Touhidur Rahman

We address the sparse signal recovery problem in the context of multiple measurement vectors (MMV) when elements in each nonzero row of the solution matrix are temporally correlated. Existing algorithms do not consider such temporal correlation and thus their performance degrades significantly with the correlation. In this paper, we propose a block sparse Bayesian learning framework which models the temporal correlation. We derive two sparse Bayesian learning (SBL) algorithms, which have superior recovery performance compared to existing algorithms, especially in the presence of high temporal correlation. Furthermore, our algorithms are better at handling highly underdetermined problems and require less row-sparsity on the solution matrix. We also provide analysis of the global and local minima of their cost function, and show that the SBL cost function has the very desirable property that the global minimum is at the sparsest solution to the MMV problem. Extensive experiments also provide some interesting results that motivate future theoretical research on the MMV model.

Multiple-input-multiple-output (MIMO) radar is an emerging technology that has significant potential for advancing the state-of-the-art of modern radar. When orthogonal waveforms are transmitted, with M+N (N transmit and M receive) antennas, an MN-element filled virtual array can be obtained. To successfully utilize such an array for high-resolution MIMO radar imaging, constant-modulus transmit signal synthesis and optimal receive filter design play

IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, VOL. 4, NO. 1, FEBRUARY 2010 ... Daniel R. Fuhrmann, Fellow, IEEE, J. Paul Browning, Member, IEEE, and Muralidhar Rangaswamy, Fellow, IEEE ... Abstract—The Hybrid MIMO Phased Array Radar ...

Multiple input multiple output (MIMO) radar can effectively improve radar performance by transmitting specially designed orthogonal signals. A novel hybrid algorithm is presented to numerically optimize such orthogonal code sets. The proposed algorithm integrates a statistical genetic algorithm (GA) with the traditional iterative code selection method. The simulation results show that the proposed algorithm is effective for the design of

To reduce the hardware complexity which is aroused by the limitation of sampling theory because of the use of the frequency-domain method, a wide-band MIMO imaging method based on a symmetrical exponential distribution nonlinear array is proposed. Compared to the uniform linear array, this given exponential distribution nonlinear array has the lower sidelobe level and better imaging performance. Furthermore, this

MIMO radar systems have been proposed elsewhere which utilise OFDM waveforms as the scene illuminator. This suggests an opportunity exists to code the OFDM radar waveform in such a way as to provide a communication link to broadcast the radar data to remote users. The benefit of this would arise from the dual use of the microwave band, addressing the

FAST CODE DESIGN FOR SENSORS IN NONCOOPERATIVE NETWORKS ... a tra-ditional approach is not possible for real-time applications, we propose a new algorithm, referred to as WDNE (Wave-form Design in Noncooperative Environment), to generate a suboptimal ...