New algorithms for designing unimodular sequences with good correlation properties
IEEE Transactions on Signal Processing
MIMO radar waveform optimization with prior information of the extended target and clutter
IEEE Transactions on Signal Processing
Designing unimodular sequence sets with good correlations: including an application to MIMO radar
IEEE Transactions on Signal Processing
MIMO radar waveform design via alternating projection
IEEE Transactions on Signal Processing
Phased-MIMO radar: a tradeoff between phased-array and MIMO radars
IEEE Transactions on Signal Processing
Construction of unimodular sequence sets for periodic correlations
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Waveform design for MIMO radar using an alternating projection approach
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Reducing the waveform cross correlation of MIMO radar with space: time coding
IEEE Transactions on Signal Processing
MIMO radar waveform design in colored noise based on information theory
IEEE Transactions on Signal Processing
Receiver design for MIMO radar range sidelobes suppression
IEEE Transactions on Signal Processing
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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 critical roles. We present in this paper a computationally attractive cyclic optimization algorithm for the synthesis of constant-modulus transmit signals with good auto- and cross-correlation properties. Then we go on to discuss the use of an instrumental variables approach to design receive filters that can be used to minimize the impact of scatterers in nearby range bins on the received signals from the range bin of interest (the so-called range compression problem). Finally, we present a number of numerical examples to demonstrate the effectiveness of the proposed approaches.