In order to enhance the location estimation performance of mobile station（MS）tracking and positioning, a new method of mobile location optimal estimation based on the federated filtering structure and the simplified...In order to enhance the location estimation performance of mobile station（MS）tracking and positioning, a new method of mobile location optimal estimation based on the federated filtering structure and the simplified unscented Kalman filter （UKF） is presented. The proposed algorithm uses the Singer mobile statement model as the reference system, and the simplified UKF as the subfilters. The subfilters receive the two groups of independently detected time difference of arrival （TDOA） measurement inputs and Doppler measurement inputs, and produce local estimation outputs to the main estimator. Then the main estimator performs the optimal fusion of the local estimation outputs according to the scalar weighted rule, and a global optimal or suboptimal estimation result is achieved. Finally the main estimator gives feedback and reset information to the subfilters and the reference system for next step estimation. In the simulations, the estimation performance of the proposed algorithm is evaluated and compared with the simplified UKF method with TDOA or Doppler measurement alone. The simulation results demonstrate that the proposed algorithm can effectively reduce the location estimation error and variance of the MS, and has favorable performance in both root mean square error（RMSE） and mean error cumulative distribution function（CDF）.展开更多
To solve the problem of information fusion in the strapdown inertial navigation system(SINS)/celestial navigation system(CNS)/global positioning system(GPS) integrated navigation system described by the nonlinear/non-...To solve the problem of information fusion in the strapdown inertial navigation system(SINS)/celestial navigation system(CNS)/global positioning system(GPS) integrated navigation system described by the nonlinear/non-Gaussian error models,a new algorithm called the federated unscented particle filtering(FUPF) algorithm was introduced.In this algorithm,the unscented particle filter(UPF) served as the local filter,the federated filter was used to fuse outputs of all local filters,and the global filter result was obtained.Because the algorithm was not confined to the assumption of Gaussian noise,it was of great significance to integrated navigation systems described by the non-Gaussian noise.The proposed algorithm was tested in a vehicle's maneuvering trajectory,which included six flight phases:climbing,level flight,left turning,level flight,right turning and level flight.Simulation results are presented to demonstrate the improved performance of the FUPF over conventional federated unscented Kalman filter(FUKF).For instance,the mean of position-error decreases from(0.640×10-6 rad,0.667×10-6 rad,4.25 m) of FUKF to(0.403×10-6 rad,0.251×10-6 rad,1.36 m) of FUPF.In comparison of the FUKF,the FUPF performs more accurate in the SINS/CNS/GPS system described by the nonlinear/non-Gaussian error models.展开更多
Aiming at the adverse effect caused by limited detecting probability of sensors on filtering preci- sion of a nonlinear system state, a novel muhi-sensor federated unscented Kalman filtering algorithm is proposed. Fir...Aiming at the adverse effect caused by limited detecting probability of sensors on filtering preci- sion of a nonlinear system state, a novel muhi-sensor federated unscented Kalman filtering algorithm is proposed. Firstly, combined with the residual detection strategy, effective observations are cor- rectly identified. Secondly, according to the missing characteristic of observations and the structural feature of unscented Kalman filter, the iterative process of the single-sensor unscented Kalman filter in intermittent observations is given. The key idea is that the state estimation and its error covariance matrix are replaced by the state one-step prediction and its error covariance matrix, when the phe- nomenon of observations missing occurs. Finally, based on the realization mechanism of federated filter, a new fusion framework of state estimation from each local node is designed. And the filtering precision of system state is improved further by the effective management of observations missing and the rational utilization of redundancy and complementary information among multi-sensor observa- tions. The theory analysis and simulation results show the feasibility and effectiveness of the pro- posed algorithm.展开更多
基金The Cultivation Fund of the Key Scientific and Technical Innovation Project of Ministry of Education of China(No.706028)
文摘In order to enhance the location estimation performance of mobile station（MS）tracking and positioning, a new method of mobile location optimal estimation based on the federated filtering structure and the simplified unscented Kalman filter （UKF） is presented. The proposed algorithm uses the Singer mobile statement model as the reference system, and the simplified UKF as the subfilters. The subfilters receive the two groups of independently detected time difference of arrival （TDOA） measurement inputs and Doppler measurement inputs, and produce local estimation outputs to the main estimator. Then the main estimator performs the optimal fusion of the local estimation outputs according to the scalar weighted rule, and a global optimal or suboptimal estimation result is achieved. Finally the main estimator gives feedback and reset information to the subfilters and the reference system for next step estimation. In the simulations, the estimation performance of the proposed algorithm is evaluated and compared with the simplified UKF method with TDOA or Doppler measurement alone. The simulation results demonstrate that the proposed algorithm can effectively reduce the location estimation error and variance of the MS, and has favorable performance in both root mean square error（RMSE） and mean error cumulative distribution function（CDF）.
基金Project(60535010) supported by the National Nature Science Foundation of China
文摘To solve the problem of information fusion in the strapdown inertial navigation system(SINS)/celestial navigation system(CNS)/global positioning system(GPS) integrated navigation system described by the nonlinear/non-Gaussian error models,a new algorithm called the federated unscented particle filtering(FUPF) algorithm was introduced.In this algorithm,the unscented particle filter(UPF) served as the local filter,the federated filter was used to fuse outputs of all local filters,and the global filter result was obtained.Because the algorithm was not confined to the assumption of Gaussian noise,it was of great significance to integrated navigation systems described by the non-Gaussian noise.The proposed algorithm was tested in a vehicle's maneuvering trajectory,which included six flight phases:climbing,level flight,left turning,level flight,right turning and level flight.Simulation results are presented to demonstrate the improved performance of the FUPF over conventional federated unscented Kalman filter(FUKF).For instance,the mean of position-error decreases from(0.640×10-6 rad,0.667×10-6 rad,4.25 m) of FUKF to(0.403×10-6 rad,0.251×10-6 rad,1.36 m) of FUPF.In comparison of the FUKF,the FUPF performs more accurate in the SINS/CNS/GPS system described by the nonlinear/non-Gaussian error models.
基金Supported by the National Natural Science Foundation(NNSF)of China under Grant(No.61300214)the Science and Technology Innovation Team Support Plan of Education Department of Henan Province(No.13IRTSTHN021)+5 种基金the Science and Technology Research Key Project of Education Department of Henan Province(No.13A413066)the Basic and Frontier Technology Research Plan of Henan Province(No.132300410148)the Funding Scheme of Young Key Teacher of Henan Province Universities(No.2013GGJS-026)the Key Project of Teaching Reform Research of Henan University(No.HDXJJG2013-07)the Postdoctoral Science Fund of Henan Province(No.2013029)the Postdoctoral Science Fund of China(No.2014M551999)
文摘Aiming at the adverse effect caused by limited detecting probability of sensors on filtering preci- sion of a nonlinear system state, a novel muhi-sensor federated unscented Kalman filtering algorithm is proposed. Firstly, combined with the residual detection strategy, effective observations are cor- rectly identified. Secondly, according to the missing characteristic of observations and the structural feature of unscented Kalman filter, the iterative process of the single-sensor unscented Kalman filter in intermittent observations is given. The key idea is that the state estimation and its error covariance matrix are replaced by the state one-step prediction and its error covariance matrix, when the phe- nomenon of observations missing occurs. Finally, based on the realization mechanism of federated filter, a new fusion framework of state estimation from each local node is designed. And the filtering precision of system state is improved further by the effective management of observations missing and the rational utilization of redundancy and complementary information among multi-sensor observa- tions. The theory analysis and simulation results show the feasibility and effectiveness of the pro- posed algorithm.
