A Dimensionality-reduced Subarray Comparison Phase Tracking Angle Measurement Method for Anti-mainlobe Interference

Abstract

The invention discloses a dimensionality reduction sub-array comparison phase tracking angle measurement method for anti-mainlobe interference. The main idea is: determine the plane array, and there is a target in the detection range of the plane array, and divide the plane array into L 1 × L 2 dimension subarray matrix, the L 1 × L 2 Dimensional subarray matrix including L 1 × L 2 subarray; for the L 1 × L 2 The dimension sub-array matrix is ​​divided to obtain the top sub-array, bottom sub-array, left sub-array and right sub-array; the optimal weight of the top sub-array and the optimal weight of the left sub-array are calculated respectively, and then the output results of the top sub-array, The output result of the bottom subarray, the output result of the left subarray and the output result of the right subarray; calculate the top subarray and the bottom subarray according to the output result of the top subarray, the output result of the bottom subarray, the output result of the left subarray, and the output result of the right subarray The phase difference between the arrays and the phase difference between the left sub-array and the right sub-array; then calculate the target elevation angle and target azimuth angle, and the target elevation angle and target azimuth angle are a kind of anti-main lobe interference Dimensionality reduction sub-array comparison phase tracking angle measurement results.

Description

technical field [0001] The invention belongs to the technical field of radar array signal processing, in particular to a dimensionality-reducing sub-array comparison phase tracking angle measurement method for resisting main lobe interference, which is suitable for practical engineering applications. Background technique [0002] Large-scale phased array radar has the advantages of high gain, long coverage distance, high resolution, and multi-target tracking, and is widely used in target tracking and direction finding; but at the same time, due to the large number of array elements, it can directly realize adaptive beamforming, Not only the hardware cost is high and the implementation is complex, but also the real-time requirements of radar cannot be met due to the large amount of calculation, so dimensionality reduction processing is required. [0003] Due to the low complexity of the sum-difference tracking and direction-finding algorithm, it is easy to implement on hardwa...

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Problems solved by technology

[0002] Large-scale phased array radar has the advantages of high gain, long coverage distance, high resolution, and multi-target tracking, and is widely used in target tracking and direction finding; but at the same time, due to the large number of array elements, it can directly realize adaptive beamforming, Not only the hardware cost is high and the implementation is complicated, but also the real-time requirements of the radar cannot be met due to the large amount of calculation, so dimensionality reduction processing is required

[0003] Due to the low complexity of the sum-difference tracking and direction-finding algorithm, it is easy to implement on hardware. At present, large-scale phased array radars often use the sum-difference tracking method to track targets. In the interference environment, adaptive beamforming technology is usually required to achieve The interference is suppressed, but when the interference is located in the main lobe, the adaptive notch will distort the main beam of the pattern, and the performance of the sum-difference tracking direction-finding algorithm is heavily dependent on the beam shape of the pattern, so When there is main lobe interference, the ordinary sum-difference tracking and direction finding algorithm is not effective; in order to solve the problem of the tracking accuracy drop caused by the distortion of the direction map when there is main lobe interference, it is necessary to carry out the main lobe shape preservation, but this increases the algorithm the complexity

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