Iterative computation method for self-adaptive weight number in space time adaptive processing (STAP)

Abstract

The invention provides an iterative computation method for self-adaptive weight number in space time adaptive processing (STAP), aiming at solving the problem that the real-time requirement is hardly met by STAP technology due to the fact that great computation quantity and equipment quantity of a system are consumed as the STAP arithmetic self-adaptive weight value computation needs to directly inverse a space-time covariance matrix. The iterative computation method comprises the following steps of: firstly, obtaining an inverse matrix of a first impulse covariance matrix in a recursion way according to the Hermitian matrix properties, and obtaining the inversion of the final space-time covariance matrix step by step by means of nestification and recursion according to the impulse order, so that the computation quantity for computing the STAP self-adaptive weight value can be greatly reduced. According to the iterative computation method, the clutter suppression performance which is as same as that of the covariance matrix direct inversion STAP algorithm can be obtained, and the computation quantity for solving the self-adaptive weight value is only about 50% of the computation quantity of the covariance matrix direct inversion since the computation of the covariance matrix direct inversion is avoided, so that the engineering realization can be preferably carried out.

Description

technical field [0001] The invention belongs to the technical field of airborne phased array radar clutter suppression, and relates to an adaptive weight iterative calculation method in space-time adaptive processing. Background technique [0002] The airborne phased array radar can realize the effective detection of ground moving targets, but the airborne phased array radar in the looking-down working state will face more serious ground / sea clutter problems than the ground-based radar. The ground / sea clutter not only has a wide distribution and high intensity, but also exhibits strong space-time coupling characteristics. Space-Time Adaptive Processing (STAP) technology can make full use of space and time domain information, while coherently accumulating target signals, it combines the advantages of adaptive space-domain processing and adaptive Doppler processing, and combines the advantages of space-time domain Adaptive filtering of clutter can obtain better main lobe clut...

AI Technical Summary

Problems solved by technology

[0003] The conventional full-dimensional STAP algorithm requires a large number of training samples satisfying the independent and identical distribution (I.I.D) condition to estimate the covariance matrix, which is especially unsatisfied in a non-uniform clutter environment; and when the system has a high degree of freedom, The direct inversion operation (SMI) of the full-dimensional covariance matrix is ​​almost impossible to realize at the current computing level

Although the dimensionality reduction and non-uniform STAP algorithm can reduce the computational load of the STAP adaptive weight calculation and improve the clutter suppression performance of the STAP algorithm in the non-uniform clutter environment, the conventional dimensionality reduction STAP algorithm still faces the problem of solving the adaptive weight. The operation of directly inverting the covariance matrix will consume a large amount of calculation and equipment in the system, making it difficult for STAP technology to meet the real-time requirements of the system

In addition, although the SMI algorithm based on the inverse update of the covariance matrix does not need to estimate the sampling covariance matrix, the number of iterative calculations is equal to the number of training samples, but because the zero matrix does not have an inverse matrix, the algorithm will be difficult to set the initial inverse matrix to equivalent Solving the adaptive weight vector of the SMI algorithm can only obtain approximate solutions

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