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
Waveform design for MIMO radar using an alternating projection approach
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
MIMO radar waveform design in colored noise based on information theory
IEEE Transactions on Signal Processing
On parameter identifiability of MIMO radar with waveform diversity
Signal Processing
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Transmit beampattern design is a critically important task in many fields including defense and homeland security as well as biomedical applications. Flexible transmit beampattern designs can be achieved by exploiting the waveform diversity offered by an array of sensors that transmit probing signals chosen at will. Unlike a standard phased-array, which transmits scaled versions of a single waveform, a waveform diversity-based system offers the flexibility of choosing how the different probing signals are correlated with one another. Recently proposed techniques for waveform diversity-based transmit beampattern design have focused on the optimization of the covariance matrix of the waveforms, as optimizing a performance metric directly with respect to the waveform matrix is a more complicated operation. Given an , obtained in a previous optimization stage or simply pre-specified, the problem becomes that of determining a signal waveform matrix whose covariance matrix is equal or close to , and which also satisfies some practically motivated constraints (such as constant-modulus or low peak-to-average-power ratio constraints). We propose a cyclic optimization algorithm for the synthesis of such an , which (approximately) realizes a given optimal covariance matrix under various practical constraints. A numerical example is presented to demonstrate the effectiveness of the proposed algorithm.