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Sensors 2013, 13(9), 12244-12265; doi:10.3390/s130912244
Article

Analytic Performance Prediction of Track-to-Track Association with Biased Data in Multi-Sensor Multi-Target Tracking Scenarios

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Received: 23 July 2013; in revised form: 9 September 2013 / Accepted: 9 September 2013 / Published: 12 September 2013
(This article belongs to the Section Physical Sensors)
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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.
Keywords: track-to-track association (TTTA); sensor biases; analytic performance prediction; global nearest neighbor (GNN) track-to-track association (TTTA); sensor biases; analytic performance prediction; global nearest neighbor (GNN)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Tian, W.; Wang, Y.; Shan, X.; Yang, J. Analytic Performance Prediction of Track-to-Track Association with Biased Data in Multi-Sensor Multi-Target Tracking Scenarios. Sensors 2013, 13, 12244-12265.

AMA Style

Tian W, Wang Y, Shan X, Yang J. Analytic Performance Prediction of Track-to-Track Association with Biased Data in Multi-Sensor Multi-Target Tracking Scenarios. Sensors. 2013; 13(9):12244-12265.

Chicago/Turabian Style

Tian, Wei; Wang, Yue; Shan, Xiuming; Yang, Jian. 2013. "Analytic Performance Prediction of Track-to-Track Association with Biased Data in Multi-Sensor Multi-Target Tracking Scenarios." Sensors 13, no. 9: 12244-12265.



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