Optimal Radar Waveform Design for Improved Target Range Resolution and Tracking Performance in a Cooperative Radar-Communication System

Abstract

The widespread usage of the Radio Frequency (RF) spectrum for wireless and mobile communication systems generated a significant spectrum scarcity. To tackle this issue, the spectral cooperation between radar-sensing and communication systems research has received a surge of interest in recent times. The Cooperative Radar- Communication System (CRCS) provides a framework to simultaneously utilize the allocated radar spectrum for both sensing and communication purposes. In addition, waveform design plays a vital role in the development of various configurations of a CRCS. Based on the literature survey, most approaches used to design a radar waveform are locally optimum. In addition, designing a globally optimized radar waveform for a cooperative scenario has been a challenging task for accomplishing the convergence of radar-sensing and communication functionalities without degrading the performance at either end. To overcome this challenge, the first objective of this research investigation proposes a novel global optimization-based Spatial Branch and Bound (SBnB) approach to optimize the phase coefficients of a Non-Linear Frequency Modulated (NLFM) radar waveform in a CRCS framework. The simulation results reveal that the proposed SBnB radar waveform design approach provides improved performance compared to the existing radar waveform design approaches. Further, the state estimation-based optimal radar waveform design for improving tracking performance is hardly reported in the CRCS framework. Thus, the second objective of this research work proposes a novel measurement model, based on communication residual components for various waveforms, using the Fisher Information Matrix (FIM) to evaluate the radar system performance in the CRCS framework. The radar waveforms considered in this investigation are rectangular pulse, triangular pulse, Gaussian pulse, Linear Frequency Modulated (LFM) pulse, LFM-Gaussian pulse, and Non-Linear Frequency Modulated (NLFM) pulse. Thereafter, the Kalman filter is deployed to estimate the target kinematics (range and range rate) of a single linearly moving target for the aforementioned radar waveforms. In the simulated results, the range and range rate estimation errors are quantified by the Root Mean i Square Error (RMSE) and they are validated with the Posterior Cramer-Rao Lower Bound (PCRLB). Furthermore, another contribution reported in this thesis is to exploit the communication waveform along with the radar waveform to improve the target state estimation performance in a CRCS. An LFM pulse radar waveform, an NLFM pulse radar waveform, and a Quadrature Amplitude Modulated (QAM) communication waveform are considered for this investigation and analyzed the target state estimation performance. At a given epoch, the target position is estimated by considering the range and range rate as a measurement in an Iterative Least Squares (ILS) framework. After that, the Kalman Filter (KF) estimates the target dynamics by taking the output of the ILS as a measurement model. Besides, the target estimated position is quantified with the RMSE and they are validated with the PCRLB. Overall the results attained in this research work signify the importance of radar waveform optimization in a CRCS. In addition, the superiority of the optimized NLFM radar waveform is very well exhibited in terms of range resolution, radar estimation rate, and target state estimation performance. Moreover, the major contributions done in this thesis added profound knowledge in the radar waveform optimization, and target tracking areas.