An improved track-before-detect strategy for STAP radars

The knowledge of the operating environment (whether scan-to-scan varying or stationary) has a significant impact on the performance of the original adaptive track-before-detect (TBD) strategy for space-time adaptive processing (STAP) radars. If the knowledge assumption is not met, the performance degrades dramatically due to the mismatch between the true covariance matrix and the estimated one. To deal with this problem, an improved adaptive TBD strategy is proposed in this paper, which reduces the impact of data contaminated by signal or interferer components on the estimation of covariance matrix and thus improves the performance. Computational complexity and performance of the proposed adaptive TBD strategy are analyzed and compared with the original adaptive TBD strategy.