Airborne radar space-time adaptive processing method based on efa and mwf

Abstract

The invention discloses a method for space-time adaptive processing of airborne radar based on EFA (Extended Factored Approach) and MWF (Multistage Wiener Filter). The train of thoughts is that the method comprises the following steps: performing spatial domain dimension reduction processing on echoes received by the airborne radar in a coherent processing interval; acquiring included N sub arrays of the airborne radar in a coherent processing interval after spatial domain dimension reduction; recording the echo received by the nth sub array of the airborne radar in a coherent processing interval after spatial domain dimension reduction as Xn; then calculating an echo space-time vector Yk of the kth Doppler channel in the N sub arrays of the airborne radar after weighted fast Fourier transform processing; then performing multistage wiener filtering processing on the echo space-time vector Yk to obtain an echo adaptive weight vector W[Yk] of the kth Doppler channel in the N sub arrays of the airborne radar after D-stage wiener filtering processing; and finally, performing wiener filtering on the echo space-time vector Yk by using the echo adaptive weight vector W[Yk] to obtain an echo adaptive weight vector Zk of the kth Doppler channel in the N sub arrays of the airborne radar after two-stage dimension reduction space-time adaptive processing based on the EFA and the MWF.

Description

technical field [0001] The invention belongs to the technical field of radar, in particular to an airborne radar space-time adaptive processing method based on EFA and MWF, that is, based on extended factored approach (Extended Factored Approach, EFA) and multistage Wiener filter (Multistage Wiener Filter , MWF) airborne radar space-time adaptive processing method, which is suitable for adaptive processing of airborne radar signals. Background technique [0002] Airborne radar also receives a large number of clutter signals while receiving the echoes of moving targets. The strength of these clutter signals is often much greater than the target signal to be detected, so that the signal of the moving target to be detected is submerged in the clutter, which affects the The target detection performance of airborne radar; in order to improve the target detection performance of airborne radar, it is necessary to suppress the clutter of the moving target echo received by the airbor...

AI Technical Summary

Problems solved by technology

[0004] An important problem that needs to be solved in space-time adaptive processing is how to achieve dimensionality reduction processing while minimizing the loss of detection performance; with the development of science and technology, the number of array elements in the airborne radar antenna array is increasing, The number of array elements is even as high as several thousand, and in the work of airborne radar, dozens or even hundreds of pulses can be transmitted within a coherent processing interval (CPI), making the space-time degrees of freedom of airborne radar reach tens of thousands , at this time, if full space-time processing is performed, it is not only difficult to obtain enough independent identically distributed (IID) training samples, but also the calculation of matrix inversion at such a high order is too large, and it is difficult to obtain sufficiently accurate calculations As a result, therefore, it is unrealistic to perform full space-time adaptive processing in the present situation, and dimensionality reduction or rank reduction processing must be performed

[0005] In 1992, R. Dipietro proposed the Factored Approach (FA) and the Extended Factored Approach (EFA); Adaptive processing method, the structure of this method is simple and easy to implement, and it can obtain better performance in the side lobe clutter area, but its performance in the main lobe clutter area is general

Different from the FA method, the EFA method combines multiple adjacent Doppler channels together for space-time adaptive processing, which greatly improves the clutter suppression in the main lobe clutter area while moderately increasing the number of samples and computation. However, in practice, in order to make the signal-to-noise ratio of the target as high as possible, the number of coherent pulses emitted by the airborne radar is usually large, and the number of samples required for clutter suppression is still large

[0006] Rank reduction processing is a method belonging to the characteristic subspace. This type of method utilizes the low-rank characteristics of the covariance matrix and the orthogonality of the noise subspace and the clutter subspace; compared with the fixed dimensionality reduction method, the rank reduction processing The performance loss is small, but the disadvantage is that the amount of calculation is large, and it is difficult to determine the clutter rank of the measured data; in 1998, Goldstein and Reed et al. proposed a multistage Wiener filter (Multistage Wiener Filter, MWF) method that does not require feature decomposition. This kind of multistage Wiener filter (Multistage Wiener Filter, MWF) method that does not require eigendecomposition can directly decompose the input space-time data step by step through recursion, without estimating the clutter covariance matrix, and the amount of calculation is somewhat reduce

If the dimension of the clutter subspace can be accurately estimated, then the rank-reduced space-time adaptive processing (STAP) can achieve better performance. However, in practice, due to the errors of the airborne radar itself and the non-uniform airborne environment The real clutter rank cannot be accurately estimated due to the influence of unfavorable factors such as clutter, which leads to a decrease in the performance of space-time adaptive processing (STAP).

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