technology that identifies.And it is miscellaneous to estimate accurately to go to sea
Ripple, the power of sexual intercourse clutter can be given follow-up to clutter recognition offer foundation.
In practical engineering project is applied, discovery, the tenacious tracking of target are brought the biggest by sea clutter and sexual intercourse clutter
Interference.Improve the target detection in clutter stronger with tenacious tracking ability need.In target tracking domain for clutter
Devise effective association algorithm and the means such as multiple association.And for the current detection of a large amount of False Intersection Points marks produced in clutter
Means the most well solve the problem that false-alarm is many.False alarm rate is reduced to radar system while improving clutter internal object probability of detection
The performance indications of system promote and are of great importance.And existing various CFAR (CFAR) detector all assumes that clutter (or noise) is carried on the back
Scape obeys certain statistical distribution pattern.And actual clutter amplitude once deviate from the statistical distribution pattern of hypothesis, CFAR detector
Detection performance will necessarily receive impact, even its CFAR characteristic and be likely to be difficult to ensure that.Therefore adaptive polo placement clutter merit
Rate has important practical significance.
Summary of the invention
Goal of the invention: it is an object of the invention to provide a kind of self-adapting clutter that can solve the problem that defect that prior art exists
Power statistic algorithm.
Technical scheme: self-adapting clutter power statistic algorithm of the present invention, comprises the following steps:
S1: input data by radar original video signal, generates the clutter power statistical data of azran two dimension, raw
This clutter power statistical data become is iterated with scanning room updating respectively between scanning, and ultimately generates can retouching of gradualization
State the data of sea clutter and sexual intercourse clutter power statistics, thus set up self-adapting clutter power statistic based on radar video signal
Figure；
S2: the cartogram obtaining step S1 is managed and stores.
Further, the process of the clutter power statistical data of described generation azran two dimension have employed sort method, also
I.e. randomly select N number of sample to be ranked up, take the value the being ordered as k clutter power estimated value as current region.
Further, described k meets: N/2 < k < N.
Further, k value when k value when investigative range internal object is few is more than target is big.
Further, during the clutter power statistical data of described generation azran two dimension, by radar detection area
Being divided into multiple azran unit, the method calculating the clutter power amplitude in azran unit is: gather with
The data of L × L the azran unit that azran unit to be calculated is neighbouring, each azran unit is by apart from upper
M distance quantifying unit and orientation on n orientation quantifying unit composition, each neighbouring azran unit is all from its m
× n unit is randomly drawed the value statistical sample as this neighbouring azran unit of a position, and to L × L
Neighbouring azran cell data is ranked up, and the azran unit that the statistical sample of range value maximum is corresponding is target institute
At unit, clutter average amplitude value is the system of the azran unit being ordered as K in L × L neighbouring azran unit
Meter sample magnitude, 0 < K < 1, adjust K value according to radar application scene.
Further, described in be iterated update during use interframe iterative algorithm, comprise the following steps:
S1.1: to present frameAdd up: radar detection area is divided into multiple azran unit, each orientation
Distance unit is made up of m distance quantifying unit and n orientation quantifying unit；Wherein, the dividing mode of distance quantifying unit is:
By the range averaging in investigative range or 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 hunting zone；
S1.2: use formula (1) to be iterated:
In formula (1),For the clutter at i-th azran unit in kth frame data and the mean power of noise,For the clutter at ith sample unit in kth-1 data and the average power content of noise,For kth frame data i-th
Sampling unit is in the clutter of present frame and the mean power of noise, and a is Forgetting coefficient.
Further, in described step S2, cartogram carrying out storage and uses compression storage mode, compression and storage method is:
To scan data to be stored in input block and scan in queue, in queue caching is scanned in input, data send into data pressure after reaching l
Contracting module, in data compressing module use LZ4 data compression algorithm data are carried out lossless compress, the data after compression according to
The structure of annular data compression blocks queue is put in memory address.
Further, the method decompressing data is: first search data to be decompressed in IOB scans queue,
If finding data to be decompressed, directly export, the most then from annular data compression blocks queue, search data pair to be decompressed
The call number answered, and decompress a compressed data block, put into IOB and scan queue and read data.
Beneficial effect: compared with prior art, the present invention has a following beneficial effect:
(1) miscellaneous under the complex scenes such as sea clutter and the sexual intercourse meteorological clutter occurred in processing for sea radar video detection
Wave power is added up, it is provided that the effective solution of complete set；
(2) give a kind of efficient data compression decompression data structure, solve storage and use clutter power statistics
The problem that when storage of a large amount of real-time data such as figure, the whole frame data of scanning room and use, memory cost is big.
(3) input that self-adapting clutter power statistic arithmetic result accumulates as combination type scanning room iteration makes with reference to thresholding
With, it is to avoid single thresholding causes and for a long time causes track rejection at calculating scanning room iterative product or cause false-alarm too much, it is achieved sea is miscellaneous
Ripple and the automatization of sexual intercourse clutter recognition.
Accompanying drawing explanation
Fig. 1 is inventive algorithm schematic diagram of location in sea clutter with sexual intercourse Clutter suppression algorithm；
Fig. 2 is the division methods schematic diagram of the distance 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 obtaining statistical sample of the present invention；
Fig. 5 is the process schematic being ranked up azran unit of the present invention；
Fig. 6 is data compression and the process schematic of decompression of the present invention.
Detailed description of the invention
The invention discloses a kind of self-adapting clutter power statistic algorithm, comprise the following steps:
S1: input data by radar original video signal, generates the clutter power statistical data of azran two dimension, raw
This clutter power statistical data become is iterated with scanning room updating respectively between scanning, and ultimately generates can retouching of gradualization
State the data of sea clutter and sexual intercourse clutter power statistics, thus set up self-adapting clutter power statistic based on radar video signal
Figure；
S2: the cartogram obtaining step S1 is managed and stores.
The present invention have employed the methods such as azran two-dimensional sampling and sequence and improves computational efficiency when carrying out statistical computation,
Remove the target interference effect to generating two dimension clutter map simultaneously.The thought using ranking method statistics clutter and noise power is ginseng
Examining algorithm based on ordered statistics in OS-CFAR, OS-CFAR is that the N number of sample near detected unit is chosen and is ordered as k
Value as the estimation of interference power level.
The process of the clutter power statistical data generating azran two dimension in step S1 have employed sort method, is little
Randomly select N number of sample in region to be ranked up, take the value the being ordered as k clutter power estimated value as current region.Therefore,
When jamming target number is less than (N-k) in statistical sample, can effectively prevent target echo from being added up, and when target sample number
During more than (N-k), result will be interfered the impact of target.Through test, < during N/2, power statistic is on the low side, can cause statistics for k
Calculate error, the therefore condition of the value general satisfaction N/2 < k < N of k.When investigative range internal object is less k value can value bigger than normal
A bit, during target comparatively dense, k value value wants less than normal.Its process is as shown in Figure 5.Self-adapting clutter power statistic is at sea clutter and cloud
In rain clutter suppression processing procedure, location is as shown in Figure 1.
During generating the clutter power statistical data of azran two dimension, radar detection area is divided into multiple side
The distance of positions from unit, the method calculating the clutter power amplitude in azran unit is: as shown in Figure 4, gathers and treats
The data of neighbouring L × L the azran unit of azran unit calculated, each neighbouring azran unit be by away from
N orientation quantifying unit composition in upper m distance quantifying unit and orientation, each neighbouring azran unit all from
Its m × n unit is randomly drawed the value statistical sample as this neighbouring azran unit of a position, and to L × L
Individual neighbouring azran cell data is ranked up, and the azran unit that the statistical sample of range value maximum is corresponding is target
Place unit, clutter average amplitude value is to be ordered as the azran unit of K in L × L neighbouring azran unit
Statistical sample amplitude, 0 < K < 1, adjust K value according to radar application scene.
Use interframe iterative algorithm during being iterated updating, comprise the following steps:
S1.1: to present frameAdd up: radar detection area is divided into multiple azran unit, azran
Unit is made up of m distance quantifying unit and n orientation quantifying unit；Wherein, dividing mode such as Fig. 2 institute of distance quantifying unit
Show, for: by the range averaging in investigative range or non-be divided equally into M unit, the dividing mode of orientation quantifying unit such as Fig. 3
Shown in, for: 360 degree are averagely divided into N number of orientation quantifying unit；It is then determined that hunting zone: with target during following the tracks of
Centered by position, search radius r is relevant with the radar antenna cycle, and the cycle search radius of the longest correspondence is the biggest, typically sets
Put between 1～5Km.
S1.2: use formula (1) to be iterated:
In formula (1),For the clutter at i-th azran unit in kth frame data and the mean power of noise,For the clutter at ith sample unit in kth-1 data and the average power content of noise,For kth frame data i-th
Sampling unit is in the clutter of present frame and the mean power of noise, and a is Forgetting coefficient.
Generally radar video data are relatively big in the scanning (circle) data volume in the cycle, and need in this patent to store
Multiple whole circle radar video data, can cause memory space expense relatively big, and therefore step S2 devises a kind of radar video data
Compression storage with decompression read method solve the bigger problem of storage overhead, as shown in Figure 6.Cartogram carries out storage use
Compression storage mode, compression and storage method is: will scan data and be stored in input block and scan in queue, scans queue when input and caches
Middle data send into data compressing module after reaching l, use LZ4 data compression algorithm to enter data in data compressing module
Row lossless compress, the data after compression are put in memory address according to the structure of annular data compression blocks queue.Data are carried out
The method of decompression is: first search data to be decompressed in IOB scans queue, if finding data to be decompressed, straight
Connect output, the most then from annular data compression blocks queue, search the call number that data to be decompressed are corresponding, and decompress a number
According to compression blocks, put into IOB and scan queue and read data.Read data buffer storage formula design advantage be normally to read orientation/
Scan data mode to read for order, when solve extrude a certain index position compression data after i.e. solve and extrude l orientation/scan number
According to, follow-up l-1 azimuth sweep is then without again solving from annular data compression blocks queue.Self-adapting clutter power statistic
Data result, scanning room accumulation algorithm intermediate object program all can use notebook data storage mode, have been greatly saved opening of memory space
Pin.
Claims (8)
1. a self-adapting clutter power statistic algorithm, it is characterised in that: comprise the following steps:
S1: input data by radar original video signal, generates the clutter power statistical data of azran two dimension, generation
This clutter power statistical data is iterated with scanning room updating respectively between scanning, and ultimately generate gradualization can describe sea
The data that clutter is added up with sexual intercourse clutter power, thus set up self-adapting clutter power statistic figure based on radar video signal；
S2: the cartogram obtaining step S1 is managed and stores.
Self-adapting clutter power statistic algorithm the most according to claim 1, it is characterised in that: described generation azran two
The process of clutter power statistical data of dimension have employed sort method, namely randomly select N number of sample and be ranked up, and takes and is ordered as
The value of k is as the clutter power estimated value of current region.
Self-adapting clutter power statistic algorithm the most according to claim 2, it is characterised in that: described k meets: N/2 < k < N.
Self-adapting clutter power statistic algorithm the most according to claim 2, it is characterised in that: when investigative range internal object is few
K value more than target time k value big.
Self-adapting clutter power statistic algorithm the most according to claim 1, it is characterised in that: described generation azran two
During the clutter power statistical data of dimension, radar detection area is divided into multiple azran unit, to azran
The method that clutter power amplitude in unit carries out calculating is: gather L × L the side neighbouring with azran unit to be calculated
The distance of positions is from the data of unit, and each azran unit is by n orientation in m upper distance quantifying unit and orientation
Quantifying unit is constituted, and each neighbouring azran unit all randomly draws the value conduct of a position from its m × n unit
The statistical sample of this neighbouring azran unit, and L × L neighbouring azran cell data is ranked up, amplitude
The azran unit that the statistical sample of value maximum is corresponding is target place unit, and clutter average amplitude value is that L × L is individual neighbouring
Azran unit in be ordered as the statistical sample amplitude of azran unit of K, 0 < K < 1, adjust according to radar application scene
Whole K value.
Self-adapting clutter power statistic algorithm the most according to claim 1, it is characterised in that be iterated renewal described in:
During use interframe iterative algorithm, comprise the following steps:
S1.1: to present frameAdd up: radar detection area is divided into multiple azran unit, each azran
Unit is made up of m distance quantifying unit and n orientation quantifying unit；Wherein, the dividing mode of distance quantifying unit is: will visit
Range averaging in the range of survey or non-be divided equally into M unit, the dividing mode of orientation quantifying unit 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；It is then determined that
Hunting zone；
S1.2: use formula (1) to be iterated:
In formula (1),For the clutter at i-th azran unit in kth frame data and the mean power of noise,For
Clutter at ith sample unit and the average power content of noise in kth-1 data,For kth frame data ith sample unit
Being in the clutter of present frame and the mean power of noise, a is Forgetting coefficient.
Self-adapting clutter power statistic algorithm the most according to claim 1, it is characterised in that: in described step S2, to system
Meter figure carries out storage and uses compression storage mode, and compression and storage method is: will scan data and be stored in input block and scan in queue, when
Input is scanned data in queue caching and is sent into data compressing module after reaching l, uses LZ4 data in data compressing module
Compression algorithm carries out lossless compress to data, and the data after compression put into internal memory ground according to the structure of annular data compression blocks queue
In location.
Self-adapting clutter power statistic algorithm the most according to claim 7, it is characterised in that: the side that data are decompressed
Method is: first searching data to be decompressed in IOB scans queue, if finding data to be decompressed, directly exporting, no
From annular data compression blocks queue, the most then search the call number that data to be decompressed are corresponding, and decompress a compressed data block,
Put into IOB scan queue and read data.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110161473A (en) * | 2019-05-08 | 2019-08-23 | 南京莱斯电子设备有限公司 | It is a kind of based on primitive clutter map and the clutter map of figure layer detection and update method |
| 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 |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101452074A (en) * | 2007-12-07 | 2009-06-10 | 南京理工大学 | Strong clutter self-adapting cancellation apparatus for analog-digital combined treatment |
| CN102540162A (en) * | 2011-12-12 | 2012-07-04 | 中国船舶重工集团公司第七二四研究所 | Method for estimating low-altitude electromagnetic wave propagation characteristic on basis of sea clutter |
| 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 |
| CN104849707A (en) * | 2015-04-27 | 2015-08-19 | 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 |
| CN105044686A (en) * | 2015-08-03 | 2015-11-11 | 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
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101452074A (en) * | 2007-12-07 | 2009-06-10 | 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 |
| CN102540162A (en) * | 2011-12-12 | 2012-07-04 | 中国船舶重工集团公司第七二四研究所 | Method for estimating low-altitude electromagnetic wave propagation characteristic on basis of sea clutter |
| CN104849707A (en) * | 2015-04-27 | 2015-08-19 | 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 |
| CN105044686A (en) * | 2015-08-03 | 2015-11-11 | 中国电子科技集团公司第二十八研究所 | Radar dense false target interference inhibition method |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110161473A (en) * | 2019-05-08 | 2019-08-23 | 南京莱斯电子设备有限公司 | It is a kind of based on primitive clutter map and the clutter map of figure layer detection and update method |
| 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 |
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