Anti-bias Track Association Algorithm of Radar and Electronic Support Measurements Based on Track Vectors Hierarchical Clustering

Baozhu LI; Lin ZHANG; Yunlong DONG; Jian GUAN

doi:10.11999/JEIT180714

Volume 41 Issue 6

Jun. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(6): 1310-1316

Baozhu LI, Lin ZHANG, Yunlong DONG, Jian GUAN. Anti-bias Track Association Algorithm of Radar and Electronic Support Measurements Based on Track Vectors Hierarchical Clustering[J]. Journal of Electronics & Information Technology, 2019, 41(6): 1310-1316. doi: 10.11999/JEIT180714

Citation:

Baozhu LI, Lin ZHANG, Yunlong DONG, Jian GUAN. Anti-bias Track Association Algorithm of Radar and Electronic Support Measurements Based on Track Vectors Hierarchical Clustering[J]. Journal of Electronics & Information Technology, 2019, 41(6): 1310-1316. doi: 10.11999/JEIT180714

Citation:

Baozhu LI, Lin ZHANG, Yunlong DONG, Jian GUAN. Anti-bias Track Association Algorithm of Radar and Electronic Support Measurements Based on Track Vectors Hierarchical Clustering[J]. Journal of Electronics & Information Technology, 2019, 41(6): 1310-1316. doi: 10.11999/JEIT180714

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Anti-bias Track Association Algorithm of Radar and Electronic Support Measurements Based on Track Vectors Hierarchical Clustering

doi: 10.11999/JEIT180714

Institute of Information Fusion, Naval Aviation University, Yantai 264001, China

Funds: The National Natural Science Foundation of China (61871392, 61531020, 61871391, 61471382, U1633122), China Postdoctoral Science Foundation (2017M620862), The Special Funds of Taishan Scholars Construction Engineering

Received Date: 2018-07-17
Rev Recd Date: 2018-11-06

Available Online: 2018-11-23

Publish Date: 2019-06-01

Abstract

Abstract

To address track-to-track association problem of radar and Electronic Support Measurements (ESM) in the presence of sensor biases and different targets reported by different sensors, an anti-bias track-to-track association algorithm based on track vectors hierarchical clustering is proposed. Firstly, the equivalent measurement is derived in the Modified Polar Coordinates (MPC). Linear relationship between state estimates and real states, sensor biases, measurement errors are established based on the approximate expansion of the equivalent measurement. The track vectors are obtained by the real state cancellation method. The homologous tracks are extracted by the method of track vectors hierarchical clustering, according to the statistical characteristics of Gaussian random vectors. The effectiveness of the proposed algorithm is verified by Monte Carlo simulation experiments in the presence of sensor biases, targets densities and detection probabilities.
- Radar,
- Electronic Support Measurements (ESM),
- Track-to-track association,
- Sensor bias,
- Track vectors,
- Hierarchical clustering

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References(19)

References

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