Joint Design of Transmit and Receive Weights for Subarrayed FDA With Partial Prior Knowledge Using Approximated Consensus-ADMM

Abstract

The distinguishing feature of frequency diverse array (FDA) systems as compared to conventional phased-array and multiple-input–multiple-output (MIMO) radar systems is the use of a small frequency offset (FO) across the array elements. Much of the development to date has focused on the FDA-MIMO structure, using an FO that is larger than the bandwidth of the baseband signal, thereby reducing the resulting transmission gain. In this article, we propose a subarrayed FDA structure that arranges the FDA-MIMO system into multiple subarrays, each of which can be treated as a phased-array transmitting unique waveforms and weights, thus offering controllable degrees of freedom in the range dimension and a coherent gain on the transmission side. Moreover, unlike existing FDA processing schemes, which implement receive beamforming in the range and angle domains to suppress mainlobe interference based on prior information detailing the precise target and interference, we investigate the problem of jointly designing the transmit and receive weights for the subarrayed FDA given partial prior knowledge. The resulting problem is formulated as a nonconvex optimization problem subject to energy and similarity constraints with the objective of maximizing the signal-to-interference-plus-noise ratio. To this end, we devise an approximated consensus alternating direction method of multipliers algorithm that yields closed-form solutions during the iterative process, for which we provide a weak convergence proof. Numerical simulations demonstrate the advantages of the proposed subarrayed FDA over conventional FDA, phased-array, and MIMO as well as the effectiveness of the developed algorithm.