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

Optimized Transmission for Parameter Estimation in Wireless Sensor Networks

A central problem in analog wireless sensor networks is to design the gain or phase-shifts of the sensor nodes (i.e. the relaying configuration) in order to achieve an accurate estimation of some parameter of interest at a fusion center, or more generally, at each node by employing a distributed parameter estimation scheme. In this paper, by using an over-parametrization of the original design problem, we devise a cyclic optimization approach that can handle tuning both gains and phase-shifts of the sensor nodes, even in intricate scenarios involving sensor selection or discrete phase-shifts. Each iteration of the proposed design framework consists of a combination of the Gram-Schmidt process and power method-like iterations, and as a result, enjoys a low computational cost. Along with formulating the design problem for a fusion center, we further present a consensus-based framework for decentralized estimation of deterministic parameters in a distributed network, which results in a similar sensor gain design problem. The numerical results confirm the computational advantage of the suggested approach in comparison with the state-of-the-art methods-an advantage that becomes more pronounced when the sensor network grows large

Optimized Transmission for Parameter Estimation in Wireless Sensor Networks

A central problem in analog wireless sensor networks is to design the gain or phase-shifts of the sensor nodes (i.e. the relaying configuration) in order to achieve an accurate estimation of some parameter of interest at a fusion center, or more generally, at each node by employing a distributed parameter estimation scheme. In this paper, by using an over-parametrization of the original design problem, we devise a cyclic optimization approach that can handle tuning both gains and phase-shifts of the sensor nodes, even in intricate scenarios involving sensor selection or discrete phase-shifts. Each iteration of the proposed design framework consists of a combination of the Gram-Schmidt process and power method-like iterations, and as a result, enjoys a low computational cost. Along with formulating the design problem for a fusion center, we further present a consensus-based framework for decentralized estimation of deterministic parameters in a distributed network, which results in a similar sensor gain design problem. The numerical results confirm the computational advantage of the suggested approach in comparison with the state-of-the-art methods-an advantage that becomes more pronounced when the sensor network grows large