Bias-Compensated Diffusion Pseudolinear Kalman Filter Algorithm for Censored Bearings-Only Target Tracking

This letter proposes a novel bias-compensated diffusion pseudolinear Kalman filter algorithm for censored bearings-only target tracking. The proposed algorithm considers two biases caused by the censored bearing angle measurements and the correlation between the measurement vector and the pseudolinear noise. First, the inverse Mills ratio is used to rebuild the uncensored measurements, which can efficiently compensate the bias arising from the censored measurements. Then, we present a bias analysis for the censored diffusion pseudolinear Kalman filter to develop a bias compensation method, leading to the BC-DPLKF-C algorithm. Simulation results show the improved performance of the proposed Kalman filter compared with existing algorithms.

[1]  Michael J. Piovoso,et al.  The Tobit Kalman Filter: An Estimator for Censored Measurements , 2016, IEEE Transactions on Control Systems Technology.

[2]  Ali H. Sayed,et al.  Distributed Spectrum Estimation for Small Cell Networks Based on Sparse Diffusion Adaptation , 2013, IEEE Signal Processing Letters.

[3]  Ali H. Sayed,et al.  Distributed Decision-Making Over Adaptive Networks , 2013, IEEE Transactions on Signal Processing.

[4]  E. Blasch,et al.  Multiresolution EO/IR target tracking and identification , 2005, 2005 7th International Conference on Information Fusion.

[5]  K. Gong,et al.  Position and Velocity Estimation Via Bearing Observations , 1978, IEEE Transactions on Aerospace and Electronic Systems.

[6]  Thiagalingam Kirubarajan,et al.  Comparison of EKF, pseudomeasurement, and particle filters for a bearing-only target tracking problem , 2002, SPIE Defense + Commercial Sensing.

[7]  Econo Metrica.  REGRESSION ANALYSIS WHEN THE DEPENDENT VARIABLE IS TRUNCATED NORMAL , 2016 .

[8]  Ngoc Hung Nguyen,et al.  Adaptive distributed waveform selection for target tracking by a multistatic radar system , 2014, 2014 IEEE Workshop on Statistical Signal Processing (SSP).

[9]  Ngoc Hung Nguyen,et al.  Improved Pseudolinear Kalman Filter Algorithms for Bearings-Only Target Tracking , 2017, IEEE Transactions on Signal Processing.

[10]  Ali H. Sayed,et al.  Diffusion Strategies Outperform Consensus Strategies for Distributed Estimation Over Adaptive Networks , 2012, IEEE Transactions on Signal Processing.

[11]  M E Gehm,et al.  Compressive sensing in the EO/IR. , 2015, Applied optics.

[12]  V. Aidala.  Kalman Filter Behavior in Bearings-Only Tracking Applications , 1979, IEEE Transactions on Aerospace and Electronic Systems.

[13]  Ali H. Sayed,et al.  Diffusion Strategies for Distributed Kalman Filtering and Smoothing , 2010, IEEE Transactions on Automatic Control.

[14]  Kutluyıl Doğançay,et al.  Bias compensation for the bearings-only pseudolinear target track estimator , 2006, IEEE Transactions on Signal Processing.

[15]  Ali Reza Toloei,et al.  State Estimation for Target Tracking Problems with Nonlinear Kalman Filter Algorithms , 2014 .

[16]  J. Heckman.  The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models , 1976 .

[17]  Danilo P. Mandic,et al.  Distributed Widely Linear Kalman Filtering for Frequency Estimation in Power Networks , 2015, IEEE Transactions on Signal and Information Processing over Networks.

[18]  Danilo P. Mandic,et al.  Class of Widely Linear Complex Kalman Filters , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[19]  Ali H. Sayed,et al.  Adaptive Networks , 2014, Proceedings of the IEEE.

[20]  Y. Bar-Shalom.  Tracking and data association , 1988 .

[21]  Danilo P. Mandic,et al.  Kalman filtering for widely linear complex and quaternion valued bearings only tracking , 2012, IET Signal Process..

[22]  Yiguang Liu,et al.  Distributed Censored Regression Over Networks , 2015, IEEE Transactions on Signal Processing.