17 research outputs found
Complexity Reduction in Beamforming of Uniform Array Antennas for MIMO Radars
Covariance matrix design and beamforming in
multiple-input multiple-output (MIMO) radar systems have
always been a time-consuming task with a substantial number of unknown variables in the optimization problem to be solved. Based on the radar and target conditions, beamforming can be a dynamic process and in real-time scenarios, it is critical to have a fast beamforming. In this paper, we propose a beampattern matching design technique that is much faster compared to the well-known traditional semidefinite quadratic programming (SQP) counterpart. We show how to calculate the covariance matrix of the probing transmitted signal to obtain the MIMO radar desired beampattern, using a facilitator library. While the proposed technique inherently satisfies the required practical constraints in covariance matrix design, it significantly reduces the number of unknown variables used in the minimum square error (MSE) optimization problem, and therefore reduces the computational complexity considerably. Simulation results show the superiority of the proposed technique in terms of complexity and speed, compared with existing methods. This superiority is enhanced by increasing the number of antennas
Multi-Spectrally Constrained Low-PAPR Waveform Optimization for MIMO Radar Space-Time Adaptive Processing
This paper focuses on the joint design of transmit waveforms and receive
filters for airborne multiple-input-multiple-output (MIMO) radar systems in
spectrally crowded environments. The purpose is to maximize the output
signal-to-interference-plus-noise-ratio (SINR) in the presence of
signal-dependent clutter. To improve the practicability of the radar waveforms,
both a multi-spectral constraint and a peak-to-average-power ratio (PAPR)
constraint are imposed. A cyclic method is derived to iteratively optimize the
transmit waveforms and receive filters. In particular, to tackle the
encountered non-convex constrained fractional programming in designing the
waveforms (for fixed filters), we resort to the Dinkelbach's transform,
minorization-maximization (MM), and leverage the alternating direction method
of multipliers (ADMM). We highlight that the proposed algorithm can iterate
from an infeasible initial point and the waveforms at convergence not only
satisfy the stringent constraints, but also attain superior performance
Joint Optimization of Waveform Covariance Matrix and Antenna Selection for MIMO Radar
In this paper, we investigate the problem of jointly optimizing the waveform
covariance matrix and the antenna position vector for
multiple-input-multiple-output (MIMO) radar systems to approximate a desired
transmit beampattern as well as to minimize the cross-correlation of the
received signals reflected back from the targets. We formulate the problem as a
non-convex program and then propose a cyclic optimization approach to
efficiently tackle the problem. We further propose a novel local optimization
framework in order to efficiently design the corresponding antenna positions.
Our numerical investigations demonstrate a good performance both in terms of
accuracy and computational complexity, making the proposed framework a good
candidate for real-time radar signal processing applications.Comment: This paper is accepted for publication in the 2019 IEEE Asilomar
Conference on Signals, Systems, and Computers (Asilomar 2019
STAR-RIS-Enabled Secure Dual-Functional Radar-Communications: Joint Waveform and Reflective Beamforming Optimization
Considering a simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS)-aided dual-functional radar-communications (DFRC) system, this paper proposes a symbol-level precoding-based scheme for concurrent securing confidential information transmission and performing target sensing, where the public signals intended for multiple unclassified users are exploited to deceive the multiple potential malicious radar targets. Specifically, the STAR-RIS-aided DFRC system design is formulated as a joint optimization problem that determines the transmission waveform signal, the transmission and reflection coefficients of STAR-RIS. The objective is to maximize the average received radar sensing power subject to the quality-of-service constraints for multiple communication users, the security constraint for multiple potential eavesdroppers, as well as various practical waveform design restrictions. However, the formulated problem is challenging to handle due to its nonconvexity. Furthermore, the high dimensionality of the optimization variables also renders existing optimization algorithms inefficient. To address these issues, we propose a distance-majorization induced low-complexity algorithm to obtain an efficient solution, which converts the nonconvex joint design problem into a sequence of subproblems that can be solved in closed-form, relieving the required high computational burden of the conventional approaches, e.g., the interior point method. Simulation results confirm the effectiveness of the STAR-RIS in improving the DFRC performance. Besides, by comparing with the state-of-the-art alternating direction method of multipliers (ADMM) algorithm, simulation results validate the efficiency of our proposed optimization algorithm and show that it enjoys excellent scalability for different number of T-R elements equipped at the STAR-RIS