PAPR-Constrained Pareto-Optimal Waveform Design for OFDM-STAP Radar

We propose a peak-to-average power ratio (PAPR)-constrained Pareto-optimal waveform-design approach for an orthogonal frequency division multiplexing (OFDM) radar signal to detect a target using the space-time adaptive processing (STAP) technique. The use of an OFDM signal does not only increase the frequency diversity of our system but also enable us to adaptively design the OFDM coefficients in order to further improve the system performance. First, we develop a parametric OFDM-STAP measurement model by considering the effects of signal-dependent clutter and colored noise. Then, we observe that the resulting STAP performance can be improved by maximizing the output signal-to-interference-plus-noise ratio (SINR) with respect to the signal parameters. However, in practical scenarios, the computation of output SINR depends on the estimated values of the spatial and temporal frequencies and target-scattering responses. Therefore, we formulate a PAPR-constrained multiobjective-optimization problem to design the OFDM spectral parameters by simultaneously optimizing four objective functions: maximizing the output SINR, minimizing two separate Cramér-Rao bounds (CRBs) on the normalized spatial and temporal frequencies, and minimizing the trace of the CRB matrix on the target-scattering coefficient estimations. We present several numerical examples to demonstrate the achieved performance improvement due to the adaptive waveform design.