Self-adaptive constant false alarm rate target detection method

Abstract

The invention relates to a self-adaptive constant false alarm rate target detection method. The method includes the following steps: 1), transmitting data received by radar into a matching filter; 2), transmitting signals output by the matching filter into a square-law detector for processing; 3), transmitting signals output by the square-law detector into a CFAR (constant false alarm rate) detector for processing, and acquiring an estimation value Z of cluster power level generated by a reference unit according to a corresponding CFAR algorithm through sampling; 4), outputting a final judgment by the CFAR detector according to the estimation value Z acquired in step 3), namely detecting whether a target exists in the unit or not. In the method, according to statistical average value and variance of sampling values in a reference sliding window, the sampling value with the variance larger than a certain numerical value is deleted, an average value of remaining valid sampling values is used to replace the sampling value, and an average value of the sampling values is recalculated.

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...

AI Technical Summary

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.

Images