面向机动目标跟踪的多传感器长时调度策略
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摘要
为解决杂波环境下机动目标跟踪以及系统辐射风险控制的问题,提出了一种面向机动目标跟踪的多传感器长时调度策略.该方法首先以交互式多模型和概率数据关联算法为基础,估计杂波环境下机动目标跟踪精度.然后以辐射度影响量化辐射代价、推导有限时域内辐射代价,以后验克拉美-罗下界衡量目标跟踪性能、预测机动目标有限时域内后验克拉美-罗下界.最后,引入传感器切换代价,考虑跟踪精度约束,建立基于代价函数和后验克拉美-罗下界的多传感器长时调度策略,并将该约束调度问题转化为决策树优化问题,采用阈值剪枝搜索技术求解最优策略.仿真结果表明:该方法验证了所提策略的有效性,与标准代价搜索相比,所提搜索算法能够以辐射风险略上升为代价,显著降低节点打开数、加快搜索空间;与随机调度、最近调度和贪婪调度相比,所提调度策略能够在满足跟踪任务需求下获得更低的辐射代价;与随机调度和贪婪调度相比,所提调度策略切换代价更低,有效克服了传感器频繁调度问题,更利于实际实现.
To control the system radiation risk and track the maneuvering target in clutter, a non-myopic multi-sensor scheduling policy for maneuvering target tracking is proposed. Firstly, the maneuvering target tracking accuracy in clutter was estimated by the interacting multiple model and probability data association(IMMPDA) algorithm. Secondly, the radiation cost was quantized by the emission level impact(ELI) and the posterior carmér-rao lower bound(PCRLB) was utilized to represent the target tracking performance. Then the radiation cost and the PCRLB over the future finite time horizon were predicted, respectively. Finally, considering the switching cost and the tracking accuracy constraint, a non-myopic multi-sensor scheduling policy with cost function and PCRLB was set up. The constrained scheduling problem was converted to a decision optimization problem which can be solved by a search algorithm with threshold pruning technique. Simulation results show the effectiveness of the proposed policy. Compared with the uniform cost search(UCS), the proposed search algorithm can reduce the number of nodes opened and improve the search speed with a slightly increased radiation cost. Compared with random scheduling, closest scheduling, and greedy scheduling, the proposed policy can obtain lower radiation cost while satisfying the target tracking requirement. Furthermore, the proposed policy also has the lowest switching cost, and the sensor scheduling frequency has been reduced effectively.
To control the system radiation risk and track the maneuvering target in clutter, a non-myopic multi-sensor scheduling policy for maneuvering target tracking is proposed. Firstly, the maneuvering target tracking accuracy in clutter was estimated by the interacting multiple model and probability data association(IMMPDA) algorithm. Secondly, the radiation cost was quantized by the emission level impact(ELI) and the posterior carmér-rao lower bound(PCRLB) was utilized to represent the target tracking performance. Then the radiation cost and the PCRLB over the future finite time horizon were predicted, respectively. Finally, considering the switching cost and the tracking accuracy constraint, a non-myopic multi-sensor scheduling policy with cost function and PCRLB was set up. The constrained scheduling problem was converted to a decision optimization problem which can be solved by a search algorithm with threshold pruning technique. Simulation results show the effectiveness of the proposed policy. Compared with the uniform cost search(UCS), the proposed search algorithm can reduce the number of nodes opened and improve the search speed with a slightly increased radiation cost. Compared with random scheduling, closest scheduling, and greedy scheduling, the proposed policy can obtain lower radiation cost while satisfying the target tracking requirement. Furthermore, the proposed policy also has the lowest switching cost, and the sensor scheduling frequency has been reduced effectively.
引文
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[12]乔成林, 单甘霖, 段修生, 等. 面向跟踪任务需求的主动传感器调度方法[J]. 系统工程与电子技术, 2017, 39(11): 2515QIAO Chenglin, SHAN Ganlin, DUAN Xiusheng, et al. Scheduling algorithm of active sensors for tracking task requirement[J]. Systems Engineering and Electronics, 2017, 39(11): 2515. DOI:10.3969/j.issn.1001-506X.2017.11.18
[13]HOULES A, BAR-SHALOMA Y. Multisensor tracking of a maneuvering target in clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 1989, 25(2): 176. DOI:10.1109/7.18679
[14]ARASARATNAM I, HAYKIN S. Cubature kalman filters[J]. IEEE Transactions on Automatic Control, 2009, 54(6): 1254. DOI:10.1109/TAC.2009.2019800
[15]SONG H, XIAO M, XIAO J, et al. A POMDP approach for scheduling the usage of airborne electronic countermeasures in air operations[J]. Aerospace Science and Technology, 2016, 48: 86. DOI:10.1016/j.ast.2015.11.001
[16]LI Y, KRAKOW L W, CHONG E K P, et al. Approximate stochastic dynamic programming for sensor scheduling to track multiple targets[J]. Digital Signal Processing, 2009, 19(6): 978. DOI:10.1016/j.dsp.2007.05.004
[17]张召友, 郝燕玲, 吴旭. 3种确定性采样非线性滤波算法的复杂度分析[J]. 哈尔滨工业大学学报, 2013, 45(12): 111ZHANG Zhaoyou, HAO Yanling, WU Xu. Complexity analysis of three deterministic sampling nonlinear filtering algorithms[J]. Journal of Harbin Institute of Technology, 2013, 45(12): 111
[2]NAYEBI-ASTANEH A, PARIZ N, NAGHIBI-SISTANI M. Adaptive node scheduling under accuracy constraint for wireless sensor nodes with multiple bearings-only sensing units[J]. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(2): 1547. DOI:10.1109/TAES.2014.130476
[3]LIU Zhigang, WANG Jinkuan, XUE Yanbo. PCRLB-based sensor selection for maneuvering target tracking in range-based sensor networks[J]. Future Generation Computer Systems, 2013, 29(7): 1751. DOI:10.1016/j.future.2012.01.003
[4]KESHAVARZ-MOHAMMADIYAN A, KHALOOZADEH H. Interacting multiple model and sensor selection algorithms for manoeuvring target tracking in wireless sensor networks with multiplicative noise[J]. International Journal of Systems Science, 2017, 48(5): 899. DOI: 10.1080/00207721.2016.1177128
[5]LIU Bin, JI Chunlin, ZHANG Yangyang, et al. Blending sensor scheduling strategy with particle filter to track a smart target[J].Wireless Sensor Network, 2009, 1(4): 300. DOI:10.4236/wsn.2009.14037
[6]吴卫华, 江晶, 高岚. 机载雷达辅助无源传感器对杂波环境下机动目标跟踪[J]. 控制与决策, 2015, 30(2): 277 WU Weihua, JIANG Jing, GAO Lan. Tracking maneuvering target in clutter with passive sensor aided by airborne radar[J].Control and Decision, 2015, 30(2): 277. DOI:10.13195/j.kzyjc.2013.1781
[7]吴巍, 王国宏, 双炜, 等. 多机载平台多目标跟踪与辐射控制[J]. 系统工程与电子技术, 2012, 34(3): 495WU Wei, WANG Guohong, SHUANG Wei, et al. Multi-airborne-platform multi-target tracking and radiation control technology[J]. Systems Engineering and Electronics, 2012, 34(3): 495. DOI:10.3969/j.issn.1001-506X.2012.03.12
[8]ZHANG Zining, SHAN Ganlin. Non-myopic sensor scheduling to track multiple reactive targets[J]. IET Signal Processing, 2015, 9(1): 37. DOI:10.1049/iet-spr.2013.0187
[9]SHI Chengang, ZHOU Jianjiang, WANG Fei. Adaptive resource management algorithm for target tracking in radar network based on low probability of intercept[J]. Multidimensional Systems and Signal Processing, 2018, 29(4): 1203. DOI:10.1007/s11045-017-0494-8
[10]KRISHNAMURTHY V. Emission management for low probability intercept sensors in network centric warfare[J]. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(1): 133. DOI:10.1109/TAES.2005.1413752
[11]SHAN Ganglin, ZHANG Zining. Non-myopic sensor scheduling for low radiation risk tracking using mixed POMDP[J]. Transactions of the Institute of Measurement and Control, 2015, 39(2): 230. DOI:10.1177/ 0142331215604211.
[12]乔成林, 单甘霖, 段修生, 等. 面向跟踪任务需求的主动传感器调度方法[J]. 系统工程与电子技术, 2017, 39(11): 2515QIAO Chenglin, SHAN Ganlin, DUAN Xiusheng, et al. Scheduling algorithm of active sensors for tracking task requirement[J]. Systems Engineering and Electronics, 2017, 39(11): 2515. DOI:10.3969/j.issn.1001-506X.2017.11.18
[13]HOULES A, BAR-SHALOMA Y. Multisensor tracking of a maneuvering target in clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 1989, 25(2): 176. DOI:10.1109/7.18679
[14]ARASARATNAM I, HAYKIN S. Cubature kalman filters[J]. IEEE Transactions on Automatic Control, 2009, 54(6): 1254. DOI:10.1109/TAC.2009.2019800
[15]SONG H, XIAO M, XIAO J, et al. A POMDP approach for scheduling the usage of airborne electronic countermeasures in air operations[J]. Aerospace Science and Technology, 2016, 48: 86. DOI:10.1016/j.ast.2015.11.001
[16]LI Y, KRAKOW L W, CHONG E K P, et al. Approximate stochastic dynamic programming for sensor scheduling to track multiple targets[J]. Digital Signal Processing, 2009, 19(6): 978. DOI:10.1016/j.dsp.2007.05.004
[17]张召友, 郝燕玲, 吴旭. 3种确定性采样非线性滤波算法的复杂度分析[J]. 哈尔滨工业大学学报, 2013, 45(12): 111ZHANG Zhaoyou, HAO Yanling, WU Xu. Complexity analysis of three deterministic sampling nonlinear filtering algorithms[J]. Journal of Harbin Institute of Technology, 2013, 45(12): 111