Kan Rui-xiang
ABSTRACT
The demand for indoor positioning is exploding. For indoor positioning, TPSN ranging model with specific frequency band is often used based on android platform or others. Time of arrival (TOA) is often used as an important part to achieve efficient localization. The signal wav files classification of line of sight (LOS) and non-line of sight (NLOS) will be involved. Support Vector Machine (SVM) is often used to complete classification in most positioning systems and the effect is not so good. At this moment, in order to improve the performance of this system, the Shrinkage Enhanced Particle Swarm Optimization (SEPSO) is introduced to improve the effect. Compared with traditional PSO, it introduces contraction coefficient to cope with the problem of few samples and non-linear system better than before. It also combines the non-linear decreasing inertia weight of asynchronous linear learning method with original algorithm to improve the ability of jumping out of local minimum area. Considering the actual use of those platforms, it improves the feature extraction algorithm and uses SEPSO to upgrade the performance of SVM from three aspects: training sample re-selection, parameter optimization and optimal preservation strategy (elitist strategy) of each generation population.
- Lei Zhang, DanJie Huang, XinHeng Wang, Christian Schindelhauer and Zhi Wang. 2017. Acoustic NLOS Identification Using Acoustic Channel Characteristics for Smartphone Indoor Localization. Sensors.17,727(2017). DOI=https://sci-hub.shop/10.3390/s17040727Google Scholar
- Lin Feng, Zhang Lei, Li GuiNan and Wang Zhi. 2017. Acoustic Self-Calibrating Indoor Localization System via Smartphones. Journal of Computer Research and Development. 54, 12 (2017), 2741--2751. DOI= https://crad.ict.ac.cn/CN/10.7544/issn1000--1239.2017.20160727Google Scholar
- Wen XiangJi. 2016. Research on Indoor Positioning System with Acoustic Signal based on Smartphones. Master Thesis. Zhejiang University.Google Scholar
- Kaikai Liu, Xinxin Liu and Xiaolin Li. 2016. Guoguo: Enabling Fine-grained Smartphone Localization via Acoustic Anchors. IEEE Trans. Mob. Comput.2016,15,1144--1156. DOI= https://sci-hub.shop/10.1109/tmc.2015.2451628Google ScholarDigital Library
- Fang WenHao. 2016. Node Self-localization and Passive Acoustic Source Target Positioning Based on Microphones of Smartphones. Master Thesis. Zhejiang University.Google Scholar
- Huang DanJie. 2017. Research on Acoustic-based Indoor Localization under NLOS Environment for Mobile Devices. Master Thesis. Zhejiang University.Google Scholar
- Lin Feng. 2018. Research and Implementation on Acoustic Self-Calibrating Indoor Localization System via Smartphones. Master Thesis. Zhejiang University.Google Scholar
- Fang WenHao, Xing ZhanYuan, Wen XiangJi, et al.2016. Passive acoustic source target positioning method based on smart phone platform TDOA estimation and system implementation. Chinese Journal of Scientific Instrument. 37, 4(2016), 952--960Google Scholar
- Wang XiaoChuan, Shi Feng, Yu Lei and Li Yang. 2011. MATLAB neural network case analysis of 43. Beihang University Press, ACM, Beijing, 119--121.Google Scholar
- Liu Mingzhu, Li Xiaoqina and Chen Hongheng.2019. Research on Speech and Emotional Recognition Algorithm Based on Support Vector Machine. Journal of Harbin University of Science and Technology.24,4(2019). DOI= http://hlgxb.hrbust.edu.cn/oa/darticle.aspx?type=view&id=20190420Google Scholar
- Ratnaweera A and Halgamuge SK, Watson HC. 2004. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Transactions on Evolutionary Computation.2004,8(3), 240--255. DOI= https://sci-hub.shop/10.1109/TEVC.2004.826071Google Scholar
- Chatterjee A and Siarry P. 2006. Nonlinear Inertai weight variation for dynamic adaptation in particle swarm optimization. Computers & Operations Research. 2006, 33(3):859--87. DOI=https://sci-hub.shop/10.1007/978--3-642--21515-5_10Google Scholar
- Shi YH and Eberhart R. 1998. A modified particle swarm optimizer. IEEE World Congress on Computational Intelligence. Anchorage, AK, USA.1998, 69--73. DOI=https://sci-hub.shop/10.1109/ICEC.1998.699146Google Scholar
Index Terms
- LOS and NLOS Signal Classification based on Advanced Particle Swarm Optimization for Acoustic Self-Calibrating Indoor Localization
Recommendations
Efficient WiFi-based indoor localization using particle swarm optimization
ICSI'11: Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part ILocation based services are rapidly gaining popularity in various mobile applications. Such services rely particularly on the capability to accurately determine the location of the user. Several techniques are already available to provide localization ...
Acoustic Signal NLOS Identification Method Based on Swarm Intelligence Optimization SVM for Indoor Acoustic Localization
The demand for an indoor localization system is increasing, and related research is also becoming more universal. Previous works on indoor localization systems mainly focus on the acoustic signals in Line of Sight (LOS) scenario to obtain accurate ...
A Polygonal Method for Ranging-Based Localization in an Indoor Wireless Sensor Network
In this paper, we propose an indoor localization method in a wireless sensor network based on IEEE 802.15.4 specification. The proposed method follows a ranging-based approach using not only the measurements of received signal strength (RSS) but also ...
Comments