An MTD Algorithm Based on Fuzzy Mathematics and Sparse Recovery for Airborne Radar

The performance of moving target detection for airborne radar depends on estimation accuracy of clutter covariance matrix. In nonhomogeneous clutter environments, dense interference will impact the estimation accuracy of the clutter covariance matrix, resulting in failure of moving target detection. To cope with this problem, this paper proposes a moving target detection algorithm for airborne radar based on fuzzy mathematics and signal sparse recovery technology. The proposed algorithm calculates the sparse recovery coefficient vectors of training snapshots firstly, and then uses the appropriate membership function to process the matrix which is consist of these vectors. The clutter components in coefficient vector are selected according to membership function, and clutter covariance matrix are estimation subsequently. By comparing and analyzing clutter space-time spectrum, clutter suppression performance, and moving target detection performance, it can be found that the proposed algorithm improves the moving target detection performance. Moreover, the proposed algorithm only needs few training snapshots, is robust to the suppression of dense interference.