A time-varying angle extraction method for refined proximity group targets tracking

In order to improve the detection probability of weak targets, tracking radar using sum and difference beams often adopt the method of long-time coherent integration. However, the multidimensional migration of time-varying targets will lead to the decline of parameter estimation accuracy. To solve this problem, this article proposes a refined angle estimation method for time-varying targets with the traditional sum and difference beam echo model, this method compensates and searches the angle parameters of the targets based on subarray rotation invariant and focus process. In addition, this article also studies the masking problem of highly dynamic proximity group targets detection, and proposes an adaptive weighted LMS-CLEAN based on Least Mean Square criterion, which effectively reduces the influence of masking effect on the parameter estimation accuracy of weak targets. Firstly, the proposed algorithm performs angle search and phase compensation on the pulse compression echo of sum and difference channels based on subarray rotation invariant. Secondly, focus the search matrix, reconstruct the strong target echo, and stripe it from both channels by adaptive weighting. Lastly, repeat the above steps until parameters of all targets are achieved precisely. The proposed two algorithms maintain a very low computational effort while effectively reducing the parameter estimation error, and are highly promising for engineering applications. In order to verify the effectiveness of the proposed algorithm, this article also provides some numerical experiments to compares with two existing algorithms in error performance, anti-noise performance, and computational complexity.