摘  要

随着定位技术的发展和室内定位服务需求的不断增加，安全地获取高精度的室内位置信息也越发重要。超宽带（Ultra-Wide-Band，UWB）技术因具有定位精度高、抗干扰能力强、安全性能好等特点，被广泛用于研究和实现室内复杂环境下的精确定位。因此，本文着重研究基于UWB的室内定位和跟踪算法，目标是改善算法性能，提高定位跟踪精度。

（1）针对静态目标定位存在的非视距（NLOS）误差和多径干扰，导致定位性能下降的问题，提出引力场优化BP神经网络的超宽带定位算法。利用BP神经网络较强的非线性逼近能力，解决到达时间差（TDOA）算法定位精度随测量误差的增大而降低、时间复杂度高等问题。进一步结合引力场算法利用其移动因子使权值和阈值快速又集中地分布在极值附近，提升了网络的收敛速度；同时，利用其自转因子使临近极值的权值和阈值随机远离极值，有效的缓解基于BP神经网络的超宽带定位算法收敛速度慢、容易陷入局部极值的问题。实验结果表明，提出的定位算法均方根误差为7.95cm，基本满足了室内高精度定位的需求。

（2）针对利用粒子滤波（PF）算法对移动目标跟踪时，存在粒子权值退化，造成跟踪性能下降的问题，提出一种基于UWB的麻雀搜索优化粒子滤波跟踪算法。为了缓解麻雀搜索算法后期迭代易陷入局部极值的问题，引入高斯变异算子与贪婪准则，增强算法跃出局部空间的能力，得到改进的麻雀搜索算法。通过改进的麻雀搜索算法对序贯重要性采样后的粒子进行优化，使粒子大部分集中于高似然区域，且保留少数的粒子在低似然区，解决粒子权值退化和多样性丧失的问题。为验证算法的跟踪性能，模拟直线路径和回型路径两种情况下的跟踪仿真实验。实验结果表明，在直线路径和回型路径中，提出的跟踪算法均方误差分别为0.5876m和1.5556m，有效缓解了粒子权值退化的问题，达到了提升待测节点跟踪精度的目标，对实际环境中精确地实现动态目标跟踪具有一定的参考价值。

ABSTRACT

With the development of positioning technology and the increasing demand for indoor positioning services, it is increasingly important to securely obtain high-precision indoor location information. Ultra-Wide-Band (UWB) technology is widely used to study and achieve precise positioning in complex indoor environments because of its high positioning accuracy, strong anti-interference capability, and good safety performance. Therefore, this paper focuses on UWB-based indoor positioning and tracking algorithms, with the goal of improving the algorithm performance and increasing the positioning and tracking accuracy.

(1) To address the problems of non-line-of-sight (NLOS) errors and multipath interference in static target localization, which lead to the degradation of localization performance, an ultra-wideband localization algorithm with gravitational field optimized BP neural network is proposed. The strong nonlinear approximation capability of BP neural network is utilized to solve the problems that the localization accuracy of time difference of arrival (TDOA) algorithm decreases with the increase of measurement error and the high time complexity. The gravitational field algorithm is further combined with its movement factor to make the weights and thresholds quickly and centrally distributed near the extremes, which improves the convergence speed of the network; at the same time, its rotation factor is used to make the weights and thresholds near the extremes randomly move away from the extremes, which effectively alleviates the problems of slow convergence and easy to fall into local extremes of the BP neural network-based ultra-wideband localization algorithm. The experimental results show that the root mean square error of the proposed localization algorithm is 7.95 cm, which basically meets the demand of indoor high-precision localization.

(2) A sparrow search optimized particle filter tracking algorithm based on UWB is proposed to address the problem of degradation of particle weights when tracking moving targets using particle filter (PF) algorithm, which causes degradation of tracking performance. In order to alleviate the problem that the late iterations of the sparrow search algorithm are prone to fall into local extremes, a Gaussian variational operator with a greedy criterion is introduced to enhance the ability of the algorithm to leap out of the local space, and an improved sparrow search algorithm is obtained. The improved sparrow search algorithm optimizes the particles after sequential importance sampling, so that most of the particles are concentrated in the high-likelihood region and a few particles are retained in the low-likelihood region to solve the problems of degradation of particle weights and loss of diversity. In order to verify the tracking performance of the algorithm, tracking simulation experiments are simulated for two cases: straight-line path and back-shaped path. The experimental results show that the mean square error of the proposed tracking algorithm is 0.5876m and 1.5556m in the straight-line path and back-type path, respectively, which effectively alleviates the problem of particle weight degradation and achieves the goal of improving the tracking accuracy of the node to be measured, and has certain reference value for accurately realizing dynamic target tracking in practical environments.

[1] 张宁,付炜平,孟荣,李光,赵智龙,杨家骏,王淳灏.室内空间定位方法研究综述[J].科学技术与工程,2022,22(3):882-892.

[2] 张子明,王从庆. 基于超宽带技术的室内定位系统应用开发[J]. 单片机与嵌入式系统应用, 2021,21(4):76-80.

[3] 刘健,曹冲. 全球卫星导航系统发展现状与趋势[J]．导航定位学报，2020，8(01):1-8．

[4] 王金恒,陈坚城,莫志裕,田桂丰.基于微信小程序的校园导航系统的设计与实现[J].电脑编程技巧与维护,2021(6):77-79.

[5] Yu T, Wang R. Design and implementation of a smart elderly positioning management system based on wireless communication network[J]. EURASIP Journal on Wireless Communications and Networking, 2021, 2021(1): 1-23.

[6] Farcas A M, Zaidi H Q, Wleklinski N P, et al. Implementing a Patient Tracking System in a Large EMS System[J]. Prehospital Emergency Care, 2021,26(2): 1-9.

[7] Raghavan K, George S M, Prahlad A D, et al. Child tracking and monitoring[J]. Journal of Computational and Theoretical Nanoscience, 2020, 17(9-10): 4041-4044.

[8] Zhao Z, Zhang M, Xu G, et al. Logistics sustainability practices: an IoT-enabled smart indoor parking system for industrial hazardous chemical vehicles[J]. International Journal of Production Research, 2020, 58(24): 7490-7506.

[9] 张永寿,赵晓辰,朱传玉,阎洪辉.基于医疗设备位置信息管理的追踪定位仪设计与应用[J].中国医学装备,2019,16(2):89-91.

[10] 前晋,陈淑荣.基于iBeacon的商场室内定位及导航系统设计与实现[J].微型电脑应用,2019,35(1):76-79.

[11] 赵林,孙增荣,王纪强,刘统玉.基于超宽带技术的矿用人员精准定位系统[J].山东科学,2021,34(2):48-53.

[12] 王宁,王坚,韩厚增.UWB应急组网定位系统的设计与实现[J].测绘科学,2020,45(6):73-79.

[13] 张日明,周欢,龙飞阳.基于UWB技术的井下快速移动目标高精度定位系统的设计与实现[J].物联网技术,2022,12(2):12-15.

[14] Xie X, Lin R, Yu B, et al. Internet of Things assisted radio frequency identification based mine safety management platform[J]. Computational Intelligence, 2021, 37(3): 1322-1337.

[15] 靳晓东.ZigBee技术在地下建筑火灾扑救消防员定位中的应用[J].中国科技信息,2021(22):37-38.

[16] 邓中亮,尹露,唐诗浩,刘延旭,宋汶轩. 室内定位关键技术综述[J].导航定位与授时, 2018, 5(3): 14-23.

[17] 田家英,张志华.基于近邻法的WIFI室内定位改进算法研究[J].测绘工程,2018,27(12):31-36.

[18] 朱正伟,蒋威,张贵玲,诸燕平,朱晨阳.基于RSSI的室内WiFi定位算法[J].计算机工程与设计,2020,41(10):2958-2962.

[19] 陈龙鹏.基于RFID技术的室内定位方法研究[D].南京邮电大学,2020.

[20] 梁胜涛,陈良坤,舒奇奇,贺凯旋.基于无线通信与RFID定位的智能仓储系统的设计[J].自动化技术与应用,2020,39(1):169-172.

[21] Kim Geok T, Zar Aung K, Sandar Aung M, et al. Review of indoor positioning: Radio wave technology[J]. Applied Sciences, 2020, 11(1): 279-323.

[22] 何文华.基于ZigBee技术的超市智能定位导购系统设计与应用研究[J].重庆理工大学学报(自然科学),2020,34(7):185-191.

[23] 曹子腾,郭阳,赵正旭,刘曼云.室内定位技术研究综述[J].计算机技术与发展,2020,30(6):202-206.

[24] Puckdeevongs A, Tripathi N K, Witayangkurn A, et al. Classroom attendance systems based on bluetooth low energy indoor positioning technology for smart campus[J]. Information, 2020, 11(6): 329-347.

[25] 黄月天.超宽带室内定位关键技术分析[J].电子制作,2020(Z2):40-41+84.

[26] 王飞.基于UWB技术的矿井精确定位系统[J].煤矿安全,2021,52(07):99-102.

[27] 杨刚,朱士玲,李强,赵克松,赵杰,郭建.基于UWB的消防员室内协同定位算法[J].计算机工程与应用,2022,58(4):106-117.

[28] Dardari D, Closas P, Djurić P M. Indoor tracking: Theory, methods, and technologies[J]. IEEE Transactions on Vehicular Technology, 2015, 64(4): 1263-1278.

[29] 宋成全.基于超宽带室内定位系统的研究与实现[D].燕山大学,2019.

[30] 武志凯.基于UWB的室内定位技术及系统研究[D].中北大学,2021.

[31] 嵇茂祥.UWB室内定位系统研究与实现[D].华东师范大学,2017.

[32] Campbell B, Dutta P, Kempke B, et al. Decawave: Exploring state of the art commercial localization[J]. Ann Arbor, 2015, 1001(4): 48109-48111.

[33] Thakur Nirmalya,Han Chia Y. Multimodal Approaches for Indoor Localization for Ambient Assisted Living in Smart Homes[J].Information,2021,12(3) :114-114.

[34] 潘祥生,陈晓晶.矿用智能巡检机器人关键技术研究[J].工矿自动化,2020,46(10):43-48.

[35] 潘有顺,彭天昊,康万杰.基于UWB的消防救援定位系统优化设计[J].消防科学与技术,2019,38(6):867-870.

[36] Poulose A,Han D S.UWB indoor localization using deep learning LSTM networks[J].Applied Sciences,2020,10(18):6290-6313.

[37] 王生亮,刘根友,高铭,等.改进的自适应遗传算法在TDOA定位中的应用[J].系统工程与电子技术,2019,41(2):254-258.

[38] 崔丽珍,许凡非,王巧利,等.基于PSO-BP神经网络的煤矿井下自适应定位算法[J].工矿自动化,2018,44(2):74-79.

[39] Nguyen D T A,Lee H G,Jeong E R,et al.Deep learning-based localization for UWB systems[J].Electronics,2020,9(10):1712-1730.

[40] Meghani S K,Asif M,Awin F,et al.Empirical based ranging error mitigation in IR-UWB: A fuzzy approach[J].IEEE Access,2019,7(7):33686-33697.

[41] 陈浩,李起伟,王子龙.基于改进TDOA在煤矿井下超宽带定位算法的研究[J].电子测量技术,2021,44(6):96-102.

[42] 叶晓桐,张裕,宋俊典.基于注意力机制的UWB室内定位算法[J].计算机应用与软件,2021,38(6):198-201.

[43] 姚军,甄梓越,马宇静.基于BP神经网络的RSSI测距优化算法[J/OL].电波科学学报:1-6[2022-05-03].

[44] Usha M, Vanitha C N. Pruning Route Modifiers in Wireless Sensor Networks[J]. Wireless Personal Communications, 2016, 89(1): 27-43.

[45] 徐义晗.基于无迹卡尔曼滤波的移动目标跟踪算法[J].火力与指挥控制,2020,45(12):149-152.

[46] Fang X, Chen L. Noise-aware manoeuvring target tracking algorithm in wireless sensor networks by a novel adaptive cubature Kalman filter[J]. IET Radar, Sonar & Navigation, 2020, 14(11): 1795-1802.

[47] Wang X, Xie W, Li L. Interacting TS fuzzy particle filter algorithm for transfer probability matrix of adaptive online estimation model[J]. Digital Signal Processing, 2021, 110(110): 102944-102982.

[48] Zhang X, Liu D, Jiang H, et al. Particle filter with unknown statistics to estimate liquid level in the silicon single-crystal growth[J]. IEEE Transactions on Instrumentation and Measurement, 2019, 69(6): 2759-2770.

[49] Liu Z, Shang J, Hua X. Smart City Moving Target Tracking Algorithm Based on Quantum Genetic and Particle Filter[J]. Wireless Communications and Mobile Computing, 2020(2020): 1-9.

[50] Martino L, Elvira V. Compressed Monte Carlo with application in particle filtering[J]. Information Sciences, 2021, 553: 331-352.

[51] 徐壮,彭力.基于KLD采样的自适应粒子滤波目标跟踪算法[J].计算机工程,2019,45(12):182-188.

[52] Elvira V, Martino L. Advances in importance sampling[J]. arXiv preprint arXiv:2102.05407, 2021-2041.

[53] Yang J, Cui X, Li J, et al. Particle filter algorithm optimized by genetic algorithm combined with particle swarm optimization[J]. Procedia Computer Science, 2021, 187: 206-211.

[54] 朱震曙,蒋长辉,薄煜明,吴盘龙.磷虾群优化的改进粒子滤波算法[J].哈尔滨工业大学学报,2020,52(2):186-192.

[55] 孔红山,李小鹏,郁滨.SIR粒子滤波的改进算法[J].计算机工程与设计,2020,41(7):1899-1904.

[56] 闾斯瑶,周武能,李龙龙.基于SAGBA优化粒子滤波的目标跟踪[J].计算机技术与发展,2020,30(3):36-39.

[57] 阮艺琳. 基于粒子滤波的室内目标跟踪算法研究[D].山西大学,2020.

[58] 武风波,刘瑶,朱代先,王明博.鲸群优化的粒子滤波算法研究[J].应用光学,2021,42(05):859-866.

[59] 李维刚,李阳,赵云涛,严保康.基于改进灰狼算法的粒子滤波算法研究[J].系统仿真学报,2021,33(01):37-45.

[60] Mazhar F, Khan M G, Sällberg B. Precise indoor positioning using UWB: A review of methods, algorithms and implementations[J]. Wireless Personal Communications, 2017, 97(3): 4467-4491.

[61] Sharma A, Garg A, Sharma S K, et al. Performance optimization for UWB communication network under IEEE 802.15. 4a channel conditions[J]. Computer Networks, 2021, 201: 108585-108597.

[62] Liu F, Li F, Jing X. INS/gravity gradient aided navigation based on gravitation field particle filter[J]. Open Physics, 2019, 17(1): 709-718.

[63] 罗开田,刘刚.基于交通引力场的复杂网络路由选择方法[J].计算机应用研究,2017,34(1):194-196+210.

[64] 陈世明,刘俊恺,肖娟.基于引力场优化的Unscented FastSLAM2.0算法[J].控制理论与应,2018,35(8):1186-1193.

[65] 武文轩.引力场优化算法的并行及优化研究[D].吉林大学,2020.

[66] 陈世明,肖娟,李海英,聂森.基于引力场的粒子滤波算法[J].控制与决策,2017,32(4):709-714.

[67] Zhou Mengxia et al. Study on conducted EMI noise source modelling applied in electromagnetic compatibility analysis based on GA in cooperation with LM[J]. IET Power Electronics, 2020, 13(5) : 927-935.

[68] 王科平,朱朋飞,费树岷等. 基于自适应平滑尺度粒子滤波的目标快速跟踪[J].信息与控制,2020,49(5):536-545.

[69] Xue J, Shen B. A novel swarm intelligence optimization approach: sparrow search algorithm[J]. Systems Science & Control Engineering, 2020, 8(1): 22-34.

[70] 吕鑫,慕晓冬,张钧,王震.混沌麻雀搜索优化算法[J].北京航空航天大学学报,2021,47(8):1712-1720.

[71] 李雅丽,王淑琴,陈倩茹,王小钢.若干新型群智能优化算法的对比研究[J].计算机工程与应用,2020,56(22):1-12.

[72] 王婷,毋涛.基于T-SSA算法的流水车间订单调度问题研究[J].计算机技术与发展,2021,31(9):182-188.

[73] 史春天,曾艳阳,侯守明.群体智能算法在图像分割中的应用综述[J].计算机工程与应用,2021,57(8):36-47.

[74] 王维高,魏云冰,滕旭东,黄圆.基于麻雀搜索优化支持向量机的短期风机发电功率预测[J].智能计算机与应用,2022,12(1):119-123.