Analytic Performance Prediction of Track-to-Track Association with Biased Data in Multi-Sensor Multi-Target Tracking Scenarios
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2013-09-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/13/9/12244 |
| _version_ | 1798004006184812544 |
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| author | Xiuming Shan Jian Yang Yue Wang Wei Tian |
| author_facet | Xiuming Shan Jian Yang Yue Wang Wei Tian |
| author_sort | Xiuming Shan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T12:16:43Z |
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| id | doaj.art-f7b8f260724247fab92db93ab9ce6216 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:16:43Z |
| publishDate | 2013-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f7b8f260724247fab92db93ab9ce62162022-12-22T04:24:18ZengMDPI AGSensors1424-82202013-09-01139122441226510.3390/s130912244Analytic Performance Prediction of Track-to-Track Association with Biased Data in Multi-Sensor Multi-Target Tracking ScenariosXiuming ShanJian YangYue WangWei TianAn 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.http://www.mdpi.com/1424-8220/13/9/12244track-to-track association (TTTA)sensor biasesanalytic performance predictionglobal nearest neighbor (GNN) |
| spellingShingle | Xiuming Shan Jian Yang Yue Wang Wei Tian Analytic Performance Prediction of Track-to-Track Association with Biased Data in Multi-Sensor Multi-Target Tracking Scenarios Sensors track-to-track association (TTTA) sensor biases analytic performance prediction global nearest neighbor (GNN) |
| title | Analytic Performance Prediction of Track-to-Track Association with Biased Data in Multi-Sensor Multi-Target Tracking Scenarios |
| title_full | Analytic Performance Prediction of Track-to-Track Association with Biased Data in Multi-Sensor Multi-Target Tracking Scenarios |
| title_fullStr | Analytic Performance Prediction of Track-to-Track Association with Biased Data in Multi-Sensor Multi-Target Tracking Scenarios |
| title_full_unstemmed | Analytic Performance Prediction of Track-to-Track Association with Biased Data in Multi-Sensor Multi-Target Tracking Scenarios |
| title_short | Analytic Performance Prediction of Track-to-Track Association with Biased Data in Multi-Sensor Multi-Target Tracking Scenarios |
| title_sort | analytic performance prediction of track to track association with biased data in multi sensor multi target tracking scenarios |
| topic | track-to-track association (TTTA) sensor biases analytic performance prediction global nearest neighbor (GNN) |
| url | http://www.mdpi.com/1424-8220/13/9/12244 |
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