Optimal Antenna Allocation in MIMO Radars with Collocated Antennas

This paper concerns with the sensor management problem in collocated Multiple-Input Multiple-Output (MIMO) radars. After deriving the Cramer-Rao Lower Bound (CRLB) as a performance measure, the antenna allocation problem is formulated as a standard Semi-definite Programming (SDP) for the single-target case. In addition, for multiple unresolved target scenarios, a sampling-based algorithm is proposed to deal with the non-convexity of the cost function. Simulations confirm the superiority of the localization results under the optimal structure.Comment: Submitted to IEEE Transactions on Aerospace and Electronic System

Antenna Array Design in MIMO Radar Using NSK Polynomial Factorization Algorithm

The work presented here is concerned with the antenna array design in collocated multiple-input multiple-output (MIMO) radars. After knowing the system requirements, the antenna array design problem is formulated as a standard polynomial factorization. In addition, an algorithm based on Newton-Schubert-Kronecker (NSK) polynomial factorization is proposed. The algorithm contains three steps. First, linear factors are extracted by extended Vieta theorem. Then, undermined high-order factors are confirmed with Newton interpolation and certain high-order factors should be searched for within the undermined ones. Finally, the antenna array configurations are determined according to the result of polynomial factorization. Simulations confirm the wide use of the proposed algorithm in MIMO radar antenna array design

Reconfigurable Linear Antenna Arrays for Beam-Pattern Matching in Collocated MIMO Radars

Beam-pattern matching plays an important role in multiple-input multiple-output radars. In the vast majority of research done in this area, the aim is to find the covariance matrix of the waveforms fed into the transmit array. Also, reconfiguring a preset array of antennas (antenna selection), which means turning off some of the antennas in the array, is an effective technique to reach the desired beam patterns, dynamically. In this article, we introduce a novel multistep method to implement this reconfiguration technique to a uniform linear array. In each step, by exploiting the relation between the diagonal elements of a covariance matrix resulted from solving a beam-pattern matching problem and the transmitted power of the antennas, we find the least important antenna of the array and turn it off accordingly. Then, we repeat this process until a predefined number of antennas remains. Our proposed method outperforms its counterparts in the literature in terms of beam-pattern matching as well as computational complexity, which makes it an appropriate method for real-time applications. Simulations are used to show the validity and superiority of the proposed method