Waveform Design for Target Information Maximization over a Complex Circle Manifold

Abstract

The cognitive radar framework presents a closed-loop adaptive processing paradigm that ensures the efficient acquisition of target information while exploring the environment and enhancing overall sensing performance. In this study, instead of mutual information, we employed the squared Pearson correlation coefficient (SPCC) to measure the target information in observations specifically considering only linear dependency. A waveform design method is proposed that simultaneously maximizes target information and minimizes the integrated sidelobe level (ISL) under the constant modulus constraint (CMC). To enhance computational efficiency, we reformulated the constrained problem as an unconstrained optimization problem by leveraging the inherent geometric property of CMC. Additionally, we present two conditional equivalences associated with waveform design in relation to target information. The simulation results validate the feasibility and effectiveness of the proposed method.