Frequency Diverse Array Signal Optimization: From Non-Cognitive to Cognitive Radar

Abstract

This paper addresses the problem of frequency diverse array (FDA) signal design for target localization in both non-cognitive and cognitive radars. For the non-cognitive case, the Cramr-Rao bound (CRB) for target localization in FDA radar is derived and optimized with respect to the transmit signal parameters. It is shown that FDA transmission introduces coupling between range and direction-of-arrival (DOA) estimation, and that the DOA estimation accuracy can be improved by increasing the signal bandwidth. Since the CRB ignores the threshold phenomenon, we propose to minimize it under a constraint which assures that the steered gain toward the target yields sufficiently large output signal-to-noise ratio (SNR). This method together with the FDA transmission properties establish the basis for developing the proposed cognitive FDA configuration, which is derived in the Bayesian approach. Based on the Bayesian CRB (BCRB) and the expected CRB (ECRB) for target localization, we propose a new criterion called the semi-ECRB (SECRB), and we prove that it is higher than the BCRB and lower than the ECRB. The SECRB is optimized with respect to the FDA signal parameters subject to mean SNR constraint. The target localization performances of the proposed methods in the non-cognitive and cognitive problems are analyzed via simulations and it is shown that they exhibit superior performance in terms of both threshold behavior and estimation accuracy, compared to other transmission methods.