低信噪比条件下多节点声呐目标跟踪算法
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摘要
低信噪比条件下,单个主动或单个被动声呐节点难以实现目标跟踪。多节点声呐系统希望通过增加探测节点提高系统探测能力,但其性能提高与否取决于选择合适的数据融合算法。该文利用双节点声呐实验数据研究低信噪比条件下的目标跟踪方法,采用"结合置信度水平的表决融合"算法对多个探测节点得到的数据进行融合,既考虑了目标回波信号的信噪比特性,又考虑了目标运动的连续性特征,还考虑了各节点探测结果的决策优化,最终实现较高精度的目标跟踪。算法实现了数据级、特征级和决策级的统一、融合,通过对判决依据进行量化、分层,简化判决的复杂性。实验数据处理结果表明,该方法能较好地解决低信噪比条件下多节点声呐目标跟踪问题,目标跟踪精度较高。
It's difficult for single active or single passive sonar to track underwater targets with low signal noise ratio(SNR). So adding more detecting nodes into multistatic sonar system is expected. But whether it can improve the detection capabilities of the sonar system lies on designing appropriate fusion algorithm.Experimental data of bistatic sonar system under low SNR was used to investigate target tracking problems.And a voting fusion algorithm combined with confidence level was put forward. This algorithm takes into account not only the SNR of target echoes, but also the continuity characteristic of target motion, and also the decision methods of the detection results from many nodes. Finally, higher precision tracking results was achieved. This algorithm realizes the unification and fusion of data level, feature level and decision level.By quantifying the decision criterion, the judgments of target tracks were simplified. Processing results of experimental data show that the proposed method can solve the target tracking problem detected by multistatic sonar with lower SNR, and the target tracking accuracy is higher.
It's difficult for single active or single passive sonar to track underwater targets with low signal noise ratio(SNR). So adding more detecting nodes into multistatic sonar system is expected. But whether it can improve the detection capabilities of the sonar system lies on designing appropriate fusion algorithm.Experimental data of bistatic sonar system under low SNR was used to investigate target tracking problems.And a voting fusion algorithm combined with confidence level was put forward. This algorithm takes into account not only the SNR of target echoes, but also the continuity characteristic of target motion, and also the decision methods of the detection results from many nodes. Finally, higher precision tracking results was achieved. This algorithm realizes the unification and fusion of data level, feature level and decision level.By quantifying the decision criterion, the judgments of target tracks were simplified. Processing results of experimental data show that the proposed method can solve the target tracking problem detected by multistatic sonar with lower SNR, and the target tracking accuracy is higher.
引文
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[15]Li Y,Chen X H,Sun C Y.A tracking method based on particle filter for multistatic sonar system[C].IEEE International Conference on Signal Processing,Communication&Computing,Guilin,China,2014:162-165.
[16]Yang L,Yang L,Ho K C.Moving target localization in multistatic sonar using time delays,Doppler shifts and arrival angles[C].IEEE International Conference on Acoustics,Speech and Signal Processing,New Orleans,LA,USA,2016:3399-3403.
[2]Coraluppi S,Grimmett D.Multistatic sonar tracking[C].Proceedings of the SPIE Conference on Signal Processing,Sensor Fusion,and Target Recognition XII,Orlando FL,USA,2003,5096:399-410.
[3]Coraluppi S,Carthel C.Multi-hypothesis sonar tracking[C].Proceedings of the 7th International Conference on Information Fusion,Stockholm,Sweden,2004.
[4]Balasingam B,Baum M,Willett P.Random finite set particle filter for source enumeration and direction-of-arrival tracking using sonar arrays[C].2017 20th International Conference on Information Fusion,Xi’an,China,2017:1-6.
[5]Coraluppi S,Carthel C.Distributed tracking in multistatic sonar[J].IEEE Transactions on Aerospace and Electronic Systems,2005,41(3):1138-1147.
[6]MunafòA,Furfaro T,Ferri G,et al.Supporting AUVlocalisation through next generation underwater acoustic networks:results from the field[C].IEEE/RSJ International Conference on Intelligent Robots and Systems 2016,Daejeon,South Korea,2016:1328-1333.
[7]Coraluppi S,Carthel C.Performance limits of real-time contact-based tracking[C].IEEE OCEANS 2007,Aberdeen,UK,June 2007:1-6.
[8]Lepage K D,Goldhahn R,Alves J,et al.Autonomous networked anti-submarine warfare research and development at CMRE[C].IEEE OCEANS 2015,Genova,Italy,May 2015:1-6.
[9]Blanding W,Willett P,Coraluppi S.Sequential ML for multistatic sonar tracking[C].IEEE OCEANS 2007,Aberdeen,UK,2007:1-6.
[10]Grimmett D,Coraluppi S.Contact-level multistatic sonar data simulator for tracker performance assessment[C].IEEE 2006 9th International Conference on Information Fusion,Florence,Italy,2006:1-7.
[11]Braca P,Goldhahn R,Ferri G,et al.Distribution information fusion in multistatic sensor networks for underwater surveillance[J].IEEE Sensor Journal,2016,16(11):4003-4014.
[12]Papa G,Braca P,Horn S,et al.Adaptive Bayesian tracking with unknown time-varying sensor network performance[C].IEEE 2006 9th International Conference on Acoustic Speech Signal Process,Brisbane,QLD,Australia,2015:2534-2538.
[13]Sildam J,Lepage K D.Ambiguity reduction of underwater targets in framework of topic modeling[C].IEEE2005 18th International Conference on Information Fusion,Washington,DC,USA,2015:2017-2024.
[14]Ferri G,MunafòA,Alves J,et al.A data-driven control strategy in synergy with continuous active sonar for littoral underwater surveillance[C].IEEE OCEANS 2016MTS/IEEE Monterey,Monterey,CA,USA,2016:1-7.
[15]Li Y,Chen X H,Sun C Y.A tracking method based on particle filter for multistatic sonar system[C].IEEE International Conference on Signal Processing,Communication&Computing,Guilin,China,2014:162-165.
[16]Yang L,Yang L,Ho K C.Moving target localization in multistatic sonar using time delays,Doppler shifts and arrival angles[C].IEEE International Conference on Acoustics,Speech and Signal Processing,New Orleans,LA,USA,2016:3399-3403.