基于EP2C35芯片设计了无线传感网络(WSN)数字基带成形滤波器,主要通过根升余弦成形滤波器的参数分析、抽头系数提取、系统结构设计与改进、系统功能与时序仿真和FPGA综合调试等过程实现。研究结果表明,该成形滤波器滚降系数为0.3时,通...基于EP2C35芯片设计了无线传感网络(WSN)数字基带成形滤波器,主要通过根升余弦成形滤波器的参数分析、抽头系数提取、系统结构设计与改进、系统功能与时序仿真和FPGA综合调试等过程实现。研究结果表明,该成形滤波器滚降系数为0.3时,通带内衰减小于0.5 d B,阻带衰减40 d B/Dec,时钟频率215 MHz,消耗288个逻辑单元,其资源消耗低,运算速度快,成本低廉,具有一定应用价值。展开更多
Mobile robot systems performing simultaneous localization and mapping(SLAM) are generally plagued by non-Gaussian noise.To improve both accuracy and robustness under non-Gaussian measurement noise,a robust SLAM algori...Mobile robot systems performing simultaneous localization and mapping(SLAM) are generally plagued by non-Gaussian noise.To improve both accuracy and robustness under non-Gaussian measurement noise,a robust SLAM algorithm is proposed.It is based on the square-root cubature Kalman filter equipped with a Huber' s generalized maximum likelihood estimator(GM-estimator).In particular,the square-root cubature rule is applied to propagate the robot state vector and covariance matrix in the time update,the measurement update and the new landmark initialization stages of the SLAM.Moreover,gain weight matrices with respect to the measurement residuals are calculated by utilizing Huber' s technique in the measurement update step.The measurement outliers are suppressed by lower Kalman gains as merging into the system.The proposed algorithm can achieve better performance under the condition of non-Gaussian measurement noise in comparison with benchmark algorithms.The simulation results demonstrate the advantages of the proposed SLAM algorithm.展开更多
文摘基于EP2C35芯片设计了无线传感网络(WSN)数字基带成形滤波器,主要通过根升余弦成形滤波器的参数分析、抽头系数提取、系统结构设计与改进、系统功能与时序仿真和FPGA综合调试等过程实现。研究结果表明,该成形滤波器滚降系数为0.3时,通带内衰减小于0.5 d B,阻带衰减40 d B/Dec,时钟频率215 MHz,消耗288个逻辑单元,其资源消耗低,运算速度快,成本低廉,具有一定应用价值。
基金Supported by the National High Technology Research and Development Program of China(2010AA09Z104)the Fundamental Research Funds of the Zhejiang University(2014FZA5020)
文摘Mobile robot systems performing simultaneous localization and mapping(SLAM) are generally plagued by non-Gaussian noise.To improve both accuracy and robustness under non-Gaussian measurement noise,a robust SLAM algorithm is proposed.It is based on the square-root cubature Kalman filter equipped with a Huber' s generalized maximum likelihood estimator(GM-estimator).In particular,the square-root cubature rule is applied to propagate the robot state vector and covariance matrix in the time update,the measurement update and the new landmark initialization stages of the SLAM.Moreover,gain weight matrices with respect to the measurement residuals are calculated by utilizing Huber' s technique in the measurement update step.The measurement outliers are suppressed by lower Kalman gains as merging into the system.The proposed algorithm can achieve better performance under the condition of non-Gaussian measurement noise in comparison with benchmark algorithms.The simulation results demonstrate the advantages of the proposed SLAM algorithm.
