Parametric waveform design for improved target detection

Feng Yin, Christian Debes, Abdelhak M. Zoubir

Abstract: We address a parametric waveform design approach for improved detection of a Gaussian point target, which is embedded in signal-dependent clutter and noise. Unlike canonical waveform design schemes, the proposed transmit waveform is represented as a weighted sum of discrete prolate spheroidal sequences. In the optimization problem, the detection performance is maximized with respect to the weighting factors of the discrete prolate spheroidal sequence under the transmit energy constraint. The main benefit of the proposed approach is the direct acquisition of the optimal waveform rather than its energy spectral density. Simulation results demonstrate the merits and demerits of the parametric waveform design approach in contrast to a canonical approach. Furthermore, the superiority of the discrete prolate spheroidal sequences in parametric waveform modeling is exemplified.