Elements of information theory
Elements of information theory
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Matrix computations (3rd ed.)
Time series: data analysis and theory
Time series: data analysis and theory
Principles of Digital Transmission: With Wireless Applications
Principles of Digital Transmission: With Wireless Applications
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Decentralized compression and predistribution via randomized gossiping
Proceedings of the 5th international conference on Information processing in sensor networks
Distributed Estimation Using Reduced-Dimensionality Sensor Observations
IEEE Transactions on Signal Processing
SOI-KF: Distributed Kalman Filtering With Low-Cost Communications Using the Sign of Innovations
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Automatica (Journal of IFAC)
Joint Source–Channel Communication for Distributed Estimation in Sensor Networks
IEEE Transactions on Information Theory
Optimizing network lifetime for distributed tracking with wireless sensor networks
Proceedings of the 6th ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks
Multi-rate distributed fusion estimation for sensor networks with packet losses
Automatica (Journal of IFAC)
Hi-index | 35.69 |
Estimation of nonstationary dynamical processes is of paramount importance in various applications including target tracking and navigation. The goal of this paper is to perform such tasks in a distributed fashion, using data collected at power-limited iensors which either communicate with a fusion center (FC) over noisy links, or, communicate with each other over nonideal channels in an ad hoc setting. In FC-based wireless sensor networks (WSNs) with a prescribed power budget, linear dimensionality reducing operators which account for the sensor-to-FC channel are derived per sensor to minimize the mean-square error (MSE) of Kalman filtered state estimates formed at the FC. Using these optrators and state predictions fed back from the FC online, sensors reduce the dimensionality of their local innovation sequences and communicate them to the FC where tracking estimates are corrected. Analytical and numerical results advocate that the novel channel-aware distributed tracker outperforms competing alternatives. In ad hoc WSNs deployed to perform distributed tracking, one sensor broadcasts reduced-dimensionality data per time slot, according to a prespecified transmission order. The dimensionality reducing operators employed by the broadcasting sensor are selected to meet its transmit-power budget, while minimizing the state estimation MSE of the sensor with the lowest receiving SNR. Based on the received reduced-dimensionality data from the broadcasting sensor, every sensor in range performs the MSE optimal tracking. Corroborating distributed target tracking simulations based on distance-only observations illustrate that the novel scheme provides sensors with accurate estimates at affordable communication cost.