Multi-objective optimized OFDM radar waveform for target detection in multipath scenarios

We propose a multi-objective optimization (MOO) technique to design an orthogonal frequency division multiplexing (OFDM) radar signal for detecting a moving target in the presence of multipath reflections. We employ an OFDM signal to increase the frequency diversity of the system. Moreover, the multipath propagation increase the spatial diversity by providing extra “looks” at the target. First, we develop a parametric measurement model by reformulating the target detection problem as a sparse estimation method. At a particular range cell, we exploit the sparsity of multiple paths and the knowledge of the environment to estimate along which path the target responses are received. Then, we formulate a constrained MOO algorithm, to optimally design the spectral parameters of the OFDM waveform, by simultaneously optimizing two objective functions: minimizing the upper bound on the estimation error to improve the efficiency of sparse-recovery and maximizing the squared Mahalanobis-distance to increase the performance of the underlying detection problem. We present numerical examples to demonstrate the performance improvement due to adaptive waveform design.