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In this paper, we design constant modulus probing waveforms with low correlation sidelobes for colocated multi-input multi-output (MIMO) radar. Through exploiting the structure of the problem, we formulate it as a non-convex consensus minimization problem. Then a customized alternating direction method of multipliers (ADMM) algorithm is proposed to solve the problem, which is guaranteed convergent to its stationary point. Numerical examples show that the proposed approach offers better performance than the state-of-the-art approaches.

Array spatial thinning is employed to select the most effective antenna elements in a large phased array for optimum performance concerning hardware and computational costs, in conjunction with managing element failure and radio interference mitigation. We formulate spatial array thinning under connectivity constraints to make the thinning applicable in large arrays. By introducing graph optimization, the problem is recast as a k-clique version of a generalized minimum clique problem.

A new method for designing single/multiple unimodular waveforms with good weighted correlation properties, which is

This work investigates the parameter estimation performance of super-resolution line spectral estimation using atomic norm minimization. The focus is on analyzing the algorithm's accuracy of inferring the frequencies and complex magnitudes from noisy observations. When the Signal-to-Noise Ratio is reasonably high and the true frequencies are separated by $O(\frac{1}{n})$, the atomic norm estimator is shown to localize the correct number of frequencies, each within a neighborhood of size $O(\sqrt{\frac{\log n}{n^3}} \sigma)$ of one of the true frequencies.

This work investigates the parameter estimation performance of super-resolution line spectral estimation using atomic norm minimization. The focus is on analyzing the algorithm's accuracy of inferring the frequencies and complex magnitudes from noisy observations. When the Signal-to-Noise Ratio is reasonably high and the true frequencies are separated by $O(\frac{1}{n})$, the atomic norm estimator is shown to localize the correct number of frequencies, each within a neighborhood of size $O(\sqrt{\frac{\log n}{n^3}} \sigma)$ of one of the true frequencies.