为弥补水下运载器(AUV,Autonomous Underwater Vehicle)中传统舵面控制机构的低速控制的不足,改善其操纵性能,引入单框架控制力矩陀螺(SGCMG,Single Gimbal Control Moment Gyro)作为控制机构进行姿态稳定与控制.把AUV简化为刚体,加入SGCMG,考虑水下环境的特点,建立基于SGCMG的AUV动力学模型,并仿真分析AUV的动力学、姿态运动、SGCMG的框架运动以及环境之间的相互作用.仿真结果说明:基于SGCMG控制的AUV的姿态机动快速、准确,低速性能理想,为操纵律设计及姿态控制算法研究提供基础. 相似文献
In this paper, we present a novel and efficient track-before-detect (TBD) algorithm based on multiple-model probability hypothesis density (MM-PHD) for tracking infrared maneuvering dim multi-target. Firstly, the standard sequential Monte Carlo probability hypothesis density (SMC-PHD) TBD-based algorithm is introduced and sequentially improved by the adaptive process noise and the importance re-sampling on particle likelihood, which result in the improvement in the algorithm robustness and convergence speed. Secondly, backward recursion of SMC-PHD is derived in order to ameliorate the tracking performance especially at the time of the multi-target arising. Finally, SMC-PHD is extended with multiple-model to track maneuvering dim multi-target. Extensive experiments have proved the efficiency of the presented algorithm in tracking infrared maneuvering dim multi-target, which produces better performance in track detection and tracking than other TBD-based algorithms including SMC-PHD, multiple-model particle filter (MM-PF), histogram probability multi-hypothesis tracking (H-PMHT) and Viterbi-like. 相似文献