A new algorithm is developed to achieve accurate state estimation in ground moving target tracking by means of using road information. It is an adaptive variable structure interacting multiple model estimator with dyn...A new algorithm is developed to achieve accurate state estimation in ground moving target tracking by means of using road information. It is an adaptive variable structure interacting multiple model estimator with dynamic models modification （DMM VS-IMM for short）. Firstly, road information is employed to modify the target dynamic models used by filter, including modification of state transition matrix and process noise. Secondly, road information is applied to update the model set of a VS-IMM estimator. Predicted state estimation and road information are used to locate the target in the road network on which the model set is updated and finally IMM filtering is implemented. As compared with traditional methods, the accuracy of state estimation is improved for target moving not only on a single road, but also through an intersection. Monte Carlo simulation demonstrates the efficiency and robustness of the proposed algorithm with moderate computational loads.展开更多
In wireless communication environment, the time-varying channel and angular spreads caused by multipath fading and the mobility of Mobile Stations (MS) degrade the performance of the conventional Direction-Of-Arrival ...In wireless communication environment, the time-varying channel and angular spreads caused by multipath fading and the mobility of Mobile Stations (MS) degrade the performance of the conventional Direction-Of-Arrival (DOA) tracking algorithms. On the other hand, although the DOA estimation methods based on the Maximum Likelihood (ML) principle have higher resolution than the beamforming and the subspace based methods, prohibitively heavy computation limits their practical applications. This letter first proposes a new suboptimal DOA estimation algorithm that combines the advantages of the lower complexity of subspace algorithm and the high accuracy of ML based algo- rithms, and then proposes a Kalman filtering based tracking algorithm to model the dynamic property of directional changes for mobile terminals in such a way that the association between the estimates made at different time points is maintained. At each stage during tracking process, the current suboptimal estimates of DOA are treated as measurements, predicted and updated via a Kalman state equation, hence adaptive tracking of moving MS can be carried out without the need to perform unduly heavy computations. Computer simulation results show that this proposed algorithm has better per- formance of DOA estimation and tracking of MS than the conventional ML or subspace based algo- rithms in terms of accuracy and robustness.展开更多
针对目前应用的MUSIC算法计算时间过长的问题,提出并实现了基于TLS-ESPRIT算法的导引头多目标到达方向(Direction of Arrival,DOA)估计技术。相比于目前MUSIC算法,DOA估计时间实现了有效的缩短。同时,针对TLS-ESPRIT算法在工程上实际估...针对目前应用的MUSIC算法计算时间过长的问题,提出并实现了基于TLS-ESPRIT算法的导引头多目标到达方向(Direction of Arrival,DOA)估计技术。相比于目前MUSIC算法,DOA估计时间实现了有效的缩短。同时,针对TLS-ESPRIT算法在工程上实际估计多目标的DOA时必须提前知道目标数量,如果目标数量估计不准确,就会造成DOA估计错误的问题,提出了基于TLS-ESPRIT算法的改进算法。可以做到在未知目标数量的情况下,进行目标数量的判断,进而进行准确的DOA估计。在实际工程条件下,估计准确率可以达到88%以上。展开更多
针对多输入多输出(Multi-Input Multi-Output,MIMO)阵列多目标定位,提出一种基于子空间特征分解的MIMO阵列旋转不变子空间算法(MIMO array estimation of signal parameters via rotational invariance techniques,简称MIMO-ESPRIT)。M...针对多输入多输出(Multi-Input Multi-Output,MIMO)阵列多目标定位,提出一种基于子空间特征分解的MIMO阵列旋转不变子空间算法(MIMO array estimation of signal parameters via rotational invariance techniques,简称MIMO-ESPRIT)。MIMO阵列各阵元发射彼此独立信号,因此阵列输出数据协方差矩阵不存在降秩以及信号子空间向噪声子空间扩散的现象,可直接应用子空间分解算法进行MIMO阵列目标方位估计。性能分析和仿真结果表明,随着发射阵元个数的增加和发射阵元间距的扩大,算法的多目标分辨能力和方位估计精度将得到明显改善。展开更多
基金Foundation item： National Natural Science Foundation of China （60502019）
文摘A new algorithm is developed to achieve accurate state estimation in ground moving target tracking by means of using road information. It is an adaptive variable structure interacting multiple model estimator with dynamic models modification （DMM VS-IMM for short）. Firstly, road information is employed to modify the target dynamic models used by filter, including modification of state transition matrix and process noise. Secondly, road information is applied to update the model set of a VS-IMM estimator. Predicted state estimation and road information are used to locate the target in the road network on which the model set is updated and finally IMM filtering is implemented. As compared with traditional methods, the accuracy of state estimation is improved for target moving not only on a single road, but also through an intersection. Monte Carlo simulation demonstrates the efficiency and robustness of the proposed algorithm with moderate computational loads.
文摘In wireless communication environment, the time-varying channel and angular spreads caused by multipath fading and the mobility of Mobile Stations (MS) degrade the performance of the conventional Direction-Of-Arrival (DOA) tracking algorithms. On the other hand, although the DOA estimation methods based on the Maximum Likelihood (ML) principle have higher resolution than the beamforming and the subspace based methods, prohibitively heavy computation limits their practical applications. This letter first proposes a new suboptimal DOA estimation algorithm that combines the advantages of the lower complexity of subspace algorithm and the high accuracy of ML based algo- rithms, and then proposes a Kalman filtering based tracking algorithm to model the dynamic property of directional changes for mobile terminals in such a way that the association between the estimates made at different time points is maintained. At each stage during tracking process, the current suboptimal estimates of DOA are treated as measurements, predicted and updated via a Kalman state equation, hence adaptive tracking of moving MS can be carried out without the need to perform unduly heavy computations. Computer simulation results show that this proposed algorithm has better per- formance of DOA estimation and tracking of MS than the conventional ML or subspace based algo- rithms in terms of accuracy and robustness.
文摘针对目前应用的MUSIC算法计算时间过长的问题,提出并实现了基于TLS-ESPRIT算法的导引头多目标到达方向(Direction of Arrival,DOA)估计技术。相比于目前MUSIC算法,DOA估计时间实现了有效的缩短。同时,针对TLS-ESPRIT算法在工程上实际估计多目标的DOA时必须提前知道目标数量,如果目标数量估计不准确,就会造成DOA估计错误的问题,提出了基于TLS-ESPRIT算法的改进算法。可以做到在未知目标数量的情况下,进行目标数量的判断,进而进行准确的DOA估计。在实际工程条件下,估计准确率可以达到88%以上。
文摘针对多输入多输出(Multi-Input Multi-Output,MIMO)阵列多目标定位,提出一种基于子空间特征分解的MIMO阵列旋转不变子空间算法(MIMO array estimation of signal parameters via rotational invariance techniques,简称MIMO-ESPRIT)。MIMO阵列各阵元发射彼此独立信号,因此阵列输出数据协方差矩阵不存在降秩以及信号子空间向噪声子空间扩散的现象,可直接应用子空间分解算法进行MIMO阵列目标方位估计。性能分析和仿真结果表明,随着发射阵元个数的增加和发射阵元间距的扩大,算法的多目标分辨能力和方位估计精度将得到明显改善。
