Stochastic systems: estimation, identification and adaptive control
Stochastic systems: estimation, identification and adaptive control
Proceedings of the 2nd international conference on Information processing in sensor networks
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Efficient particle filtering for jump Markov systems. Application to time-varying autoregressions
IEEE Transactions on Signal Processing
Expectation maximization algorithms for MAP estimation of jumpMarkov linear systems
IEEE Transactions on Signal Processing
Iterative algorithms for state estimation of jump Markov linearsystems
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
Stability of Kalman filtering with Markovian packet losses
Automatica (Journal of IFAC)
Networked data fusion with packet losses and variable delays
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On predictive coding for erasure channels using a Kalman framework
IEEE Transactions on Signal Processing
Optimal linear state estimation over a packet-dropping network using linear temporal coding
Automatica (Journal of IFAC)
Ultra wideband indoor positioning system in support of emergency evacuation
Proceedings of the Fifth ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness
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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 asymptotic error variance can be directly computed. We include a few illustrative examples including .xed-length burst errors, a two-state model,and partial losses due to multiple SNR states. Our analysis encompasses modeling discrete changes not only in the received data as stated above, but also in the underlying system. In the context of the lossy sensor model, the former allows for variation in sensor positioning, power control, and loss of data communications; the latter could allow for discrete changes in the dynamics of the variable monitored by the sensor. This freedom in modeling yields a tool that is potentially valuable in various scenarios in which entities that share information are subjected to challenging and time-varying network conditions.