Time domain reconstruction airborne radar clutter rejection method based on knowledge assistance

Abstract

The invention belongs to the technical field of the radar, and discloses a time domain reconstruction airborne radar clutter rejection method based on knowledge assistance. A clutter covariance matrixof a to-be-detected unit is reconstructed by utilizing data of the to-be-detected unit, the method can effectively overcome non-uniformity of a training sample, and the error is more stable; strong ground clutter of an airborne early-warning radar under non-uniform environment can be effectively rejected, the estimation precision of the clutter covariance matrix of the to-be-detected unit is improved, the time domain reconstruction airborne radar clutter rejection method is easy to realize on the engineering, and good clutter rejection and target detection performance can be obtained at the same time.

Description

technical field [0001] The invention belongs to the technical field of radar, in particular to a knowledge-assisted time-domain reconstruction airborne radar clutter suppression method, which is suitable for airborne early warning radars in non-uniform environments to suppress strong ground clutter and detect ground low-speed moving targets. 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 signal processing (STAP) technology makes full use of the two-dimensional system degrees of freedom of the radar airspace and time domain, and can ensure that the target signal obtains a certain coherent accumulation gain...

AI Technical Summary

Problems solved by technology

The power selection training method adaptively selects samples with sufficient clutter power as training samples according to the actual clutter intensity measured. Disadvantages of overestimating

The phase and power selection training method selects echo data with sufficient power and phase distribution close to the clutter phase distribution as training samples, which can eliminate training samples containing strong interference target signals, but the problem of overestimation of noise-to-noise ratio still exists

Although this can reduce the non-uniformity between different samples to a certain extent, it cannot fundamentally solve the problem of non-uniform training samples

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