Knowledge-aided nonparametric constant false alarm detection method

Abstract

The invention discloses a knowledge-aided nonparametric constant false alarm detection method to solve detection problems which may occur in the non-uniform environment with unknown distribution, and especially relates to the knowledge assisting, nonparametric estimation and constant false alarm detection technologies, belonging to the technical field of radar knowledge assisting. The method mainly comprises four steps: the first step is to acquire a reference unit which is as independently and identically distributed as possible with a to-be-detected unit by using priori supplementary knowledge; the second step is to obtain an amplitude probability density function of the to-be-detected unit by using a nonparametric PDF estimation method; the third step is to calculate a detection threshold based on the PDF obtained in the previous step and a set false alarm probability; the last step is to compare the echo of the to-be-detected unit and the threshold obtained in the third step, and determining whether there is a target. In the invention, a uniform reference unit is obtained according to the priori information, and then background distribution is accurately estimated and the detection threshold is calculated, thereby improving the CFAR detection performance when the distribution is unknown in the non-uniform environment.

Description

Technical field [0001] The invention belongs to the field of radar knowledge assistance technology, and relates to knowledge assistance, non-parametric estimation and constant false alarm detection technology. Background technique [0002] The commonly used constant false alarm detection method first assumes that the background distribution is known, and then uses adjacent reference units to estimate the distribution parameters, and then estimate the detection threshold. However, with the improvement of radar detection power, the detection environment is becoming more and more complicated (time-varying and space-variant), and the statistical characteristics of clutter are becoming more and more complicated. The distribution characteristics of clutter are related to radar incidence angle, polarization method, terrain distribution, and artificial buildings , Pre-processing methods (coherent accumulation, amplitude detection, etc.) and other conditions are closely related. If any o...

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Problems solved by technology

If any of the above conditions changes, the statistical characteristics of clutter may change accordingly, resulting in a mismatch between the assumed clutter distribution model and the actual clutter distribution model or it is difficult to obtain the statistical distribution characteristics of clutter

At this time, the traditional constant false alarm detection method of parameter estimation has great defects: firstly, the assumed distribution model is difficult to simulate complex distribution, and secondly, the adjacent reference unit caused by non-uniformity cannot accurately reflect the clutter of the unit to be detected power situation

The disadvantage of the above aspects is that when faced with a complex distribution environment, the assumed distribution is often inaccurate, which affects the final detection performance

[0005] In summary, there is still a lack of a perfect solution to the two detection defects of the traditional constant false alarm detection algorithm in a complex and non-uniform environment

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