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YAN W X , LAN H , WANG Z F , et al . Nonlinear filtering for spaceborne radars based on variational bayes [J ] . Acta Aeronautica et Astronautica Sinica , 2020 , 41 ( S2 ): 724395 . (in Chinese) DOI: 10.7527/S1000-6893.2020.24395 http://doi.org/10.7527/S1000-6893.2020.24395 Spaceborne radars play an important role in early warning defense systems because of their unique advantages such as wide detection range, long distance and all-weather surveillance capability. Due to the high-speed movement of the platform and the strong nonlinear observation function, high-accuracy target tracking for spaceborne radars is difficult. In this paper, we propose a variational Bayes-based nonlinear filtering method, which transforms the nonlinear state estimation problem into an optimization problem. The analytical solution is obtained via a closed-loop iteration manner. Moreover, a pitch angle estimation method is presented using the a priori information of target height. Simulation results show that, compared with the extended Kalman filter, unscented Kalman filter, and the converted measurement Kalman filter, the proposed variational Bayes-based nonlinear filtering method achieves the best estimation accuracy.

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张昀普 , 单甘霖 . 道路约束下多传感器协同地面目标跟踪的管理方法 [J ] . 兵工学报 , 2022 , 43 ( 3 ): 542 - 555 . DOI: 10.12382/bgxb.2021.0122 http://doi.org/10.12382/bgxb.2021.0122 为实现道路约束下地面目标的有效跟踪、控制传感器系统的辐射损失，提出一种多传感器协同管理方法。将传感器管理过程描述为部分可观马尔可夫决策过程，建立道路约束下目标跟踪模型和传感器截获损失模型，给出跟踪精度和截获损失的具体计算方法，并提出一种多普勒盲区下的目标预测状态修正方法；针对高维数下管理方案求取困难的问题，设计了一种莱维飞行- 樽海鞘群算法以快速获得高质量的解。仿真实验结果表明：相比于经典寻优算法，所提算法具有更好的全局搜索能力，能够在缩短寻优时间的同时找到高质量的解；所提管理方法能够有效解决地面目标跟踪问题，既保证了跟踪任务的完成质量，又提高了传感器系统的生存能力。

ZHANG Y P , SHAN G L . Multi-sensor cooperative management for ground target tracking under road constraints [J ] . Acta Armamentarii , 2022 , 43 ( 3 ): 542 - 555 . (in Chinese) DOI: 10.12382/bgxb.2021.0122 http://doi.org/10.12382/bgxb.2021.0122 A multi-sensor cooperative management method is proposed to effectively track the ground target under road constraints and control the radiation loss of the sensor system. The sensor management process is described as a partially observable Markov decision process. A road-constrainted target tracking model and a sensor interception loss model are established, the calculation methods for tracking accuracy and interception loss are presented, and a correction method for target prediction state in Doppler blind zone is proposed. In order to solve the problem that a management scheme is difficultly got when the system state dimension is high, a Levy flight-salp swarm algorithm is designed to obtain a high-quality solution quickly. The simulated results show that the proposed algorithm has better global search capability, and can find high-quality solutions while shortening the optimization time compared with the classic optimization algorithms. The proposed management method can effectively solve the problem of ground target tracking, which not only guarantees the completion quality of the tracking task, but also improves the survivability of the sensor system.

熊光明 , 罗震 , 孙冬 , 等 . 基于红外相机和毫米波雷达融合的烟雾遮挡无人驾驶车辆目标检测与跟踪 [J ] . 兵工学报 , 2024 , 45 ( 3 ): 893 - 906 . DOI: 10.12382/bgxb.2022.0602 http://doi.org/10.12382/bgxb.2022.0602 战场环境下无人驾驶车辆的感知系统易受烟雾、扬尘等天气的影响,对关键目标的检测与跟踪能力大大下降,造成目标漏检、目标误检、目标丢失等严重后果。针对该问题,开发毫米波雷达和红外相机融合系统,采用目标级融合方式建立简洁有效的融合规则,提炼和组合各传感器的优势信息,最终输出稳定的目标感知结果。对毫米波雷达的目标进行有效性检验和提取,并提出改进的基于密度的含噪声空间聚类应用算法,以减少毫米波雷达噪音干扰。以YOLOv4网络为基础,引入MobileNetv2主干网络,在网络训练过程中运用迁移学习方法,同时对红外数据样本进行扩充,解决了红外图像训练样本少的问题。试验结果表明,相较于仅基于红外相机的算法,融合检测算法在烟雾环境下的精度显著提升,且算法实时性高,实现了烟雾环境下毫米波雷达与红外相机融合的目标检测与跟踪,提高了无人驾驶车辆目标检测与跟踪系统的抗烟雾干扰能力。

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