Self-adaptive constant false alarm rate target detection method

A constant false alarm rate and target detection technology, applied in the field of signal detection, can solve the problems of missed detection of targets at the intersection of clutter, failure to achieve constant false alarm rate detection, and detection performance degradation

Inactive Publication Date: 2015-04-08
CHANGAN UNIV
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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 decreas

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Embodiment Construction

[0043] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0044] see figure 1 , the present invention comprises the following steps:

[0045] Step 1): Pass the data received by the radar into the matched filter;

[0046] Step 2): Pass the signal output by the matched filter into the square law detector for processing;

[0047]Step 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;

[0048] Step 4): According to the estimated value Z of the clutter power level obtained by obtaining the reference unit sampled according to the corresponding CFAR algorithm obtained in step 3), the CFAR detector outputs a final decision, that is, whether there is a target in the detection unit.

[0049] In step 1), the type of da...

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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...

Claims

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Application Information

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IPC IPC(8): G01S7/41G01S7/292G01S7/35
CPCG01S7/292G01S7/35G01S7/41
Inventor 刘盼芝巫春玲谷文萍惠萌黄鹤王会峰
Owner CHANGAN UNIV
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