Clutter Suppression Method for Airborne Radar Based on Knowledge Aided Maximum Likelihood

Abstract

The invention discloses an airborne radar clutter suppression method based on knowledge-assisted maximum likelihood. The main idea is to determine the airborne radar, and obtain N max range gate radar echo data and N max unit data to be detected; then determine the clutter ridge; let l∈{1,2,…,N max}, calculate the data x of the lth unit to be detected l The final clutter power matrix on the clutter ridge then uses the data x of the lth unit to be detected l Calculate the final clutter power matrix on the clutter ridge with the data x of the lth unit to be detected l The final space-time reconstruction covariance matrix of l adds 1 to the value of l until the Nth max The final space-time reconstruction covariance matrix of the unit data to be detected and then use the Nth max The calculation of the final space-time reconstruction covariance matrix of the unit data to be detected is used to process the Nth max The weight of the unit data to be detected is used to obtain the space-time adaptive processing result, which is the airborne radar clutter suppression result based on knowledge-assisted maximum likelihood.

Description

technical field [0001] The invention belongs to the technical field of radar, in particular to an airborne radar clutter suppression method based on knowledge-assisted maximum likelihood, which is suitable for suppressing strong ground clutter and detecting ground slow-moving targets by airborne early warning radars in non-uniform environments. Background technique [0002] Airborne early warning radar will inevitably be affected by complex and strong ground clutter while receiving target echo signals. How to effectively detect moving targets from the extremely strong clutter background is always the key point of airborne early warning radar signal processing. Therefore, clutter suppression has become the primary problem that must be solved; space-time adaptive processing technology can simultaneously distinguish moving targets and clutter from the two dimensions of airspace and time domain, and can effectively improve the clutter suppression and The performance of moving ob...

AI Technical Summary

Problems solved by technology

[0003] Since the statistical characteristics of clutter signals are usually unpredictable, traditional STAP needs to select the echo data near the unit to be detected as a training sample to estimate it; in order to obtain better STAP performance (the performance loss relative to the optimal processing is not more than 3dB), the number of training samples required should not be less than twice the degree of freedom of the system; here it is set that the selected training samples and the data of the unit to be tested satisfy the condition of independent and identical distribution, that is, these training samples are uniform and The statistical characteristics of the detection unit data are the same; however, due to the complex surface types, vegetation coverage, isolated interference, water towers, tall buildings, etc. in the actual scene, airborne radars usually work in a non-uniform clutter environment; in addition , when there is radar antenna yaw and target pollution; these factors will lead to non-uniform airborne radar echo data, making it difficult to satisfy the independent and identically distributed sample conditions required by the above assumptions, and then affect the estimation of the clutter covariance matrix of the unit to be detected Accuracy, resulting in a serious decline in STAP's clutter suppression performance

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