A Subspace Hybrid Integration Method for High-Speed and Maneuvering Target Detection

Long-time integration is an effective method to improve target detection performance in a noisy background. However, when detecting high-speed and maneuvering targets by long-time integration, it is easy to encounter the across range unit and across Doppler unit effects, which deteriorate the detection performance of algorithms with low computational complexity, such as moving target detection and hybrid integration (HI). The generalized Radon-Fourier transform (GRFT) detector has proven to have the best detection performance. However, the GRFT has a high computational burden for an ergodic search in a multidimensional motion parameter space and thus is hardly employed in real engineering applications. In this paper, we propose subspace HI (SHI) to achieve a good balance between detection performance and computational complexity. SHI first divides the parameter space into several equidimensional subspaces and moves them to the center of the coordinate system. Then, HI is implemented on all the subspaces, and all the HI results are finally fused. Through parameter space division and subspace movement, the values of the parameters in the subspaces are reduced, which increases the subaperture length, i.e., the time that the target echoes stay in one range-Doppler unit. By increasing the subaperture length, SHI gradually improves the detection performance with an increase in the computational complexity. Conversely, by shortening the subaperture length, the computational complexity of SHI can be reduced at the expense of the detection performance. Compared with HI and the GRFT, SHI achieves a better compromise between detection performance and computational complexity.