Tracking With Sparse and Correlated Measurements via a Shrinkage-Based Particle Filter

This paper presents a shrinkage-based particle filter method for tracking a mobile user in wireless networks. The proposed method estimates the shadowing noise covariance matrix using the shrinkage technique. The particle filter is designed with the estimated covariance matrix to improve the tracking performance. The shrinkage-based particle filter can be applied in a number of applications for navigation, tracking, and localization when the available sensor measurements are correlated and sparse. The performance of the shrinkage-based particle filter is compared with the posterior Cramer–Rao lower bound, which is also derived in this paper. The advantages of the proposed shrinkage-based particle filter approach are demonstrated via simulation and experimental results.

[1]  Goran M. Djuknic,et al.  Geolocation and Assisted GPS , 2001, Computer.

[2]  Brian L. Mark,et al.  Robust mobility tracking for cellular networks , 2002, 2002 IEEE International Conference on Communications. Conference Proceedings. ICC 2002 (Cat. No.02CH37333).

[3]  Shahid Khan,et al.  Localization Performance Evaluation of Extended Kalman Filter in Wireless Sensors Network , 2014, ANT/SEIT.

[4]  Ian F. Akyildiz,et al.  A new random walk model for PCS networks , 2000, IEEE Journal on Selected Areas in Communications.

[5]  R. Singer.  Estimating Optimal Tracking Filter Performance for Manned Maneuvering Targets , 1970, IEEE Transactions on Aerospace and Electronic Systems.

[6]  Brian L. Mark,et al.  Real-time mobility tracking algorithms for cellular networks based on Kalman filtering , 2005, IEEE Transactions on Mobile Computing.

[7]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[8]  Cedric Nishan Canagarajah,et al.  Localization of Mobile Nodes in Wireless Networks with Correlated in Time Measurement Noise , 2011, IEEE Transactions on Mobile Computing.

[9]  Kaveh Pahlavan,et al.  Wireless Information Networks: Pahlavan/Wireless Information Networks, Second Edition , 2005 .

[10]  LI X.RONG,et al.  Best linear unbiased filtering with nonlinear measurements for target tracking , 2004, IEEE Transactions on Aerospace and Electronic Systems.

[11]  Eric Moulines,et al.  Comparison of resampling schemes for particle filtering , 2005, ISPA 2005. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 2005..

[12]  Clarence C. Y. Kwan.  Estimation error in the average correlation of security returns and shrinkage estimation of covariance and correlation matrices , 2008 .

[13]  Petar M. Djuric,et al.  Resampling Methods for Particle Filtering: Classification, implementation, and strategies , 2015, IEEE Signal Processing Magazine.

[14]  Branko Ristic,et al.  Beyond the Kalman Filter: Particle Filters for Tracking Applications , 2004 .

[15]  Lyudmila Mihaylova,et al.  Sequential Markov Chain Monte Carlo for multi-target tracking with correlated RSS measurements , 2015, 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP).

[16]  Li Han,et al.  P-LEACH: An Efficient Cluster-Based Technique to Track Mobile Sinks in Wireless Sensor Networks , 2014, Int. J. Distributed Sens. Networks.

[17]  Jun S. Liu,et al.  Sequential Monte Carlo methods for dynamic systems , 1997 .

[18]  K. Strimmer,et al.  Statistical Applications in Genetics and Molecular Biology A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics , 2011 .

[19]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[20]  Robert Grover Brown,et al.  Introduction to random signals and applied Kalman filtering : with MATLAB exercises and solutions , 1996 .

[21]  Shovan Bhaumik,et al.  Tracking of ballistic target on re-entry using ensemble Kalman filter , 2012, 2012 Annual IEEE India Conference (INDICON).

[22]  Xiaodong Wang,et al.  Joint mobility tracking and handoff in cellular networks via sequential Monte Carlo filtering , 2003, IEEE Trans. Signal Process..

[23]  Minyue Fu,et al.  Target Tracking in Wireless Sensor Networks Based on the Combination of KF and MLE Using Distance Measurements , 2012, IEEE Transactions on Mobile Computing.

[24]  Tong Liu,et al.  Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks , 1998, IEEE J. Sel. Areas Commun..

[25]  Yong Qi,et al.  Online Estimation of the Approximate Posterior Cramer-Rao Lower Bound for Discrete-Time Nonlinear Filtering , 2011, IEEE Transactions on Aerospace and Electronic Systems.

[26]  L. Mihaylova,et al.  Localization of multiple nodes based on correlated measurements and shrinkage estimation , 2014, 2014 Sensor Data Fusion: Trends, Solutions, Applications (SDF).

[27]  Ainslie,et al.  CORRELATION MODEL FOR SHADOW FADING IN MOBILE RADIO SYSTEMS , 2004 .

[28]  Brian L. Mark,et al.  A mobility tracking model for wireless ad hoc networks , 2003, 2003 IEEE Wireless Communications and Networking, 2003. WCNC 2003..

[29]  Biao Huang,et al.  A Particle Filter Approach to Approximate Posterior Cramer-Rao Lower Bound: The Case of Hidden States , 2013, IEEE Transactions on Aerospace and Electronic Systems.

[30]  Joumana Farah,et al.  Target Tracking Using Machine Learning and Kalman Filter in Wireless Sensor Networks , 2014, IEEE Sensors Journal.

[31]  Cedric Nishan Canagarajah,et al.  Mobility Tracking in Cellular Networks Using Particle Filtering , 2007, IEEE Transactions on Wireless Communications.

[32]  Xiaoli Wang,et al.  Mobility tracking using GPS, Wi-Fi and Cell ID , 2012, The International Conference on Information Network 2012.

[33]  Carlos H. Muravchik,et al.  Posterior Cramer-Rao bounds for discrete-time nonlinear filtering , 1998, IEEE Trans. Signal Process..

[34]  Olivier Ledoit,et al.  Honey, I Shrunk the Sample Covariance Matrix , 2003 .

[35]  Fredrik Gustafsson,et al.  New trends in radio network positioning , 2015, 2015 18th International Conference on Information Fusion (Fusion).