Target detection method based on two-dimensional sliding window robust space-time self-adaptive processing

Abstract

The invention provides a target detection method based on two-dimensional sliding window robust space-time self-adaptive processing, which can increase the accuracy of target detection. The target detection method comprises the steps of : step 1, receiving echo data through an airborne radar; step 2, carrying out space-time sliding window processing on the echo data; step 3, establishing a target apparent steering vector error boundary matrix; step 4, calculating an autocorrelation matrix of projectional component of the echo data after space-time sliding window processing on an orthogonal complementary space of a subspace formed by the target; step 5, calculating a correlation matrix after diagonal loading of the autocorrelation matrix; step 6, solving a weight vector; step 7, judging whether a module value of a column vector obtained through multiplying a conjugate transpose matrix of the boundary matrix by the weight vector is less than 1, if so, increasing loading factors and then executing step 5 and step 6, otherwise, regarding the weight vector as the optimal weight vector; and step 8, constructing a filter by utilizing the optimal weight vector so as to filter the echo data after space-time sliding window processing, and acquiring target echo data.

Description

technical field [0001] The invention relates to the technical field of space-time adaptive processing, in particular to a target detection method based on two-dimensional sliding window robust space-time adaptive processing, and the invention is applied to echo signal processing of airborne radar. Background technique [0002] The space-time coupling spectrum characteristic of ground and object echoes of airborne radar determines that the clutter suppression effect of Space-Time Adaptive Processing (STAP) is better than that of one-dimensional clutter suppression technology. The traditional STAP method uses the training data to estimate the space-time correlation matrix of the test data. In a homogeneous environment, the statistical properties of the two sets of data are basically similar. This type of method effectively utilizes the statistical characteristics of the echo and can obtain better clutter suppression performance; however, the actual working environment of airb...

AI Technical Summary

Problems solved by technology

However, in practical applications, the parameter information of the target, that is, the direction of arrival (DOA) or Doppler frequency of the target is often not accurately obtained, and the true steering vector of the target is mismatched.

The 2D-APES method is very sensitive to steering vector mismatch, which leads to the degradation of object detection performance

However, in actual situations, the loading amount is often set according to empirical values, and the uncertainty of the loading amount seriously affects the performance of diagonal loading.

[0005] The second method is the optimal robust method based on the worst performance. This method constrains the target DOA and Doppler frequency within an error cone, and solves the optimal filter weights through a convex optimization method. However, this method cannot gives an analytical solution, and is computationally expensive

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