针对粒子滤波计算量大、硬件实现困难的问题,提出了一种用于纯方位跟踪的简化粒子滤波算法,并通过Xilinx System Generator在FPGA上实现。首先,对通用粒子滤波算法进行适当简化,使其减少计算量并且易于硬件实现;其次,采用模块化设计,利...针对粒子滤波计算量大、硬件实现困难的问题,提出了一种用于纯方位跟踪的简化粒子滤波算法,并通过Xilinx System Generator在FPGA上实现。首先,对通用粒子滤波算法进行适当简化,使其减少计算量并且易于硬件实现;其次,采用模块化设计,利用状态机综合并实现各个模块的时序控制;最后,转换为硬件语言,完成硬件仿真。仿真结果表明,所设计的简化粒子滤波算法各个模块工作正常,且具有较好的跟踪精度及运行速度,可用于非线性、非高斯系统的粒子滤波实现,对于粒子滤波的硬件实现方面具有一定的参考价值。展开更多
According to the biased angles provided by the bistatic sensors, the necessary condition of observability and Cramer-Rao low bounds for the bistatic system are derived and analyzed, respectively. Additionally, a dual ...According to the biased angles provided by the bistatic sensors, the necessary condition of observability and Cramer-Rao low bounds for the bistatic system are derived and analyzed, respectively. Additionally, a dual Kalman filter method is presented with the purpose of eliminating the effect of biased angles on the state variable estimation. Finally, Monte-Carlo simulations are conducted in the observable scenario. Simulation results show that the proposed theory holds true, and the dual Kalman filter method can estimate state variable and biased angles simultaneously. Furthermore, the estimated results can achieve their Cramer-Rao tow bounds.展开更多
文摘针对粒子滤波计算量大、硬件实现困难的问题,提出了一种用于纯方位跟踪的简化粒子滤波算法,并通过Xilinx System Generator在FPGA上实现。首先,对通用粒子滤波算法进行适当简化,使其减少计算量并且易于硬件实现;其次,采用模块化设计,利用状态机综合并实现各个模块的时序控制;最后,转换为硬件语言,完成硬件仿真。仿真结果表明,所设计的简化粒子滤波算法各个模块工作正常,且具有较好的跟踪精度及运行速度,可用于非线性、非高斯系统的粒子滤波实现,对于粒子滤波的硬件实现方面具有一定的参考价值。
基金the Natural Science Foundation of Jiangsu Province, China (BK2004132).
文摘According to the biased angles provided by the bistatic sensors, the necessary condition of observability and Cramer-Rao low bounds for the bistatic system are derived and analyzed, respectively. Additionally, a dual Kalman filter method is presented with the purpose of eliminating the effect of biased angles on the state variable estimation. Finally, Monte-Carlo simulations are conducted in the observable scenario. Simulation results show that the proposed theory holds true, and the dual Kalman filter method can estimate state variable and biased angles simultaneously. Furthermore, the estimated results can achieve their Cramer-Rao tow bounds.
