Noise Subspace Estimation Method Using Sliding Window Judgment Based on MWF

Abstract

The invention relates to a noise subspace estimation method based on the application of sliding window determination in MWF. In the method, according to the characteristics of MWF and in combination with the maximum eigenvalue operation of a small matrix, a method for order determination by using a sliding window is provided, so that a noise subspace can be estimated robustly, and an optimal signal to interference plus noise ratio is obtained more stably. By adoption of the method, the interference subspace decomposition can be more accurate and reliable so as to obtain a better anti-interference effect.

Description

technical field [0001] The invention mainly relates to the field of signal processing, in particular to a noise subspace decomposition method using sliding window judgment based on MWF. Background technique [0002] The power of the navigation signal received by the earth's surface is very small, taking GPS as an example, it is about 21dB lower than the thermal noise of the receiver. When subject to man-made interference, the received signal is difficult to recover. Using the antenna array and performing time delay expansion to form a space-time two-dimensional pattern can greatly increase the degree of freedom and enhance the anti-interference ability. Assuming that the number of array elements is M and the number of time delays is N, the received data X is an MN×1-dimensional vector, and its covariance matrix R x =E{XX H}, is an MN×MN dimensional matrix, and its optimal processing operation is about O((MN) 3 ), with the increase of the dimension of space-time processin...

AI Technical Summary

Problems solved by technology

Using the classic MWF to reduce the dimensionality of the space-time two-dimensional pattern anti-interference processing will make it difficult to estimate the optimal subspace dimension accurately. There are two reasons: 1. The noise changes with the environment, and it is difficult to accurately estimate the interference subspace with a fixed threshold Dimension; 2. Because each step of the MWF extracts the interference component according to the maximum correlation with the received signal vector, the variance of the observed data after each step of extraction changes in an L-shape, that is, it drops steeply first, and then drops slowly, resulting in the residual error of the subsequent received signal The difference between variance and white noise variance is not obvious, and it is difficult to find out

In the actual use of classic MWF, the iteration order is easy to exceed the optimum, which will cause a large decrease in the signal-to-noise ratio, about 4-5dB [2 ], so it is necessary to accurately estimate the interference subspace

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