Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Using covariance intersection for SLAM
Robotics and Autonomous Systems
Sensor selection via convex optimization
IEEE Transactions on Signal Processing
Optimization-based dynamic sensor management for distributed multitarget tracking
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Injecting power-awareness into epidemic information dissemination in sensor networks
Future Generation Computer Systems
Balancing energy consumption with mobile agents in wireless sensor networks
Future Generation Computer Systems
Posterior Cramer-Rao bounds for discrete-time nonlinear filtering
IEEE Transactions on Signal Processing
Approximate Dynamic Programming for Communication-Constrained Sensor Network Management
IEEE Transactions on Signal Processing
Networked sensor management and data rate control for tracking maneuvering targets
IEEE Transactions on Signal Processing
Efficient Sensor Management Policies for Distributed Target Tracking in Multihop Sensor Networks
IEEE Transactions on Signal Processing
Large-Scale Optimal Sensor Array Management for Multitarget Tracking
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Dynamic sensor collaboration via sequential Monte Carlo
IEEE Journal on Selected Areas in Communications
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Recent years have witnessed the shift of wireless sensor networks (WSNs) from theoretical research to practical applications. Due to their simplicity, range-based sensor networks have been widely used. To track a maneuvering target in range-based sensor networks, first, we derive the relationship between the multiple model posterior Cramer-Rao lower bound (PCRLB) and the distance from the sensor to the target, which forms the basis of choosing the subset of candidate sensors that may attend the incoming tracking event. Second, we design two optimization strategies under the communication constraint, namely the optimal sensor selection and cluster head selection. Third, we can estimate the state of the maneuvering target by making use of the interacting multiple model (IMM) algorithm and predict the model index one time step ahead. Last, simulation results show the effectiveness of the proposed schemes.