Method for estimating target echo signal subspaces of passive radars based on CSA-MWF (correlation subtraction algorithm-multistage wiener filter)

Abstract

The invention provides a method for estimating target echo signal subspaces of passive radars based on a CSA-MWF (correlation subtraction algorithm-multistage wiener filter), which comprises the following steps of: 1, extracting the vectors of observation data from a passive-radar receiving system, assigning the vectors to the initial observation data of the CSA-MWF, and initializing a desired signal; 2, deducing an expression of a target echo subspace estimation method; 3, calculating a forward filter of this level in the CSA-MWF; 4, calculating a desired signal of this level in the CSA-MWF;5, calculating updated observation data in the CSA-MWF; 6, carrying out threshold judgment; and 7, obtaining a target echo signal subspace through calculating. In the invention, the CSA-MWF (an effective dimensionality reduction method) is applied to passive radars, so that an operation of estimating a covariance matrix of observation data can be avoided, therefore, an operation of carrying out eigenvalue decomposition on the covariance matrix is avoided; and the calculated amount can be effectively reduced, therefore, the method is suitable to be used in complex environments with variable signals.

Description

technical field [0001] The invention belongs to the field of passive radar, in particular to a CSA-MWF-based method for estimating a subspace of a passive radar target echo signal. Background technique [0002] Passive radar refers to the radar itself that does not emit electromagnetic wave signals but only uses target radiation electromagnetic wave signals (external radiation sources) for target detection and tracking. It has good "four resistance properties", and has low cost, strong concealment, Advantages of high mobility. The electromagnetic signal radiated by the target may be the signal emitted by the target itself, or the electromagnetic signal of a third-party electromagnetic wave signal reflected by the target. Therefore, according to the type of target radiation signal source, passive radar can be divided into two categories: one is the passive radar that uses the target's own radiation source, including the radiation source carried by the target to be observed, ...

AI Technical Summary

Problems solved by technology

However, these algorithms need to decompose the covariance of the observed data, so the calculation is still very large.

The multi-level Wiener filter algorithm (GRS-MWF) proposed by Goldstein et al. is a new rank-reducing processing method, but its forward decomposition filters are not orthogonal to each other, and the numerical robustness is not good. It is used for passive The effect of radar target subspace estimation is not good, and it cannot meet the performance requirements of passive radar

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