| 要約: | Aiming at maximizing waveform diversity gain when designing a phase-coded multiple-input multiple-output (MIMO) radar waveform set, it is desirable that all waveforms are orthogonal to each other. Hence, the lowest possible peak cross-correlation ratio (<i>PCCR</i>) is expected. Meanwhile, low peak auto-correlation side-lobe ratio (<i>PASR</i>) is needed for good detection performance. However, it is difficult to obtain a closed form solution to the waveform set from the expected values of the <i>PASR</i> and <i>PCCR</i>. In this paper, the waveform set design problem is modeled as a multi-objective, NP-hard constrained optimization problem. Unlike conventional approaches that design the waveform set through optimizing a weighted sum objective function, the proposed optimization model evaluates the performance of multi-objective functions based on Pareto level and obtains a set of Pareto non-dominated solutions. That means that the MIMO radar system can trade off each objective function for different requirements. To solve this problem, this paper presents a multi-objective quantum genetic algorithm (MoQGA) based on the framework of quantum genetic algorithm. A new population update strategy for the MoQGA is designed based on the proposed model. Compared to the state-of-the-art methods, like BiST and Multi-CAN, the <i>PASR</i> and <i>PCCR</i> metrics of the waveform set are 0.95–3.91 dB lower with the parameters of the numerical simulation. The MoQGA is able to minimize <i>PASR</i> and <i>PCCR</i> of the MIMO radar waveform set simultaneously.
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