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Kalman filtering with partial observation losses (2004)
| Content Provider | CiteSeerX |
|---|---|
| Author | Liu, Xiangheng Goldsmith, Andrea |
| Description | We study the Kalman filtering problem when part or all of the observation measurements are lost in a random fashion. Pioneering work has recently addressed the Kalman filtering problem with intermittent observations, where the observation measurements are either received in full or completely lost. Partial observation losses can occur in a distributed control system where measurements are taken at different sensors that are at different physical locations or one sensor needs to send its data in multiple packets. We formulate the Kalman filtering problem with partial observation losses and derive the Kalman filter updates with partial observation measurements. We show that with these partial measurements the Kalman filter and its error covariance matrix iteration become stochastic, since they now depend on the random packet arrivals of the sensor measurements, which can be lost or delayed when transmitted over a communication network. The communication network needs to provide a sufficient throughput for each of the sensor measurements in order to guarantee the stability of the Kalman filter, where the throughput captures the rate of the sensor measurements correctly received. We investigate the statistical convergence properties of the error covariance matrix iteration as a function of the throughput of the sensor measurements. A throughput region that guarantees the convergence of the error covariance matrix is found by solving a feasibility problem of a Linear Matrix Inequality (LMI). We also find an unstable throughput region such that the state estimation error of the Kalman filter is unbounded. When the Kalman filter is stable, the expected error covariance matrix is bounded both from above and from below. The results are illustrated with some simple numerical examples. I. IEEE Trans. on Autom. Control |
| File Format | |
| Language | English |
| Publisher Date | 2004-01-01 |
| Access Restriction | Open |
| Subject Keyword | Communication Network Need Partial Observation Measurement Kalman Filter Communication Network Error Covariance Matrix Multiple Packet Error Covariance Matrix Iteration Simple Numerical Example Different Sensor Feasibility Problem Expected Error Covariance Matrix Random Fashion Partial Observation Loss Observation Measurement Different Physical Location Intermittent Observation Sufficient Throughput State Estimation Error Control System Throughput Region Unstable Throughput Region Kalman Filtering Problem Linear Matrix Inequality Statistical Convergence Property Sensor Measurement Random Packet Arrival Kalman Filter Update Partial Measurement |
| Content Type | Text |
| Resource Type | Article |
