LAUSR

Radar system design for the detection of targets with unknown actual range in spectrally crowded environments is a challenge due to increasing demands for spectrum and the possibly experienced echo eclipsing. Assuming unknown target echo delay, this article deals with robust joint design of the radar code and receive filter bank guaranteeing spectral compatibility with the surrounding electromagnetic radiators. The average signal-to-interference-plus-noise ratio (SINR) at the output of the filter bank is considered as the performance measure to optimize under both similarity and spectral compatibility constraints on the radar code. Through an appropriate reformulation of the resulting nonconvex optimization problem, we devise an alternate optimization algorithm, which sequentially improves the average SINR and converges to a point fulfilling the Karush–Kuhn–Tucker (KKT) conditions of the design problem. Each iteration of the algorithm involves both a convex and a hidden convex optimization problem, which can be solved via the rank-1 decomposition procedure. Finally, the performance is assessed by studying the tradeoff among the SINR, spectral shape, and autocorrelation. Numerical results reveal that the new method has better flexibility and robustness compared to several counterparts, which are presented in open literature.