Abstract
Multilateration (MLAT) is the de facto standard to localize points of
interest (POIs) in navigation and surveillance systems. Despite sensors
being inherently noisy, most existing techniques i) are oblivious to
noise patterns in sensor measurements; and ii) only provide point
estimates of the POI’s location. This often results in unreliable
estimates with high variance, i.e., that are highly sensitive to
measurement noise. To overcome this caveat, we advocate the use of
Bayesian modeling. Using Bayesian statistics, we provide a comprehensive
guide to handle uncertainties in MLAT. We provide principled choices for
the likelihood function and the prior distributions. Inference within
the resulting model follows standard MCMC techniques. Besides coping
with unreliable measurements, our framework can also deal with sensors
whose location is not completely known, which is an asset in mobile
systems. The proposed solution also naturally incorporates multiple
measurements per reference point, a common practical situation that is
usually not handled directly by other approaches. Comprehensive
experiments with both synthetic and real-world data indicate that our
Bayesian approach to the MLAT task provides better position estimation
and uncertainty quantification when compared to the available
alternatives.