基于感知代理和移动控制法则的分布式目标跟踪
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摘要
针对由无人自动跟踪机器人构成的移动传感器网络中的目标跟踪问题,提出了一种基于感知代理和移动控制法则的分布式目标跟踪方案。首先,在构建移动机器人代理模型和覆盖感知目标跟踪模型的基础上,提出了一种分布式目标跟踪算法,算法由估计阶段和一致性阶段构成.通过这两个阶段的执行,网络中的全部代理就最大感知置信值以及相应的目标状态和后验协方差矩阵达成一致,从而提高各自预测的性能;其次,针对一般移动代理的移动提出了一种基于势场的控制法则,以确保传感器网络在任何时候都保持连接,使得每个移动代理尽可能接近目标,提高其感知置信值,并避免与目标、环境墙壁和其他代理发生碰撞,从而有助于保持该区域的覆盖率在令人满意的水平。仿真实验结果表明,跟踪方案不仅能够提高跟踪精度,而且还具有较好的覆盖感知性能。
In this paper,perceptual agent and mobile control law based distributed target tracking scheme is proposed to solve the problem of target tracking in mobile sensor networks composed of unmanned automatic tracking robots. Firstly,a distributed target tracking algorithm is proposed on the basis of constructed mobile robot agent model and perceptual-coverage target tracking model. The algorithm consists of estimation phase and consensus phase. Through the execution of these two phases,all the agents in the network achieve an agreement on the maximum perception confidence value,as well as on the corresponding target state and a posteriori covariance matrix,so as to improve the individual prediction performance. Secondly,potential field based control law is proposed for the movement of the general mobile agent so as to ensure that the sensor network is connected at any time,such that each mobile agent approaches the target as close as possible to improve its perception confidence value and to avoid collision with the target,the environment walls and the other agents. This is useful for keeping the coverage of the area at a satisfactory level. Simulation results show that the proposed tracking scheme not only improves the tracking accuracy,but also has a better coverage perception performance.
In this paper,perceptual agent and mobile control law based distributed target tracking scheme is proposed to solve the problem of target tracking in mobile sensor networks composed of unmanned automatic tracking robots. Firstly,a distributed target tracking algorithm is proposed on the basis of constructed mobile robot agent model and perceptual-coverage target tracking model. The algorithm consists of estimation phase and consensus phase. Through the execution of these two phases,all the agents in the network achieve an agreement on the maximum perception confidence value,as well as on the corresponding target state and a posteriori covariance matrix,so as to improve the individual prediction performance. Secondly,potential field based control law is proposed for the movement of the general mobile agent so as to ensure that the sensor network is connected at any time,such that each mobile agent approaches the target as close as possible to improve its perception confidence value and to avoid collision with the target,the environment walls and the other agents. This is useful for keeping the coverage of the area at a satisfactory level. Simulation results show that the proposed tracking scheme not only improves the tracking accuracy,but also has a better coverage perception performance.
引文
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[11] VALICKA C G,REKOSKE R A,STIPANOVIC D M,etal. Multiattribute utility copulas for multi-objectivecoverage control[J]. Paladyn Journal of BehavioralRobotics,2014,5(1):3218-3223.
[12] FU J,WANG J.Robust finite-time containment control ofgeneral linear multi-agent systems under directedcommunication graphs[J]. Journal of the FranklinInstitute,2016,353(12):2670-2689.
[13] DENIZ F,BAGCI H,KORPEOGLU I,et al.An adaptive,energy-aware and distributed fault-tolerant topology-control algorithm for heterogeneous wireless sensornetworks[J].Ad Hoc Networks,2016,44(C):104-117.
[14] ANSARI N A,DESHPANDE U A,MOHAMMAD S P. Anasynchronous algorithm for providing energy efficientcoverage and connectivity in wireless sensor networks[C].IEEE International Conference on Advanced Networksand Telecommunications Systems,2016:1-6.
[15] SRIVASTAVA J R,SUDARSHAN T S B.Energy-efficientcache node placement using genetic algorithm in wirelesssensor networks[J]. Soft Computing,2015,19(11):3145-3158.
[2] KAUSHIK P P G,DAHAKE V D,SINGH B S. Anoverview on energy-aware clustering technique formaximizing wireless sensor network life time[J].International Journal of Engineering Sciences&ResearchTechnology,2014,3(12):212-220.
[3]杨树杰.面向网络化系统的分布式估计及优化算法研究[D].北京:北京邮电大学,2017.YANG SH J. Research on distributed estimation andoptimization algorithms for networked systems[D].Beijing:Beijing University of Posts andTelecommunications,2017.
[4] BOTHA F J,DAALEN C E V,TREURNICHT J. Datafusion of radar and stereo vision for detection and trackingof moving objects[C]. Pattern Recognition Association ofSouth Africa and Robotics and Mechatronics InternationalConference,2016:1-7.
[5] PAOLA D D,PETITTI A,RIZZO A.Distributed Kalmanfiltering via node selection in heterogeneous sensornetworks[J]. International Journal of Systems Science,2015,46(14):2572-2583.
[6] DAS S,MOURA J M F.Distributed Kalman filtering withdynamic observations consensus[J]. IEEE Transactionson Signal Processing,2015,63(17):4458-4473.
[7] ILIC N,STANKOVIC M S,STANKOVIC S S. Adaptiveconsensus-based distributed target tracking in sensornetworks with limited sensing range[J]. IEEETransactions on Control Systems Technology,2014,22(2):778-785.
[8]陈文晓.无线传感器网络中分布式LMS算法的研究[D].西安:西安邮电大学,2014.CHEN W X.The research on distributed LMS algorithm inwireless sensor network[D]. Xi'an:Xi'an University ofPosts and Telecommunications,2014.
[9] UMALE M,MARKANDE S D.Energy efficient routing algorithmon the target tracking in wireless sensor network[C].International Conference on Information Processing,2015:176-181.
[10] GEORGE D E,UNNIKRISHNAN A.On the divergence ofinformation filter for multi sensors fusion[J]. InformationFusion,2016,27(C):76-84.
[11] VALICKA C G,REKOSKE R A,STIPANOVIC D M,etal. Multiattribute utility copulas for multi-objectivecoverage control[J]. Paladyn Journal of BehavioralRobotics,2014,5(1):3218-3223.
[12] FU J,WANG J.Robust finite-time containment control ofgeneral linear multi-agent systems under directedcommunication graphs[J]. Journal of the FranklinInstitute,2016,353(12):2670-2689.
[13] DENIZ F,BAGCI H,KORPEOGLU I,et al.An adaptive,energy-aware and distributed fault-tolerant topology-control algorithm for heterogeneous wireless sensornetworks[J].Ad Hoc Networks,2016,44(C):104-117.
[14] ANSARI N A,DESHPANDE U A,MOHAMMAD S P. Anasynchronous algorithm for providing energy efficientcoverage and connectivity in wireless sensor networks[C].IEEE International Conference on Advanced Networksand Telecommunications Systems,2016:1-6.
[15] SRIVASTAVA J R,SUDARSHAN T S B.Energy-efficientcache node placement using genetic algorithm in wirelesssensor networks[J]. Soft Computing,2015,19(11):3145-3158.