An Adaptive Constant False Alarm Rate Target Detection Method

Abstract

The invention relates to an adaptive constant false alarm rate target detection method, comprising the following steps: 1): inputting data received by a radar into a matched filter; 2): inputting a signal output by a matched filter into a square law processing in the detector; 3): Finally, the signal output from the square law detector is passed to the CFAR detector for processing, and the estimated value Z of the clutter power level generated by the reference unit sampling according to the corresponding CFAR algorithm is obtained; 4): according to 3) Obtained reference unit sampling According to the estimated value Z of the clutter power level generated by the corresponding CFAR algorithm, the CFAR detector outputs the final decision, that is, whether there is a target in the detection unit. According to the statistical mean value x and variance of the sampling values ​​in the reference sliding window, the method deletes the sampling values ​​whose variance is greater than a certain value, replaces the sampling value with the mean value of the remaining effective sampling values, and recalculates the mean value of the sampling values.

Description

technical field [0001] The invention belongs to the field of signal detection, and in particular relates to an adaptive constant false alarm rate target detection method. Background technique [0002] Constant false alarm rate detection (CFAR) is the use of adaptive threshold estimation technology to automatically detect the target signal. The detection threshold is related to the average power of local environmental noise or clutter. Therefore, in order to design a good CFAR receiver, the statistical information of background noise or clutter is particularly important. Usually they obey various specific distributions, such as Rayleigh distribution, lognormal distribution, Weibull distribution or k-distribution. [0003] When the background noise obeys the uniform Rayleigh distribution, the CA-CFAR algorithm has the best detection performance. At this time, background noise sampling needs to meet certain assumptions, that is, all samples are independent and identically di...

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Problems solved by technology

However, the actual working conditions are much more complicated than the above assumptions. The following two situations make the detection environment no longer meet the independent and identical distribution: 1) abnormal values ​​from interference targets, pulse interference, etc.; 2) when the detection The environment is located at the junction of land and sea, and the clutter characteristics are no longer uniform

In the first case, the strong target will have a shadowing effect on the weak target, making the detection threshold higher than the actual value, thereby reducing the detection probability

In the second case, at the clutter junction, which is the so-called clutter edge, when the detection unit is located in a weak power area, it will produce a result similar to the first case with a decrease in detection probability; when the detection unit is located in a high power area , so that the detection threshold is smaller than the actual value, which will produce an excessively high false alarm rate, and will cause missed detection of targets at the junction of clutter

As shown in the previous analysis, when the number of interference targets changes randomly, the background noise power level value will deviate from the actual value, and then the false alarm probability and detection probability will deviate, making it impossible to achieve constant false alarm rate detection, and even affect the reliability of detection results. sex

From the above analysis, it can be known that most of the methods will degrade their detection performance when the number of interference targets changes randomly.

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