Abstract
An analytic method for predicting the performance of track-to-track association (TTTA) with biased data in multi-sensor multi-target tracking scenarios is proposed in this paper. The proposed method extends the existing results of the bias-free situation by accounting for the impact of sensor biases. Since little insight of the intrinsic relationship between scenario parameters and the performance of TTTA can be obtained by numerical simulations, the proposed analytic approach is a potential substitute for the costly Monte Carlo simulation method. Analytic expressions are developed for the global nearest neighbor (GNN) association algorithm in terms of correct association probability. The translational biases of sensors are incorporated in the expressions, which provide good insight into how the TTTA performance is affected by sensor biases, as well as other scenario parameters, including the target spatial density, the extraneous track density and the average association uncertainty error. To show the validity of the analytic predictions, we compare them with the simulation results, and the analytic predictions agree reasonably well with the simulations in a large range of normally anticipated scenario parameters.
Highlights
In distributed multi-sensor surveillance systems, the process of associating sets of local estimates from multiple sensors is a fundamental problem, named track-to-track association (TTTA) [1,2]
Little insight can be obtained for the intrinsic relationship between the relevant scenario parameters and the performance based on numerical simulations
The novelty of this paper is to extend these results to the TTTA problem with biased data in multi-sensor multi-target tracking applications
Summary
In distributed multi-sensor surveillance systems, the process of associating sets of local estimates from multiple sensors is a fundamental problem, named track-to-track association (TTTA) [1,2]. It is valuable for predicting the performance of TTTA, in multi-sensor tracking system design, and in subsequent situation assessment and on-line resource allocation, such as sensor tasking. Analytic prediction of data association performance, which does not resort to detailed and costly simulations, is of significant practical and theoretical importance. Saha provided a methodology for predicting the performance of a logic based ESM/radar TTTA algorithm [5].
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