NDLI: Kalman filtering with state-dependent packet losses

Content Provider	IET Digital Library
Author	Thapliyal, Omanshu Nandiganahalli, Jayaprakash Suraj Hwang, Inseok
Abstract	This study addresses the problem of state estimation for discrete-time, linear time invariant systems subject to packet losses, which occur in specific regions of the state space. Most practical estimation problems are characterised by occurrences of loss of observation packets, which makes the packet arrival process a non-stationary statistic, making the analysis and design of such an estimator challenging. This estimation problem subject to state-dependent packet losses is formulated using a state-dependent hybrid measurement model and solved using the projection theorem-based approach to obtain minimum mean square error state estimates. By systematically utilising the a priori information of the regions where the packet loss is likely to occur, the proposed estimator takes the Kalman filter structure with the modified algebraic Riccati iteration for the error covariance matrix being stochastic due to the probabilistic packet arrival process. Finally, the proposed estimator is demonstrated using an illustrative two-dimensional aircraft tracking example with state-dependent packet loss and is shown to have improved performance over the baseline packet loss algorithm.
Starting Page	306
Ending Page	312
Page Count	7
ISSN	17518644
Volume Number	13
e-ISSN	17518652
Issue Number	Issue 2, Jan (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cta/13/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cta.2018.5425
Journal	IET Control Theory & Applications
Publisher Date	2018-12-02
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Baseline Packet Loss Algorithm Covariance Matrices Discrete Control System Discrete Time System Estimation Problem Filtering Method in Signal Processing Interpolation And Function Approximation Iterative Method Kalman Filter Least Mean Squares Method Linear System Linear Time Invariant System Subject Mean Square Error Method Minimum Mean Square Error State Estimate Modelling And Identification Numerical Analysis Observation Packet Practical Estimation Problem Probabilistic Packet Arrival Process Riccati Equation Signal Processing Theory Simulation State Estimation State Space State-dependent Hybrid Measurement Model State-dependent Packet Loss
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Human-Computer Interaction Electrical and Electronic Engineering Computer Science Applications

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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