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This paper develops an accurate, efficient filter (called the `TT filter')for tracking multiple targets using a spatially-distributed network ofamplitude sensors that estimate distance but not direction. Several innovationsare included in the algorithm that increase accuracy and reduce complexity. Forinitial target acquisition once tracking begins, a constrained Hessian searchis used to find the maximum likelihood (ML) target vector, based on themeasurement model and a Gaussian approximation of the prior. The Hessian at theML vector is used to give an initial approximation of the negative loglikelihood for the target vector distribution: corrections are applied if theHessian is not positive definite due to the near-far problem. Furthercorrections are made by applying a transformation that matches the knownnonlinearity introduced by distance-only sensors. A set of integration pointsis constructed using this information, which are used to estimate the mean andmoments of the target vector distribution. Results show that the TT filtergives superior accuracy and lower complexity than previous alternatives such asKalman-based or particle filters.

High Performance Low Complexity Multitarget Tracking Filter for a Array of Non-directional Sensors