Expectation Maximization Algorithm for Simultaneous Tracking and Sparse Calibration of Sensor Networks
(English)Manuscript (preprint) (Other academic)
The performance of ground sensor networks (gsn) relies on accurate knowledge of sensor positions and sensor linearity parameters. Calibration of these should be done automatically after the deployment and later on regular intervals. Preferably, only targets of opportunity should be used to avoid manual intervention. We present a Bayesian solution that scales well with network size and efficiently utilizes a limited number of data to get a sparse estimate of a potentially huge number of calibration parameters. The approach is based on iteratively solving two different estimation problems using the expectation maximization (em) algorithm. First, the target trajectory and sensor parameters are estimated for a given prior on the parameters. Second, the prior is estimated using the evidence approximation (ea) principleto get a sparse solution. The algorithm successfully jointly estimates the sparsity structure, calibration parameters and target trajectory as demonstrated on both simulated and real data from a microphone network with a passing vehicle.
Engineering and Technology
IdentifiersURN: urn:nbn:se:liu:diva-97377OAI: oai:DiVA.org:liu-97377DiVA: diva2:647282