Estimation from lossy sensor data: jump linear modeling and Kalman filtering

Due to constraints in cost, power, and communication, losses often arise in large sensor networks. The sensor can be modeled as an output of a linear stochastic system with random losses of the sensor output samples. This paper considers the general problem of state estimation for jump linear systems where the discrete transitions are modeled as a Markov chain. Among other applications, this rich model can be used to analyze sensor networks. The sensor loss events are then modeled as Markov processes. Under the jump linear system model, many types of underlying losses can be easily considered, and the optimal estimator to be performed at the receiver in the presence of missing sensor data samples is given by a standard time-varying Kalman filter. We show that the asymptotic average estimation error variance converges and is given by a Linear Matrix Inequality, which can be easily solved. Under this framework, any arbitrary Markov loss process can be modeled, and its average asymptoti...

• Type: Conference
• Year: 2004
• Where: IPSN
• Authors: Alyson K. Fletcher, Sundeep Rangan, Vivek K. Goyal
• Keywords: IPSN 2004, Large Sensor Networks, Sensor, Sensor Networks