仿蝎子振源定位机理的位置指纹室内定位方法

doi:10.13229/j.cnki.jdxbgxb20180996

摘要/Abstract

摘要：

针对基于接受信号强度（RSS）的位置指纹室内定位方法易受到多径效应及噪声干扰而导致定位精度低这一问题，提出了一种仿蝎子振源定位机理的位置指纹室内定位方法。该方法首先仿蝎子的 $n / 1$ 神经元构型构建神经元结构对振动信号进行编码，将振动信号转化为脉冲。然后，提取脉冲作为位置指纹特征，并利用该脉冲建立位置指纹特征库。最后，用加权K近邻（WKNN）算法进行振源位置估计。为验证本文算法的有效性，模仿蝎子生理结构，搭建了一套仿蝎子振动感知的振动信号采集系统，在室内环境中进行用户踏步信号的采集，并根据振动数据进行定位试验。结果表明：本文提出的仿蝎子 $n / 1$ 神经元构型的位置指纹定位方法比基于RSS的位置指纹定位方法的平均定位准确度提高了0.148 4 m。

关键词: 信息处理技术, 室内定位, 蝎子, 位置指纹, 加权K近邻

Abstract:

The fingerprint indoor localization method based on the Received Signal Strength (RSS) is vulnerable to multipath effect and noise interference, resulting in low positioning accuracy. To solve this problem, an indoor positioning method based on location fingerprinting of imitating the mechanism of scorpion vibration source location is proposed. Firstly, the method imitates the n/1 neuron configuration of scorpion to construct the neuron structure, in order to encode the vibration signal and transform the vibration signal into pulses. Secondly, pulses are extracted as the location fingerprint feature, and then the location fingerprint feature database is established by the number of pulses. Finally, the Weighted K-Nearest Neighbours algorithm is used to estimate the position of vibration source. To verify the performance of the proposed algorithm, a vibration signal acquisition system is set up to imitate the vibration perception of scorpions. It is used to collect the user's step signals in the indoor environment. The experimental results indicate that the proposed method can improve the average positioning accuracy by 0.148 4 meters compared with the location fingerprinting based on RSS.

Key words: information processing technology, indoor positioning, scorpions, location fingerprinting, weighted K-nearest neighbors

中图分类号:

TN911.73

刘富, 权美静, 王柯, 刘云, 康冰, 韩志武, 侯涛. 仿蝎子振源定位机理的位置指纹室内定位方法[J]. 吉林大学学报(工学版), 2019, 49(6): 2076-2082.

Fu LIU, Mei-jing QUAN, Ke WANG, Yun LIU, Bing KANG, Zhi-wu HAN, Tao HOU. Indoor positioning method based on location fingerprinting of imitating mechanism of scorpion vibration source[J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(6): 2076-2082.

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参考文献

1	He S N , Chan S H G . Wi-Fi fingerprint-based indoor positioning: recent advances and comparisons[J]. IEEE Communications Surveys & Tutorials. 2016, 18(1): 466-490.
2	Dwiyasa F , Lim M H , Ong Y S , et al . Extreme learning machine for indoor location fingerprinting[J]. Multidimensional Systems and Signal Processing, 2017, 28(3): 867-883.
3	Niculescu D , Nath B . Ad Hoc positioning system(APS) using AOA[C]∥IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies, San Francisco, CA, USA, 2003: 2926-2931.
4	Alavi B , Pahlavan K . Modeling of the TOA-based distance measurement error using UWB indoor radio measurements[J]. IEEE Communications Letters, 2006, 10(4): 275-277.
5	Ma W K , Vo B N , Singh S S , et al . Tracking an unknown time-varying number of speakers using TDOA measurements: a random finite set approach[J]. IEEE Transactions on Signal Processing, 2006, 54(9): 3291-3304.
6	Tian Xiao-hua , Shen Ruo-fei , Liu Duo-wen , et al . Performance analysis of RSS fingerprinting based indoor localization[J]. IEEE Transactions on Mobile Computing, 2017, 16(10): 2847-2861.
7	Bahl P , Padmanabhan V N . RADAR: an in-building RF-based user location and tracking system[J/OL].[2018-09-20]. https:∥.
8	Youssef M , Agrawala A . The horus WLAN location determination system[C]∥3rd International Conference on Mobile Systems, Applications, and Services, Seattle, Washington, 2005: 205-218.
9	Matic A , Papliatseyeu A , Osmani V , et al . Tuning to your position: FM radio based indoor localization with spontaneous recalibration[C]∥2010 IEEE International Conference on Pervasive Computing and Communications, Mannheim, Germany, 2010: 153-161.
10	Ishida S , Izumi K , Tagashira S , et al . WiFi AP-RSS monitoring using sensor nodes toward anchor-free sensor localization[C]∥2015 IEEE 82nd Vehicular Technology Conference, Boston, MA, USA, 2015: 1-5.
11	Chen Qiu-xia , Ding Dong-dong , Zheng Yue . Indoor pedestrian tracking with sparse RSS fingerprints[J]. Tsinghua Science and Technology, 2018, 23(1): 95-103.
12	Chen F , Au W S A , Tan Z H , et al . Received-sigal-strength-based indoor positioning using compressive sensing[J]. IEEE Transactions on Mobile Computing, 2012, 11(12): 1983-1993.
13	Brownell P , Farley R D ．Detection of vibrations in sand by tarsal sense organs of the nocturnal scorpion, Paruroctonus Mesaensis [J]. Journal of Comparative Physiology A, 1979, 131(1): 23-30.
14	Brownell P H , van Hemmen J L . Vibration sensitivity and a computational theory for prey-localizing behavior in sand scorpions[J]. Integrative and Comparative Biology, 2001, 41(5): 1229-1240.
15	王柯，刘富，康冰，等．基于沙蝎定位猎物的仿生震源定位方法[J]．吉林大学学报:工学版, 2018, 48(2): 633-639．
15	Wang Ke , Liu Fu , Kang Bing , et al . Bionic hypocenter localization method inspired by sand scorpion in locating preys[J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(2): 633-639.
16	Stürzl W , Kempter R , van Hemmen J L . Theory of arachnid prey localization[J]. The American Physical Society, 2000, 84(24): 5668-5671.
17	Kim D E . Neural network mechanism for the orientation behavior of sand scorpions towards prey[J]. IEEE Transactions on Neural Networks, 2006, 17(4): 1070-1076.
18	Seydnejad S R . Reconstruction of the input signal of the leaky integrate-and-fire neuronal model from its interspike intervals[J]. Biological Cybernetics, 2016, 110(1): 3-15.

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