This paper develops a general theory of\ud validation gating for non-linear non-Gaussian mod-\ud els. Validation gates are used in target tracking\ud to cull very unlikely measurement-to-track associa-\ud tions, before remaining association ambiguities are\ud handled by a more comprehensive (and expensive)\ud data association scheme. The essential property of\ud a gate is to accept a high percentage of correct associ-\ud ations, thus maximising track accuracy, but provide\ud a su±ciently tight bound to minimise the number of\ud ambiguous associations.\ud For linear Gaussian systems, the ellipsoidal vali-\ud dation gate is standard, and possesses the statistical\ud property whereby a given threshold will accept a cer-\ud tain percentage of true associations. This property\ud does not hold for non-linear non-Gaussian models.\ud As a system departs from linear-Gaussian, the ellip-\ud soid gate tends to reject a higher than expected pro-\ud portion of correct associations and permit an excess\ud of false ones. In this paper, the concept of the ellip-\ud soidal gate is extended to permit correct statistics for\ud the non-linear non-Gaussian case. The new gate is\ud demonstrated by a bearing-only tracking example
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