School of Information Engineering, East China Jiaotong University, Nanchang 330031, China.
School of Information Science and Engineering, Southeast University, Nanjing 210096, China.
Chaos. 2019 Nov;29(11):113104. doi: 10.1063/1.5112073.
A novel scheme to optimize the adaptive transmit waveform of chaotic multiple-input multiple-output (MIMO) radar is developed. The main objective of this work is to achieve high ability in target discrimination by using a Dirichlet process mixture model (DPMM)-based clustering method based on nonparametric Bayesian theory and to improve the capability of target detection by minimizing the mean square error of radar channel response via Kalman filtering (KF) technique. The two stages are the discrimination of multiple range-extended targets and the optimization of the adaptive chaos-based waveform for transmission. The adaptive chaotic MIMO waveform optimization scheme overcomes the problem of target discrimination and detection in an intelligent transportation system, where there is a need for extracting the feature of target information achieved from vehicle-mounted sensor. As the number of iterations increases, simulation experiments demonstrate better target discrimination capability provided by the proposed DPMM-KF technique as compared with the traditional waveform design method. In addition, the proposed DPMM-KF technique leads to improved target detection probability and receiver operating characteristics in the interference environment.
提出了一种新的方案，用于优化混沌多输入多输出（MIMO）雷达的自适应发射波形。这项工作的主要目标是通过使用基于非参数贝叶斯理论的 Dirichlet 过程混合模型（DPMM）聚类方法实现高目标分辨能力，并通过卡尔曼滤波（KF）技术最小化雷达通道响应的均方误差来提高目标检测能力。这两个阶段分别是对多个距离扩展目标的区分和对基于混沌的自适应发射波形的优化。自适应混沌 MIMO 波形优化方案克服了智能交通系统中目标识别和检测的问题，该系统需要从车载传感器中提取目标信息的特征。随着迭代次数的增加，仿真实验表明，与传统的波形设计方法相比，所提出的 DPMM-KF 技术具有更好的目标分辨能力。此外，在干扰环境下，所提出的 DPMM-KF 技术提高了目标检测概率和接收机工作特性。
