Journal of Jilin University(Engineering and Technology Edition) ›› 2019, Vol. 49 ›› Issue (6): 2076-2082.doi: 10.13229/j.cnki.jdxbgxb20180996

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Indoor positioning method based on location fingerprinting of imitating mechanism of scorpion vibration source

Fu LIU1,2(),Mei-jing QUAN2,Ke WANG2,Yun LIU2,Bing KANG2,Zhi-wu HAN3,Tao HOU2,3()   

  1. 1. State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China
    2. College of Communication Engineering, Jilin University, Changchun 130022, China
    3. Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China
  • Received:2018-09-28 Online:2019-11-01 Published:2019-11-08
  • Contact: Tao HOU E-mail:liufu@jlu.edu.cn;ht_happy@jlu.edu.cn

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

CLC Number: 

  • TN911.73

Fig.1

Schematic diagram of location fingerprinting"

Fig.2

Distribution of sand scorpion′s BCSS"

Fig.3

n/1 configuration of scorpion neurons"

Fig.4

Connection block diagram of indoor fingerprint localization based on acceleromete"

Fig.5

Schematic diagram of reference points"

Fig.6

Photo of experiment site"

Fig.7

Arrangement of sensors"

Fig.8

Signal sample of vibration source"

Fig.9

Signal discharge spiking"

Table 1

Mean positioning error"

平均定位误差/m
KNN WKNN

1/1构型

2/1构型

3/1构型

5/1构型

7/1构型

0.5384

0.5096

0.3952

0.4384

0.4721

0.4839

0.4605

0.3517

0.3925

0.4182

RSS 0.5569 0.5001

Fig.10

Cumulative distribution function oferror distance"

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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