基于量子粒子群优化容积卡尔曼滤波的LANDMARC室内定位算法
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摘要
针对噪声环境下,基于标准容积卡尔曼滤波的LANDMARC室内定位算法因噪声特性估计不准,引起滤波性能下降而导致定位误差较大的问题,提出一种基于量子粒子群优化容积卡尔曼滤波的LANDMARC室内定位算法。该算法首先建立基于LANDMARC定位框架下的运动目标动态模型,然后引入量子粒子群优化技术对容积卡尔曼滤波中时间更新过程的状态预测值进行优化,以降低因畸变噪声引起的误差;最后将改进的容积卡尔曼滤波算法应用到运动目标状态估计中。实验结果表明,所提算法定位误差均值为0.175 m,与相同环境下传统的LANDMARC算法、基于容积卡尔曼滤波的LANDMARC算法以及基于粒子群优化容积卡尔曼滤波的LANDMARC算法相比,定位精度和稳定性均有明显提高,且运算时间比基于粒子群优化的算法少,应用在室内定位中能够得到较为真实的目标移动轨迹。
When the LANDMARC indoor localization algorithm based on standard cubature Kalman filter is applied in the noisy environment,it has the problem of the large location error due to the decrease of the filtering performance caused by the inaccurate estimation of the noise characteristics. Aiming to solve this problem,a LANDMARC indoor location algorithm based on quantum particle swarm optimization cubature Kalman filter is proposed in this paper. Firstly,a dynamic model of the moving target based on the LANDMARC localization framework is established. Then the quantum particle swarm optimization technique is introduced to optimize the state prediction during the time updating process in the cubature Kalman filter for reducing the error caused by distortion noise. Finally,the optimized cubature Kalman filter algorithm is applied to the state estimation of moving target. The experimental results show that the mean error of the proposed algorithm is 0. 175 m. Compared with the traditional LANDMARC algorithm,the LANDMARC algorithm based on the cubature Kalman filter and the LANDMARC algorithm based on the particle swarm optimization cubature Kalman filter,the localization accuracy and stability are significantly improved,and the operation time is shorter under the same environment. Therefore,the proposed algorithm can obtain a more realistic trajectory of moving target when it is applied to indoor localization.
When the LANDMARC indoor localization algorithm based on standard cubature Kalman filter is applied in the noisy environment,it has the problem of the large location error due to the decrease of the filtering performance caused by the inaccurate estimation of the noise characteristics. Aiming to solve this problem,a LANDMARC indoor location algorithm based on quantum particle swarm optimization cubature Kalman filter is proposed in this paper. Firstly,a dynamic model of the moving target based on the LANDMARC localization framework is established. Then the quantum particle swarm optimization technique is introduced to optimize the state prediction during the time updating process in the cubature Kalman filter for reducing the error caused by distortion noise. Finally,the optimized cubature Kalman filter algorithm is applied to the state estimation of moving target. The experimental results show that the mean error of the proposed algorithm is 0. 175 m. Compared with the traditional LANDMARC algorithm,the LANDMARC algorithm based on the cubature Kalman filter and the LANDMARC algorithm based on the particle swarm optimization cubature Kalman filter,the localization accuracy and stability are significantly improved,and the operation time is shorter under the same environment. Therefore,the proposed algorithm can obtain a more realistic trajectory of moving target when it is applied to indoor localization.
引文
[1]许莹,刘社函.一种改进的基于双标签的LANDMARC定位算法[J].计算机应用研究,2015,32(12):3769-3772.XU Y,LIU SH H.Improved LANDMARC localization algorithm based on double-tag[J].Application Research of Computers,2015,32(12):3769-3772.
[2]曹洁,牛丽波,王进花.一种改进LANDMARC射频识别室内定位算法[J].计算机工程与科学,2015,37(9):1671-1675.CAO J,NIU L B,WANG J H.An improved LANDMARC RFID indoor location algorithm[J].Computer Engineering&Science,2015,37(9):1671-1675.
[3]钟晴,叶芝慧,郭小青.基于RFID室内可视化定位系统设计与实现[J].电子测量技术,2016,39(8):186-190.ZHONG Q,YE ZH H,GUO X Q.Design and implementation of RFID-based indoor visual positioning system[J].Electronic Measurement Technology,2016,39(8):186-190.
[4]KHAN M A,ANTIWAL V K.Location estimation technique using extended 3-D LANDMARC algorithm for passive RFID tag[C].IEEE International Advance Computing Conference,2009:249-253.
[5]马翠红,丁建南,杨友良,等.改进的LANDMARC在空间定位中的应用[J].自动化仪表,2015,36(9):22-25.MA C H,DING J N,LIANG Y L,et al.Application of the improved LANDMARC in spatial position[J].Process Automation Instrumentation,2015,36(9):22-25.
[6]王小辉,田磊.基于RFID技术的三维定位算法研究[J].电子设计工程,2017,25(6):136-139.WANG X H,TIAN L.Design of the three-dimensional positioning algorithm based on RFID[J].Electronic Design Engineering,2017,25(6):136-139.
[7]李宝山,岳康.LANDMARC定位算法的修正与优化[J].计算机应用与软件,2016,33(4):99-102.LI B SH,YUE K.Amendment and Optimization of LANDMARC localization algorithm[J].Computer Applications and Software,2016,33(4):99-102.
[8]王宏健,李村,么洪飞,等.基于高斯混合容积卡尔曼滤波的UUV自主导航定位算法[J].仪器仪表学报,2015,36(2):254-261.WANG H J,LI C,YAO H F,et al.Gaussian mixture cubature Kalman filter based autonomous navigation and localization algorithm for UUV[J].Chinese Journal of Scientific Instrument,2015,36(2):254-261.
[9]魏喜庆,宋申民.无模型容积卡尔曼滤波及其应用[J].控制与决策,2013(5):769-773.WEI X Q,SONG SH M.Model-free cubature Kalman filter and its application[J].Control and Decision,2013(5):769-773.
[10]蒋鹏,宋华华,王兴民.基于动态生成树和改进不敏卡尔曼滤波的传感器网络目标跟踪算法研究[J].仪器仪表学报,2015,36(2):415-421.JIANG P,SONG H H,WANG X M.Target tracking algorithm for sensor networks based on dynamic spanning tree and improved unscented Kalman filter[J].Chinese Journal of Scientific Instrument,2015,36(2):415-421.
[11]陈小玲,茅旭初.基于粒子群算法和平方根平淡卡尔曼滤波的北斗导航系统定位估计算法[J].上海交通大学学报,2017,51(5):592-597.CHEN X L,MAO X CH.Positioning algorithm of Bei Dou navigation satellite system based on particle swarm optimization and square-root unscented Kalman filter[J].Journal of Shang Hai Jiao Tong University,2017,51(5):592-597.
[12]阮承治,赵德安,刘晓洋,等.基于IPSO-UKF的水草清理作业船组合导航定位方法[J].农业机械学报,2017,48(7):38-45.RUAN CH ZH,ZHAO D AN,LIU X Y,et al.Integrated navigation positioning method based on IPSO-UKF for aquatic plants cleaning workboat[J].Transactions of the Chinese Society of Agricultural Machinery,2017,48(7):38-45.
[13]尹忠刚,肖鹭,孙向东,等.基于粒子群优化的感应电机模糊扩展卡尔曼滤波器转速估计方法[J].电工技术学报,2016,31(6):55-65.YIN ZH G,XIAO L,SUN X D,et al.A speed estimation method of fuzzy extended Kalman filter for induction motors based on particle swarm optimization[J].Transactions of China Electrotechnical Society,2016,31(6):55-65.
[14]范启蒙,尹成友,廖飞龙.基于量子粒子群优化的短波相控阵天线的激励优化研究[J].电子与信息学报,2017(7):1769-1773.FAN Q M,YIN CH Y,LIAO F L.Analysis of excitation optimization of short wave phased array based on quantum-behaved particle swarm optimization[J].Journal of Electronics&Information Technology,2017,39(7):1769-1773.
[15]李一博,张博林,刘自鑫,等.基于量子粒子群算法的自适应随机共振方法研究[J].物理学报,2014,63(16):36-43.LI Y B,ZHANG B L,LIU Z X.Adaptive stochastic resonance method based on quantum particle swarm optimization[J].Chinese Journal of Physics,2014,63(16):36-43.
[16]史伟光,韩晓迪,李建雄,等.一种适于无源LANDMARC算法的定位性能评价方法[J].哈尔滨工业大学学报,2016,48(5):127-133.SHI W G,HAN X D,LI J X,et al.A performance evaluation mechanism suitable for passive LANDMARC algorithm[J].Journal of Harbin Institute of Technology,2016,48(5):127-133.
[17]ARASARATNAM I,HAYKIN S,THOMAS R.H.Cubature Kalman filtering for continuous-discrete systems:Theory and simulations[J].IEEE Transactions on Signal Processing,2010,58(10):4977-4993.
[18]伍永健,陈跃东,陈孟元.改进QPSO和Morphin算法下移动机器人混合路径规划[J].电子测量与仪器学报,2017,31(2):295-301.WU Y J,CHEN Y D,CHEN M Y.Hybrid path planning of mobile robot based on improved QPSO and Morphin algorithm[J].Journal of Electronic Measurement and Instrumentation,2017,31(2):295-301.
[19]李丽娜,马俊,龙跃,等.基于LANDMARC与压缩感知的双段式室内定位算法[J].电子与信息学报,2016,38(7):1631-1637.LI L N,MA J,LONG Y,et al.Double stage indoor localization algorithm based on LANDMARC and compressive sensing[J].Journal of Electronics&Information Technology,2016,38(7):1631-1637.
[2]曹洁,牛丽波,王进花.一种改进LANDMARC射频识别室内定位算法[J].计算机工程与科学,2015,37(9):1671-1675.CAO J,NIU L B,WANG J H.An improved LANDMARC RFID indoor location algorithm[J].Computer Engineering&Science,2015,37(9):1671-1675.
[3]钟晴,叶芝慧,郭小青.基于RFID室内可视化定位系统设计与实现[J].电子测量技术,2016,39(8):186-190.ZHONG Q,YE ZH H,GUO X Q.Design and implementation of RFID-based indoor visual positioning system[J].Electronic Measurement Technology,2016,39(8):186-190.
[4]KHAN M A,ANTIWAL V K.Location estimation technique using extended 3-D LANDMARC algorithm for passive RFID tag[C].IEEE International Advance Computing Conference,2009:249-253.
[5]马翠红,丁建南,杨友良,等.改进的LANDMARC在空间定位中的应用[J].自动化仪表,2015,36(9):22-25.MA C H,DING J N,LIANG Y L,et al.Application of the improved LANDMARC in spatial position[J].Process Automation Instrumentation,2015,36(9):22-25.
[6]王小辉,田磊.基于RFID技术的三维定位算法研究[J].电子设计工程,2017,25(6):136-139.WANG X H,TIAN L.Design of the three-dimensional positioning algorithm based on RFID[J].Electronic Design Engineering,2017,25(6):136-139.
[7]李宝山,岳康.LANDMARC定位算法的修正与优化[J].计算机应用与软件,2016,33(4):99-102.LI B SH,YUE K.Amendment and Optimization of LANDMARC localization algorithm[J].Computer Applications and Software,2016,33(4):99-102.
[8]王宏健,李村,么洪飞,等.基于高斯混合容积卡尔曼滤波的UUV自主导航定位算法[J].仪器仪表学报,2015,36(2):254-261.WANG H J,LI C,YAO H F,et al.Gaussian mixture cubature Kalman filter based autonomous navigation and localization algorithm for UUV[J].Chinese Journal of Scientific Instrument,2015,36(2):254-261.
[9]魏喜庆,宋申民.无模型容积卡尔曼滤波及其应用[J].控制与决策,2013(5):769-773.WEI X Q,SONG SH M.Model-free cubature Kalman filter and its application[J].Control and Decision,2013(5):769-773.
[10]蒋鹏,宋华华,王兴民.基于动态生成树和改进不敏卡尔曼滤波的传感器网络目标跟踪算法研究[J].仪器仪表学报,2015,36(2):415-421.JIANG P,SONG H H,WANG X M.Target tracking algorithm for sensor networks based on dynamic spanning tree and improved unscented Kalman filter[J].Chinese Journal of Scientific Instrument,2015,36(2):415-421.
[11]陈小玲,茅旭初.基于粒子群算法和平方根平淡卡尔曼滤波的北斗导航系统定位估计算法[J].上海交通大学学报,2017,51(5):592-597.CHEN X L,MAO X CH.Positioning algorithm of Bei Dou navigation satellite system based on particle swarm optimization and square-root unscented Kalman filter[J].Journal of Shang Hai Jiao Tong University,2017,51(5):592-597.
[12]阮承治,赵德安,刘晓洋,等.基于IPSO-UKF的水草清理作业船组合导航定位方法[J].农业机械学报,2017,48(7):38-45.RUAN CH ZH,ZHAO D AN,LIU X Y,et al.Integrated navigation positioning method based on IPSO-UKF for aquatic plants cleaning workboat[J].Transactions of the Chinese Society of Agricultural Machinery,2017,48(7):38-45.
[13]尹忠刚,肖鹭,孙向东,等.基于粒子群优化的感应电机模糊扩展卡尔曼滤波器转速估计方法[J].电工技术学报,2016,31(6):55-65.YIN ZH G,XIAO L,SUN X D,et al.A speed estimation method of fuzzy extended Kalman filter for induction motors based on particle swarm optimization[J].Transactions of China Electrotechnical Society,2016,31(6):55-65.
[14]范启蒙,尹成友,廖飞龙.基于量子粒子群优化的短波相控阵天线的激励优化研究[J].电子与信息学报,2017(7):1769-1773.FAN Q M,YIN CH Y,LIAO F L.Analysis of excitation optimization of short wave phased array based on quantum-behaved particle swarm optimization[J].Journal of Electronics&Information Technology,2017,39(7):1769-1773.
[15]李一博,张博林,刘自鑫,等.基于量子粒子群算法的自适应随机共振方法研究[J].物理学报,2014,63(16):36-43.LI Y B,ZHANG B L,LIU Z X.Adaptive stochastic resonance method based on quantum particle swarm optimization[J].Chinese Journal of Physics,2014,63(16):36-43.
[16]史伟光,韩晓迪,李建雄,等.一种适于无源LANDMARC算法的定位性能评价方法[J].哈尔滨工业大学学报,2016,48(5):127-133.SHI W G,HAN X D,LI J X,et al.A performance evaluation mechanism suitable for passive LANDMARC algorithm[J].Journal of Harbin Institute of Technology,2016,48(5):127-133.
[17]ARASARATNAM I,HAYKIN S,THOMAS R.H.Cubature Kalman filtering for continuous-discrete systems:Theory and simulations[J].IEEE Transactions on Signal Processing,2010,58(10):4977-4993.
[18]伍永健,陈跃东,陈孟元.改进QPSO和Morphin算法下移动机器人混合路径规划[J].电子测量与仪器学报,2017,31(2):295-301.WU Y J,CHEN Y D,CHEN M Y.Hybrid path planning of mobile robot based on improved QPSO and Morphin algorithm[J].Journal of Electronic Measurement and Instrumentation,2017,31(2):295-301.
[19]李丽娜,马俊,龙跃,等.基于LANDMARC与压缩感知的双段式室内定位算法[J].电子与信息学报,2016,38(7):1631-1637.LI L N,MA J,LONG Y,et al.Double stage indoor localization algorithm based on LANDMARC and compressive sensing[J].Journal of Electronics&Information Technology,2016,38(7):1631-1637.