Channel energy based estimation of target trajectories using distributed sensors with low communication rate

Authors:
Christian R. Berger;Sora Choi;Shengli Zhou;Peter Willett
Affiliations:
Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA and Department of Electrical and Computer Engineering, University of Connecticut;Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT;Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT;Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT
Venue:
IEEE Transactions on Signal Processing
Year:
2010

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Abstract

Sensor localization using channel energy measurements of distributed sensors has been studied in various scenarios. However, it is usually assumed that the target does not move significantly during the time needed to collect and process the data from the sensors. We want to estimate the trajectory of a moving target using a network of distributed sensors that measure only the received signal strength (RSS), sampled and as a function of time, without knowledge of the target amplitude/source level. To reduce the communication load, sensors communicate a reduced data set to the fusion center (FC), generated through local processing. It consists of three characteristic parameters: i) the maximum measured amplitude, corresponding to the closest-point-of-approach (CPA); ii) the corresponding time index; and iii) the time it takes for the amplitude to diminish by 6 dB relative to the CPA. To generate the reduced data sets, each sensor calculates a local maximum likelihood (ML) estimate of its parameters. The accuracy of these local estimates can be reasonably described by their respective Fisher information matrices (FIMs). The FC combines the data transmitted by the sensors using a ML-like formulation based on the local FIMs. This results in a heavily nonlinear least-squares problem, which we initialize via geometrical considerations. This approach has a very low communication load, performs comparably to a centralized estimator, and due to the modularized setup, any measurement model at the sensors can be considered.