CN105866748A - Constant false alarm detection method based on former detection information-constant window - Google Patents

Constant false alarm detection method based on former detection information-constant window Download PDF

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Publication number
CN105866748A
CN105866748A CN201610166232.3A CN201610166232A CN105866748A CN 105866748 A CN105866748 A CN 105866748A CN 201610166232 A CN201610166232 A CN 201610166232A CN 105866748 A CN105866748 A CN 105866748A
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China
Prior art keywords
detection
sliding window
cfar
target
stored
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CN201610166232.3A
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CN105866748B (en
Inventor
于雪莲
李海翔
何荣江
贾静
周云
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University of Electronic Science and Technology of China
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University of Electronic Science and Technology of China
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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
    • G01S7/28—Details of pulse systems
    • G01S7/285—Receivers
    • G01S7/292—Extracting wanted echo-signals
    • G01S7/2923—Extracting wanted echo-signals based on data belonging to a number of consecutive radar periods
    • G01S7/2927—Extracting wanted echo-signals based on data belonging to a number of consecutive radar periods by deriving and controlling a threshold value
    • 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
    • G01S7/35—Details of non-pulse systems
    • G01S7/352—Receivers
    • G01S7/354—Extracting wanted echo-signals

Abstract

The invention discloses a constant false alarm detection method based on a former detection information-constant window, belongs to the technology of signal processing, and specifically relates to a constant false alarm detection method. The method comprises the steps: obtaining the mean power of a plurality of detection units with no target; multiplying a nominal factor with the mean power, obtaining a target detection threshold value, comparing the power of the next detection unit with the threshold value, and judging whether the detection unit has a target or not: bringing the power of the detection unit into the mean power for calculating the threshold value if there is no target, and sequentially carrying out the above steps in a circular manner. The method is simple in calculation, and is high in precision.

Description

A kind of fixed window long CFAR detection method based on detection priori
Technical field
The invention belongs to signal processing technology, be specifically related to CFAR detection method.
Background technology
Detection is a basic operation of signal processing.CFAR algorithm is a kind of particularly important method in detection technique, nearly tens Through in-depth study over Nian, it is widely used in the field such as radar, communication.
So-called CFAR (false alarm rate, CFAR), namely keeps constant false-alarm probability during detection, is not subject to The impact of background clutter.In actual detections of radar, in order to obtain stable detection performance and constant false-alarm probability, usually from real time Measure and the echo data obtained is estimated background clutter power level, thus be adaptively adjusted detection threshold.
Background clutter power is estimated by key that CFAR processes exactly.Corresponding processing method is divided into two big classes: a class is empty Territory CFAR, utilizes the reference unit the most adjacent with unit to be detected to estimate clutter power；Another kind of is sequential CFAR, By same unit Multiple-Scan is carried out power estimation, have main steps that the iteration in time domain.Cell-average (cell average, CA) CFAR, MAXIMUM SELECTION (greatest of, GO) CFAR, minimum selection (smallest of, SO) CFAR are with orderly Statistics (ordered statistics, OS) CFAR is several spatial domains CFAR algorithm the most classical.They are estimating background power Shi Junwei gets rid of the object element being detected.
Summary of the invention
The technical problem to be solved is to provide the fixed window based on detection priori that a kind of amount of calculation is little, accuracy of detection is high Long CFAR detection method.
The present invention solves that above-mentioned technical problem be employed technical scheme comprise that, a kind of fixed window length (former based on detection priori Detection information-constant window, FDI-CW) CFAR detection method, the method includes:
Step 1: by the two paths of signals of in-phase component and quadrature component through matched filtering, square-law detector try to achieve current detection The performance number of echo samples in unit；
Step 2: compared with detection threshold by current power value, performance number is not less than thresholding, it is determined that for target being detected；Otherwise, It is judged to driftlessness；Initial threshold is tried to achieve by the following method: directly take away R the space cell of beginning as original reference unit, Try to achieve their average, be multiplied by after normalized factor T as detection threshold, the performance number of this R unit is directly stored in reference In sliding window, being all stored in 0 in testing result sliding window, described normalized factor T determines according to practical situation simultaneously；
Step 3: if the target of detecting, be then stored in 0 with reference to sliding window, be stored in testing result sliding window by 1；If the target of being not detected by, Then current power value is stored in reference to sliding window, is stored in testing result sliding window by 0；Described reference sliding window and testing result sliding window length phase It is all R, and data therein follow first in first out；
Step 4: reference sliding window now is summed to sum, testing result sliding window is summed to m, with sum divided by (R-m), Obtaining detection statistic Z, Z is multiplied by normalized factor T and obtains the thresholding of detection next time；
Step 5: try to achieve the performance number of next detector unit according to step 1, then carry out step 2, the now thresholding in step 2 Value tries to achieve threshold value for step 4, then carries out following step successively；Final circulation solves, and echo signal is carried out target detection.
Further, in described step 2, normalized factor T uses following two formula of associating to try to achieve:
Pfa=(1+T/ (R-m))-(R-m)
P d = ( 1 + T ( 1 + λ ) ( R - m ) ) - ( R - m )
P in formulafaFor false-alarm probability, PdFor detection probability, λ is signal to noise ratio.
The invention has the beneficial effects as follows:
1, under homogeneous background single goal environment, the detection performance of the present invention is better than GO-CFAR, SO-CFAR and OS-CFAR, And suitable with CA-CFAR, but amount of calculation of the present invention is little, and simple to operate.As shown in Figure 2, under homogeneous background single goal environment, The detection probability curve of the present invention and CA-CFAR is completely superposed.And in such circumstances, the performance of the present invention be better than GO-CFAR, SO-CFAR and OS-CFAR.
2, under homogeneous background target-rich environment, the detection performance of the present invention is better than CA-CFAR, GO-CFAR, SO-CFAR With OS-CFAR algorithm.So-called multiple target, refers to there is one or more jamming target with reference in sliding window.In the present context, Know that the performance of SO-CFAR is better than GO-CFAR and CA-CFAR.From the figure 3, it may be seen that the detection performance of the present invention is better than SO-CFAR And OS-CFAR.Therefore, under homogeneous background target-rich environment, the detection performance of the present invention is better than above-mentioned four kinds of algorithms.
Accompanying drawing explanation
Fig. 1 is a kind of fixed window long CFAR detection method schematic diagram based on detection priori of the present invention；
Fig. 2 is under homogeneous background single goal environment, the detection probability curve synoptic diagram of the present invention and CA-CFAR；In Fig. 2, two is bent Line is completely superposed, and always, but amount of calculation of the present invention is less for testing result, and speed is faster；
Fig. 3 is under homogeneous background target-rich environment, and the present invention detects performance comparison figure；Wherein (a) is the present invention and SO-CFAR Contrast, (b) is the present invention and OS-CFAR comparison diagram.
Detailed description of the invention
The present invention comprises I/Q two ways of digital signals and enters detector of the present invention.
Time initial, I road and Q road signal through matched filtering, are tried to achieve echo samples in current detection unit by square-law detector Performance number.
Being compared with detection threshold by current power value, performance number is not less than thresholding, it is determined that for target being detected, i.e. H1；Otherwise, It is judged to driftlessness, i.e. H0。
After judging to terminate, if H1, it is stored in reference to sliding window by 0, as the reference unit of next detector unit；If H0, ought Front performance number is stored in reference to sliding window.So, reference sliding window eliminates and has been detected as target.Meanwhile, this testing result is deposited Enter testing result sliding window, if H1, it is stored in 1；H0, it is stored in 0.
The detection threshold of this detection is obtained by following methods:
The present invention has two sliding window, is that window length is all fixed as R with reference to sliding window and testing result sliding window respectively.
Before this has judged, sue for peace to reference to sliding window and testing result sliding window, and value is respectively sum and m.Then (R-m) Represent non-targeted number in current reference sliding window.With sum divided by (R-m), obtaining detection statistic Z, detection statistic is right The estimation of background clutter.Z is multiplied by normalized factor T and can be obtained by the thresholding of this detection.
Concrete structure of the present invention and workflow be as shown in Figure 1:
In Fig. 1, D represents the performance number of current detection unit, Xi(i=1 ..., R) represent reference unit, collectively form with reference to sliding Window.H (i) represents XiAs testing result during detector unit, collectively constitute testing result sliding window.M represents with reference to target in sliding window Number, Z is detection statistic, and T is the normalized factor, and S represents detection threshold.
For detector of the present invention, false-alarm probability PfaWith detection probability PdIt is respectively as follows:
Pfa=(1+T/ (R-m))-(R-m)
P d = ( 1 + T ( 1 + λ ) ( R - m ) ) - ( R - m )
In formula, λ represents signal to noise ratio.
1, under homogeneous background single goal environment, the detection performance of the present invention is better than GO-CFAR, SO-CFAR and OS-CFAR, And it is suitable with CA-CFAR.As shown in Figure 2, under homogeneous background single goal environment, the detection of the present invention and CA-CFAR is general Rate curve is completely superposed.And in such circumstances, it is known that the performance of CA-CFAR be better than GO-CFAR, SO-CFAR and OS-CFAR.Therefore, under homogeneous background single goal environment, the detection performance of the present invention is also superior to above-mentioned three kinds of algorithms.
2, under homogeneous background target-rich environment, the detection performance of the present invention is better than CA-CFAR, GO-CFAR, SO-CFAR With OS-CFAR algorithm.So-called multiple target, refers to there is one or more jamming target with reference in sliding window.In the present context, Know that the performance of SO-CFAR is better than GO-CFAR and CA-CFAR.From the figure 3, it may be seen that the detection performance of the present invention is better than SO-CFAR And OS-CFAR.Therefore, under homogeneous background target-rich environment, the detection performance of the present invention is better than above-mentioned four kinds of algorithms.

Claims (2)

1. a fixed window long CFAR detection method based on detection priori, the method includes:
Step 1: by the two paths of signals of in-phase component and quadrature component through matched filtering, square-law detector try to achieve current detection The performance number of echo samples in unit；
Step 2: current power value is compared with detection threshold, performance number is not less than thresholding, it is determined that for target being detected；No Then, it is determined that for driftlessness；Initial threshold is tried to achieve by the following method: directly take away R the space cell of beginning as original reference list Unit, tries to achieve their average, is multiplied by after normalized factor T as detection threshold, the performance number of this R unit is directly stored in With reference in sliding window, being all stored in 0 in testing result sliding window, described normalized factor T determines according to practical situation simultaneously；
Step 3: if the target of detecting, be then stored in 0 with reference to sliding window, be stored in testing result sliding window by 1；If the target of being not detected by, Then current power value is stored in reference to sliding window, is stored in testing result sliding window by 0；Described reference sliding window and testing result sliding window length phase It is all R, and data therein follow first in first out；
Step 4: reference sliding window now is summed to sum, testing result sliding window is summed to m, with sum divided by (R-m), Obtaining detection statistic Z, Z is multiplied by normalized factor T and obtains the thresholding of detection next time；
Step 5: try to achieve the performance number of next detector unit according to step 1, then carry out step 2, the now thresholding in step 2 Value tries to achieve threshold value for step 4, then carries out following step successively；Final circulation solves, and echo signal is carried out target detection.
A kind of fixed window long CFAR detection method based on detection priori, it is characterised in that described In step 2, normalized factor T uses following two formula of associating to try to achieve:
Pfa=(1+T/ (R-m))-(R-m)
P d = ( 1 + T ( 1 + λ ) ( R - m ) ) - ( R - m )
P in formulafaFor false-alarm probability, PdFor detection probability, λ is signal to noise ratio.
CN201610166232.3A 2016-03-22 2016-03-22 A kind of long CFAR detection method of fixation window based on detection priori Expired - Fee Related CN105866748B (en)

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Cited By (4)

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CN110954879A (en) * 2019-12-02 2020-04-03 北京无线电测量研究所 Digital detection method and system for moving threshold
CN111856406A (en) * 2020-06-02 2020-10-30 珠海微度芯创科技有限责任公司 Target detection method and device based on FMCW radar echo
CN112492888A (en) * 2019-07-12 2021-03-12 华为技术有限公司 Method and apparatus for an object detection system
CN115032606A (en) * 2022-08-11 2022-09-09 湖北工业大学 Constant false alarm detector based on local minimum selected unit average

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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112492888A (en) * 2019-07-12 2021-03-12 华为技术有限公司 Method and apparatus for an object detection system
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CN111856406A (en) * 2020-06-02 2020-10-30 珠海微度芯创科技有限责任公司 Target detection method and device based on FMCW radar echo
CN111856406B (en) * 2020-06-02 2023-07-07 珠海微度芯创科技有限责任公司 Target detection method and device based on FMCW radar echo
CN115032606A (en) * 2022-08-11 2022-09-09 湖北工业大学 Constant false alarm detector based on local minimum selected unit average
CN115032606B (en) * 2022-08-11 2022-11-04 湖北工业大学 Constant false alarm detector based on local minimum selected unit average

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