Cognitive Design of Radar Waveform and the Receive Filter for Multitarget Parameter Estimation

Abstract

This research work considers waveform design for an adaptive radar system. The aim is to achieve enhanced feature extraction performance for multiple extended targets. There are two scenarios to consider: multiple extended targets separated in range and multiple extended targets close in range. We propose a waveform optimization scheme based on Kalman filtering by minimizing the mean square error of separated target scattering coefficient estimation and a waveform optimization approach by minimizing the mean square error of closed power spectrum density estimation. A convex cost function is established, and the optimal solution can be obtained using the existing convex programming algorithm. With subsequent iterations of the algorithm, the simulation results demonstrate an improvement in the estimation of target parameters from the dynamic scene, such as target scattering coefficient and power spectrum density, while maintaining relatively lower computational complexity.

Cognitive Waveform Optimization for Phase-Modulation-Based Joint Radar-Communications System

“…However, the target detection performance is degraded due to the waveform variation from pulse to pulse. Communication data embedding schemes were also proposed for dual functionality with multiple transmit/receive frameworks [9], [19]- [21]. A dual-function radar-communication (DFRC) system that employs sidelobe control and waveform diversity has been presented in [22].…”

“…Most recently proposed DFRC systems are based on phased arrays and the multiple-input multiple-output (MIMO) technique [23]- [29]. Time-modulated arrays are used to realize dual functionality to enable target detection in the main lobe while performing wireless communication in the sidelobe in [19]. The main strategy of the approach is to use sparse time-modulated array or phase-only synthesis time-modulated array approaches to control the instantaneous pattern.…”

“…The main strategy of the approach is to use sparse time-modulated array or phase-only synthesis time-modulated array approaches to control the instantaneous pattern. Both methods enable the excitation of variations in the sidelobe levels (SLLs) toward a specified direction [19]. However, since the number of transmission antennas is constant, the former affords only a few degrees of freedom.…”