Aircraft radar clutter inhibiting method based on knowledge assisting sparse gradient minimum variance

Abstract

The invention discloses an aircraft radar clutter inhibiting method based on a knowledge assisting sparse gradient minimum variance. According to the main thought, the method comprises the steps of determining an aircraft radar and obtaining Nmax pieces of range gate radar echo data and Nmax pieces of to-be-detected unit data respectively; then determining a clutter ridge; making l belong to {1,2,...,Nmax} and calculating a final clutter power matrix of the l to-be-detected unit data xl on the clutter ridge; utilizing the final clutter power matrix of the l to-be-detected unit data xl on the clutter ridge to calculate a rebuilt space-time two-dimensional covariance matrix of the l to-be-detected unit data xl; making the value of l added with 1 until a rebuilt space-time two-dimensional covariance matrix of the Nmax to-be-detected unit data xl is obtained; utilizing the rebuilt space-time two-dimensional covariance matrix of the Nmax to-be-detected unit data tocalculate the weight used for processing the Nmax to-be-detected unit data and then obtain a space-time self-adaptive processing result, wherein the space-time self-adaptive processing result isthe aircraft radar clutter inhibiting result based on the knowledge assisting sparse gradient minimum variance.

Description

technical field [0001] The invention belongs to the field of radar technology, in particular to an airborne radar clutter suppression method based on knowledge-assisted sparse asymptotic minimum variance, 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. Since complex and strong ground clutter seriously affects the detection of ground moving targets by airborne early warning radar, how to reduce or eliminate the impact of ground clutter on the detection performance is a difficult problem for airborne early warning radar; space-time adaptive processing technology can Simultaneously distinguishing moving targets and clutter from the two dimensions of airspace and time domain can effectively improve the clutter s...

AI Technical Summary

Problems solved by technology

[0003] Since the statistical characteristics of the clutter signal of the unit to be detected are usually not known in advance, the traditional space-time adaptive processing 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), and the number of training samples required should not be less than twice the system degrees of freedom. Here, it is assumed that the selected training samples and the unit data to be tested satisfy the independent and identical distribution conditions, that is, these The training samples are uniform and have the same statistical characteristics as the data of the unit to be detected; however, due to the complex surface types and target pollution in the actual scene, airborne radars usually work in a non-uniform clutter environment; in addition, when When the radar antenna rotates, this will also lead to non-uniform airborne radar echo data, which makes it difficult to satisfy the above independent and identical distribution assumptions in the actual environment, which in turn affects the estimation accuracy of the clutter covariance matrix of the unit to be detected, resulting in STAP clutter Inhibition performance drops significantly

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