Bayesian Multilateration
نویسندگان
چکیده
Multilateration (MLAT) is the de facto technique to localize points of interest (POIs) in navigation and surveillance systems. Despite sensors being inherently noisy, most existing techniques i) are oblivious noise patterns sensor measurements; ii) only provide point estimates POI. This often results unreliable with high variance, xmlns:xlink="http://www.w3.org/1999/xlink">i.e ., that highly sensitive measurement noise. To overcome this caveat, we advocate use Bayesian modeling. Using statistics, a comprehensive guide handle uncertainties MLAT, including principled choices for likelihood function prior distributions. Notably, resulting model easy implement can leverage off-the-shelf Markov Chain Monte Carlo (MCMC) software inference. Besides coping measurements, our framework also deal whose location not completely known, which an asset mobile Our solution naturally incorporates multiple measurements per reference point, common practical situation usually handled directly by other approaches. Comprehensive experiments both synthetic real-world data indicate approach MLAT task provides better position estimation uncertainty quantification when compared available alternatives.
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ژورنال
عنوان ژورنال: IEEE Signal Processing Letters
سال: 2022
ISSN: ['1558-2361', '1070-9908']
DOI: https://doi.org/10.1109/lsp.2022.3161122