This paper proposes a new approach for solving the bearings-only target tracking (BoT) problem by introducing a maximum correntropy criterion to the pseudolinear Kalman filter (PLKF). PLKF has been a popular choice fo...This paper proposes a new approach for solving the bearings-only target tracking (BoT) problem by introducing a maximum correntropy criterion to the pseudolinear Kalman filter (PLKF). PLKF has been a popular choice for solving BoT problems owing to the reduced computational complexity. However, the coupling between the measurement vector and pseudolinear noise causes bias in PLKF. To address this issue, a bias-compensated PLKF (BC-PLKF) under the assumption of Gaussian noise was formulated. However, this assumption may not be valid in most practical cases. Therefore, a bias-compensated PLKF with maximum correntropy criterion is introduced, resulting in two new filters: maximum correntropy pseudolinear Kalman filter (MC-PLKF) and maximum correntropy bias-compensated pseudolinear Kalman filter (MC-BC-PLKF). To demonstrate the performance of the proposed estimators, a comparative analysis assuming large outliers in the process and measurement model of 2D BoT is conducted. These large outliers are modeled as non-Gaussian noises with diverse noise distributions that combine Gaussian and Laplacian noises. The simulation results are validated using root mean square error (RMSE), average RMSE (ARMSE), percentage of track loss and bias norm. Compared to PLKF and BC-PLKF, all the proposed maximum correntropy-based filters (MC-PLKF and MC-BC-PLKF) performed with superior estimation accuracy.展开更多
This article addresses the nonlinear state estimation problem where the conventional Gaussian assumption is completely relaxed.Here,the uncertainties in process and measurements are assumed non-Gaussian,such that the ...This article addresses the nonlinear state estimation problem where the conventional Gaussian assumption is completely relaxed.Here,the uncertainties in process and measurements are assumed non-Gaussian,such that the maximum correntropy criterion(MCC)is chosen to replace the conventional minimum mean square error criterion.Furthermore,the MCC is realized using Gaussian as well as Cauchy kernels by defining an appropriate cost function.Simulation results demonstrate the superior estimation accuracy of the developed estimators for two nonlinear estimation problems.展开更多
文摘This paper proposes a new approach for solving the bearings-only target tracking (BoT) problem by introducing a maximum correntropy criterion to the pseudolinear Kalman filter (PLKF). PLKF has been a popular choice for solving BoT problems owing to the reduced computational complexity. However, the coupling between the measurement vector and pseudolinear noise causes bias in PLKF. To address this issue, a bias-compensated PLKF (BC-PLKF) under the assumption of Gaussian noise was formulated. However, this assumption may not be valid in most practical cases. Therefore, a bias-compensated PLKF with maximum correntropy criterion is introduced, resulting in two new filters: maximum correntropy pseudolinear Kalman filter (MC-PLKF) and maximum correntropy bias-compensated pseudolinear Kalman filter (MC-BC-PLKF). To demonstrate the performance of the proposed estimators, a comparative analysis assuming large outliers in the process and measurement model of 2D BoT is conducted. These large outliers are modeled as non-Gaussian noises with diverse noise distributions that combine Gaussian and Laplacian noises. The simulation results are validated using root mean square error (RMSE), average RMSE (ARMSE), percentage of track loss and bias norm. Compared to PLKF and BC-PLKF, all the proposed maximum correntropy-based filters (MC-PLKF and MC-BC-PLKF) performed with superior estimation accuracy.
基金Rahul Radhakrishnan received the B.Tech.degree in Applied Electronics and Instrumentation from the Government Engineering College,Calicut,India,in 2010 and the M.Tech.degreein Control Systems from the Department of Electrical Engineering,National Institute of Technology Kurukshetra,India,in 2013.He received the Ph.D.degree from the Department of Electrical Engineering,Indian Institute of Technology Patna,India,in 2018.Currently,he is workingasan Assistant Professor in the Department of Electrical Engineering,Sardar Vallabhbhai National Institute of Technology,Surat,Gujarat,India.His main research interests include nonlinear filtering,aerospace,and underwater target tracking.
文摘This article addresses the nonlinear state estimation problem where the conventional Gaussian assumption is completely relaxed.Here,the uncertainties in process and measurements are assumed non-Gaussian,such that the maximum correntropy criterion(MCC)is chosen to replace the conventional minimum mean square error criterion.Furthermore,the MCC is realized using Gaussian as well as Cauchy kernels by defining an appropriate cost function.Simulation results demonstrate the superior estimation accuracy of the developed estimators for two nonlinear estimation problems.
