The problem of soft-input so,output （ SISO ） detection for time-varying frequency-selec- tive fading channels is considered. Based on a suitably-designed factor graph and the sum-product al- gorithm, a low-complexit...The problem of soft-input so,output （ SISO ） detection for time-varying frequency-selec- tive fading channels is considered. Based on a suitably-designed factor graph and the sum-product al- gorithm, a low-complexity iterative message passing scheme is proposed for joint channel estima- tion, equalization and decoding. Two kinds of schedules （parallel and serial） are adopted in message updates to produce two algorithms with different latency. The computational complexity per iteration of the proposed algorithms grows only linearly with the channel length, which is a significantly de- crease compared to the optimal maximum a posteriori （MAP） detection with the exponential com- plexity. Computer simulations demonstrate the effectiveness of the proposed schemes in terms of bit error rate performance.展开更多
同时定位与建图(Simultaneous Localization and Mapping,SLAM)技术使移动机器人在缺乏先验环境信息的条件下,能够在估计自身位姿的同时构建环境地图。然而,在海洋、矿洞等复杂环境中,移动机器人容易受到随机突变噪声的干扰,进而导致SLA...同时定位与建图(Simultaneous Localization and Mapping,SLAM)技术使移动机器人在缺乏先验环境信息的条件下,能够在估计自身位姿的同时构建环境地图。然而,在海洋、矿洞等复杂环境中,移动机器人容易受到随机突变噪声的干扰,进而导致SLAM性能下降。现有的概率假设密度(Probability Hypothesis Density,PHD)SLAM算法未考虑随机突变噪声,受到干扰时在线自适应调整能力较弱。为解决移动机器人因随机突变噪声导致状态估计和建图精度降低的问题,本文结合强跟踪滤波器(Strong Tracking Filter,STF)与PHD滤波器,提出了一种基于强跟踪的自适应PHD-SLAM滤波算法(Strong Tracking Probability Hypothesis Density Simultaneous Localization and Mapping,STPHD-SLAM)。该算法以PHD-SLAM为框架,针对过程噪声协方差和量测噪声协方差随机突变问题,本文通过在特征预测协方差中引入STF中的渐消因子,实现了对特征预测的自适应修正和卡尔曼增益的动态调整,从而增强了算法的自适应能力。其中渐消因子根据量测新息递归更新,确保噪声突变时每个时刻的量测新息保持正交,从而充分利用量测信息,准确并且快速地跟踪突变噪声。针对渐消因子激增导致的滤波器发散问题,本文对渐消因子进行边界约束,提高算法的鲁棒性。仿真结果表明,在量测噪声协方差和过程噪声协方差随机突变的情况下,所提算法相较于PHD-SLAM 1.0和PHD-SLAM 2.0的定位和建图精度都得到了提高,同时保证了计算效率。展开更多
传统的误差配准算法假设系统偏差恒定或缓慢变化,当系统误差发生突变或快速变化时,这一假设不再成立。针对这一问题,研究了时变条件下的误差配准算法,引入渐消因子,对常规的基于地心地固坐标系的广义最小二乘算法(generalized least squ...传统的误差配准算法假设系统偏差恒定或缓慢变化,当系统误差发生突变或快速变化时,这一假设不再成立。针对这一问题,研究了时变条件下的误差配准算法,引入渐消因子,对常规的基于地心地固坐标系的广义最小二乘算法(generalized least squares algorithm based on the earth-centered earth-fixed coordinate system,ECEF-GLS)进行了修正,弱化历史量测对配准的影响,并对渐消因子的选取问题进行了研究,给出了合理的设计方法。算法验证表明,基于渐消因子的ECEF-GLS估计算法能够对时变的系统偏差进行有效估计,精度满足配准要求。展开更多
Two kinds of fading filters and their principles are introduced. An adaptive robust filter is given with corresponding principle. The basic abilities of the fading filters and adaptively robust filter in controlling t...Two kinds of fading filters and their principles are introduced. An adaptive robust filter is given with corresponding principle. The basic abilities of the fading filters and adaptively robust filter in controlling the influences of the kinematic model errors are analyzed. A practical example is given. The results of the fading filter and adaptively robust filter are compared and analyzed.展开更多
基金Supported by the National Natural Science Foundation of China(61201181)Specialized Research Fund for the Doctoral Program of Higher Education(20121101120020)the Co-innovation Laboratory of Aerospace Broadband Network Technology
文摘The problem of soft-input so,output （ SISO ） detection for time-varying frequency-selec- tive fading channels is considered. Based on a suitably-designed factor graph and the sum-product al- gorithm, a low-complexity iterative message passing scheme is proposed for joint channel estima- tion, equalization and decoding. Two kinds of schedules （parallel and serial） are adopted in message updates to produce two algorithms with different latency. The computational complexity per iteration of the proposed algorithms grows only linearly with the channel length, which is a significantly de- crease compared to the optimal maximum a posteriori （MAP） detection with the exponential com- plexity. Computer simulations demonstrate the effectiveness of the proposed schemes in terms of bit error rate performance.
文摘同时定位与建图(Simultaneous Localization and Mapping,SLAM)技术使移动机器人在缺乏先验环境信息的条件下,能够在估计自身位姿的同时构建环境地图。然而,在海洋、矿洞等复杂环境中,移动机器人容易受到随机突变噪声的干扰,进而导致SLAM性能下降。现有的概率假设密度(Probability Hypothesis Density,PHD)SLAM算法未考虑随机突变噪声,受到干扰时在线自适应调整能力较弱。为解决移动机器人因随机突变噪声导致状态估计和建图精度降低的问题,本文结合强跟踪滤波器(Strong Tracking Filter,STF)与PHD滤波器,提出了一种基于强跟踪的自适应PHD-SLAM滤波算法(Strong Tracking Probability Hypothesis Density Simultaneous Localization and Mapping,STPHD-SLAM)。该算法以PHD-SLAM为框架,针对过程噪声协方差和量测噪声协方差随机突变问题,本文通过在特征预测协方差中引入STF中的渐消因子,实现了对特征预测的自适应修正和卡尔曼增益的动态调整,从而增强了算法的自适应能力。其中渐消因子根据量测新息递归更新,确保噪声突变时每个时刻的量测新息保持正交,从而充分利用量测信息,准确并且快速地跟踪突变噪声。针对渐消因子激增导致的滤波器发散问题,本文对渐消因子进行边界约束,提高算法的鲁棒性。仿真结果表明,在量测噪声协方差和过程噪声协方差随机突变的情况下,所提算法相较于PHD-SLAM 1.0和PHD-SLAM 2.0的定位和建图精度都得到了提高,同时保证了计算效率。
文摘传统的误差配准算法假设系统偏差恒定或缓慢变化,当系统误差发生突变或快速变化时,这一假设不再成立。针对这一问题,研究了时变条件下的误差配准算法,引入渐消因子,对常规的基于地心地固坐标系的广义最小二乘算法(generalized least squares algorithm based on the earth-centered earth-fixed coordinate system,ECEF-GLS)进行了修正,弱化历史量测对配准的影响,并对渐消因子的选取问题进行了研究,给出了合理的设计方法。算法验证表明,基于渐消因子的ECEF-GLS估计算法能够对时变的系统偏差进行有效估计,精度满足配准要求。
基金Supported by the National Natural Science Foundation of China （No.40174009, No.40274002）.
文摘Two kinds of fading filters and their principles are introduced. An adaptive robust filter is given with corresponding principle. The basic abilities of the fading filters and adaptively robust filter in controlling the influences of the kinematic model errors are analyzed. A practical example is given. The results of the fading filter and adaptively robust filter are compared and analyzed.
