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

Two solutions to the localization using time difference of arrival problem

T. Sathyan ; T. Kirubaraja

Computationally efficient assignment-based algorithms for data association for tracking with angle-only sensors

Also published as: Signal and Data Processing of Small Targets 2007, 28–30 August 2007, San Diego, California, USA / Oliver E. Drummond, Richard D. Teichgraeber (eds.):669901T. Sathyan, A. Sinha and T. Kirubaraja

Joint detection and tracking of unresolved targets with monopulse radar

Detection and estimation of multiple unresolved targets with a monopulse radar is a challenging problem. For ideal single bin processing, it was shown in the literature that at most two unresolved targets can be extracted from the complex matched filter output signal. A new algorithm is developed to jointly detect and track more than two unresolved targets from a single detection with the help of tracking information. That is, the method involves the use of tracking information in the detectionprocess. For this purpose, target states are transformed into detection parameter space, which involves high nonlinearities. In order to handle the nonlinearities, the particle filter, which has proven to be effective in nonlinear non-Gaussian estimation problems, is used as the basis of the closed loop system for tracking multiple unresolved targets. In addition to the standard particle filtering steps, the detection parameters corresponding to the predicted particles are evaluated using the nonlinearmonopulse radar beam model. This in turn enables the evaluation of the likelihood of the monopulse signal given tracking data. Bayesian model selection is then used to find the correct detectionevent. The corresponding particle set is taken as the correct representation of the target posterior. A simulated amplitude comparison monopulse radar is used to generate the data and validate the joint detection and tracking of more than two unresolved targets