A Near-Ultrasonic Robust Indoor Localization Method Based on Stacking Ensemble Learning

Jia Naizheng; Xue Can; Yang Liu; Wang Zhi

doi:10.7544/issn1000-1239.202330882

Journal of Computer Research and Development > 2025 > 62(2): 488-502. > DOI: 10.7544/issn1000-1239.202330882 CSTR: 32373.14.issn1000-1239.202330882

Jia Naizheng, Xue Can, Yang Liu, Wang Zhi. A Near-Ultrasonic Robust Indoor Localization Method Based on Stacking Ensemble Learning[J]. Journal of Computer Research and Development, 2025, 62(2): 488-502. DOI: 10.7544/issn1000-1239.202330882

Citation:

PDF (2465 KB)

A Near-Ultrasonic Robust Indoor Localization Method Based on Stacking Ensemble Learning

College of Control Science and Engineering, Zhejiang University, Hangzhou 310013

Funds: This work was supported by the National Key Research and Development Program of China (2021YFB3900800).

More Information

Author Bio:
Jia Naizheng: born in 1998. PhD candidate. His main research interests include signal processing and acoustic signal localization

Xue Can: born in 1997. PhD candidate. His main research interests include embedded system and acoustic signal localization

Yang Liu: born in 2000. PhD candidate. His main research interests include acoustic signal processing and robot localization

Wang Zhi: born in 1967. PhD, associate professor. Member of CCF. His main research interests include signal processing and acoustic signal sensing, and robot navigation
Received Date: October 30, 2023
Revised Date: April 25, 2024
Accepted Date: May 23, 2024
Available Online: June 30, 2024

Graphical Abstract

Abstract

Abstract

Recent economic advancements have significantly supported the popularity of indoor positioning systems (IPS) and indoor localization-based services (ILBS). This trend is particularly obvious as global navigation satellite systems (GNSS) are ineffective in indoor environments. Traditional IPS, such as WIFI and Bluetooth positioning, face challenges like low accuracy and are prone to non-line-of-sight (NLOS) and noise interference. In response to this issue, we propose a novel near-ultrasonic robust indoor localization method based on the stacking ensemble model. Initially, the method employs an optimized enhanced cross-correlation technique to effectively mitigate multipath interference in acoustic ranging. Compared with the conventional methods based on peak extraction or fixed thresholding, this approach significantly improves ranging accuracy in reverberant environments. Subsequently, time difference of arrival (TDOA) is extracted as a feature. Finally, we utilize a stacking ensemble learning model, incorporating optimized machine learning models, to train a pre-set dataset. This method, integrating the extracted feature, enables to achieve correct localization results in NLOS and large ranging error. Numerical simulations, ray-tracing acoustic analyses, and empirical validations suggest that our approach notably mitigates errors prevalent in NLOS and acoustically noisy indoor environments and yielding localization accuracy significantly exceeds current methods by 50%−90%. The core dataset available at https://github.com/ChirsJia/JSJYF.
- indoor positioning,
- near-ultrasonic localization,
- signal processing,
- machine learning,
- TDOA

FullText(HTML)

References (53)

References

[1]	Obeidat H, Shuaieb W, Obeidat O, et al. A review of indoor localization techniques and wireless technologies[J]. Wireless Personal Communications, 2021, 119: 289−327 doi: 10.1007/s11277-021-08209-5
[2]	Zhang Lan, Liu Kebin, Jiang Yonghang, et al. Montage: Combine frames with movement continuity for realtime multi-user tracking[J]. IEEE Transactions on Mobile Computing, 2017, 16(4): 1019−1031 doi: 10.1109/TMC.2016.2577586
[3]	薛灿,韩强,王智. 基于信号统计模型的变电站半遮挡融合定位方法[J]. 电力工程技术,2023,42(1):185−192 doi: 10.12158/j.2096-3203.2023.01.022 Xue Can, Han Qiang, Wang Zhi. Semi-occlusion substation fusion positioning method based on multi-sensor signal statistical model[J]. Electric Power Engineering Technology, 2023, 42(1): 185−192 (in Chinese) doi: 10.12158/j.2096-3203.2023.01.022
[4]	Cheng Sisi, Wang Feng, Li Guangcai, et al. Single-frequency multi-GNSS PPP-RTK for smartphone rapid centimeter-level positioning[J]. IEEE Sensors Journal, 2023, 23(18): 21553−21561 doi: 10.1109/JSEN.2023.3301658
[5]	Jia Naizheng, Shu Haoran, Wang Xinheng, et al. Smartphone-based social distance detection technology with near-ultrasonic signal[J]. Sensors, 2022, 22(19): 7345−7375 doi: 10.3390/s22197345
[6]	Zou Han, Zhou Yuxun, Yang Jianfei, et al. Unsupervised WIFI-enabled IOT device-user association for personalized location-based service[J]. IEEE Internet of Things Journal, 2019, 6(1): 1238−1245 doi: 10.1109/JIOT.2018.2868648
[7]	Shi Lina, Shi Dayu, Zhang Xun, et al. 5G Internet of radio light positioning system for indoor broadcasting service[J]. IEEE Transactions on Broadcasting, 2020, 66(2): 534−544 doi: 10.1109/TBC.2020.2981755
[8]	AlHory O, Shoushara O, Al Suri H, et al. 5G mmwave indoor location identification using beamforming and RSSI[C]//Proc of the 11th Int Conf on Information and Communication Systems (ICICS). Piscataway, NJ: IEEE, 2020: 91−95
[9]	Deng Zhongliang, Zheng Xinyu, Zhang Chongyu, et al. A TDOA and PDR fusion method for 5G indoor localization based on virtual base stations in unknown areas[J]. IEEE Access, 2020, 8: 225123−225133 doi: 10.1109/ACCESS.2020.3044812
[10]	Yu Yue, Chen Ruizhi, Chen Liang, et al. A novel 3-D indoor localization algorithm based on BLE and multiple sensors[J]. IEEE Internet of Things Journal, 2021, 8(11): 9359−9372 doi: 10.1109/JIOT.2021.3055794
[11]	You Yuan, Wu Chang. Hybrid indoor positioning system for pedestrians with swinging arms based on smartphone IMU and RSSI of BLE[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1−15
[12]	Rahman M, Haider A, Naghshvarianjahromi M. A systematic methodology for the time-domain ringing reduction in UWB band-notched antennas[J]. IEEE Antennas and Wireless Propagation Letters, 2020, 19(3): 482−486 doi: 10.1109/LAWP.2020.2972025
[13]	Bao Dongxuan, Zou Zhuo, Nejad M, et al. A wirelessly powered UWB RFID sensor tag with time-domain analog-to-information interface[J]. IEEE Journal of Solid-State Circuits, 2018, 53(8): 2227−2239 doi: 10.1109/JSSC.2018.2825455
[14]	Sharma S, Deka K, Mandloi M. Deep learning noncoherent UWB receiver design[J]. IEEE Sensors Letters, 2021, 5(6): 1−4
[15]	Liu Manni, Cheng Linsong, Qian Kun, et al. Indoor acoustic localization: A survey[J]. Human-centric Computing and Information Sciences, 2020, 10(1): 1−24 doi: 10.1186/s13673-019-0205-6
[16]	Albuquerque D, Vieira J, Lopes S, et al. Indoor acoustic simulator for ultrasonic broadband signals with doppler effect[J]. Applied Acoustics, 2015, 97: 140−151 doi: 10.1016/j.apacoust.2015.04.010
[17]	Gualda D, Perez-Rubio M, Urena J, et al. Locate-us: Indoor positioning for mobile devices using encoded ultrasonic signals, inertial sensors and graph-matching[J]. Sensors, 2021, 21(6): 1950−1973
[18]	Dubey A, Sood P, Santos J, et al. An enhanced approach to imaging the indoor environment using WIFI RSSI measurements[J]. IEEE Transactions on Vehicular Technology, 2021, 70(9): 8415−8430 doi: 10.1109/TVT.2021.3101009
[19]	Zafari F, Gkelias A, Leung K. A survey of indoor localization systems and technologies[J]. IEEE Communications Surveys & Tutorials, 2019, 21(3): 2568−2599
[20]	Zhang Ji, Singh S. Loam: Lidar odometry and mapping in real-time[J]. Robotics: Science and Systems, 2014, 2(9): 1−9
[21]	Cai Chao, Hu Menglan, Cao Doudou, et al. Self-deployable indoor localization with acoustic-enabled IoT devices exploiting participatory sensing[J]. IEEE Internet of Things Journal, 2019, 6(3): 5297−5311 doi: 10.1109/JIOT.2019.2900524
[22]	Huang Wenchao, Xiong Yan, Li Xiangyang, et al. Swadloon: Direction finding and indoor localization using acoustic signal by shaking smartphones[J]. IEEE Transactions on Mobile Computing, 2014, 14(10): 2145−2157
[23]	Yang Lingyu, Feng Xiaoke, Zhang Jing, et al. Multi-ray modeling of ultrasonic sensors and application for micro-UAV localization in indoor environments[J]. Sensors, 2019, 19(8): 1770−1787
[24]	Höflinger F, Zhang Rui, Hoppe J, et al. Acoustic self-calibrating system for indoor smartphone tracking (ASSIST)[C/OL]//Proc of the 1st Int Conf on Indoor Positioning and Indoor Navigation (IPIN). Piscataway, NJ: IEEE, 2012 [2024-04-22]. https://ieeexplore.ieee.org/document/6418877
[25]	Höflinger F, Bordoy J, Simon N, et al. Indoor-localization system for smart phones[C/OL]//Proc of the 3rd IEEE Int Workshop on Measurements & Networking (M&N). Piscataway, NJ: IEEE, 2015 [2024-04-22]. https://ieeexplore.ieee.org/document/7322974/
[26]	Prieto J, Jiménez A, Guevara J, et al. Performance evaluation of 3D-locus advanced acoustic LPS[J]. IEEE Transactions on Instrumentation and Measurement, 2009, 58(8): 2385−2395 doi: 10.1109/TIM.2009.2016378
[27]	Sertatıl C, Altınkaya M, Raoof K. A novel acoustic indoor localization system employing CDMA[J]. Digital Signal Processing, 2012, 22(3): 506−517 doi: 10.1016/j.dsp.2011.12.001
[28]	Liu Kaikai, Liu Xinxin, Li Xiaolin. Guoguo: Enabling fine-grained smartphone localization via acoustic anchors[J]. IEEE Transactions on Mobile Computing, 2016, 15(5): 1144−1156 doi: 10.1109/TMC.2015.2451628
[29]	Liu Zuoya, Chen Ruizhi, Ye Feng, et al. Improved TOA estimation method for acoustic ranging in a reverberant environment[J]. IEEE Sensors Journal, 2020, 22(6): 4844−4852
[30]	Cao Shuai, Chen Xiang, Zhang Xu, et al. Effective audio signal arrival time detection algorithm for realization of robust acoustic indoor positioning[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(10): 7341−7352 doi: 10.1109/TIM.2020.2981985
[31]	Peng Chunyi, Shen Guobin, Zhang Yongguang. Beepbeep: A high-accuracy acoustic-based system for ranging and localization using cots devices[J]. ACM Transactions on Embedded Computing Systems, 2012, 11(1): 1−29
[32]	Zhang Lei, Chen Minlin, Wang Xinheng, et al. TOA estimation of chirp signal in dense multipath environment for low-cost acoustic ranging[J]. IEEE Transactions on Instrumentation and Measurement, 2018, 68(2): 355−367
[33]	Mao Wenguang, He Jian, Qiu Lili. Cat: High-precision acoustic motion tracking[C]//Proc of the 22nd Annual Int Conf on Mobile Computing and Networking. New York: ACM, 2016: 69−81
[34]	Liu Yunhao, Wang Jiliang, Zhang Yunting, et al. Vernier: Accurate and fast acoustic motion tracking using mobile devices[J]. IEEE Transactions on Mobile Computing, 2019, 20(2): 754−764
[35]	Wang Wei, Liu A, Sun Ke. Device-free gesture tracking using acoustic signals[C]//Proc of the 22nd Annual Int Conf on Mobile Computing and Networking. New York: ACM, 2016: 82−94
[36]	Lin Xinchuang, Chen Ruizhi, Huang Lixiong, et al. Chirptracker: A precise-location-aware system for acoustic tag using single smartphone[J]. IEEE Internet of Things Journal, 2023, 11(1): 848−862
[37]	Wang Hucheng, Xue Can, Wang Zhi, et al. Smartphone-based pedestrian NLOS positioning based on acoustics and IMU parameter estimation[J]. IEEE Sensors Journal, 2022, 22(23): 23095−23108 doi: 10.1109/JSEN.2022.3185248
[38]	Wang Hucheng, Wang Zhi, Zhang Lei, et al. A highly stable fusion positioning system of smartphone under NLOS acoustic indoor environment[J]. ACM Transactions on Internet Technology, 2023, 23(2): 1−19
[39]	Cai Chao, Zheng Rong, Li Jun, et al. Asynchronous acoustic localization and tracking for mobile targets[J]. IEEE Internet of Things Journal, 2020, 7(2): 830−845 doi: 10.1109/JIOT.2019.2945054
[40]	Ji Cheng, Chen Minlin, Sun Peng, et al. An encoding and decoding scheme for long-distance ultrasonic localization[C]//Proc of the 39th Chinese Control Conf (CCC). Piscataway, NJ: IEEE, 2020: 5247−5252
[41]	Li Zheng, Chen Ruizhi, Guo Guangyi, et al. Dual-step acoustic chirp signals detection using pervasive smartphones in multipath and NLOS indoor environments[J]. IEEE Internet of Things Journal, 2023, 11(4): 6494−6507
[42]	Jia Naizheng, Cui Weimeng, Wang Yuwei, et al. Robust acoustic TOA estimation based on multipath extraction in frequency domain[C/OL]//Proc of the 13th Int Conf on Indoor Positioning and Indoor Navigation (IPIN). Piscataway, NJ: IEEE, 2023 [2024-04-22]. https://ieeexplore.ieee.org/document/10332467/
[43]	Chan Y, Ho K. A simple and efficient estimator for hyperbolic location[J]. IEEE Transactions on Signal Processing, 1994, 42(8): 1905−1915 doi: 10.1109/78.301830
[44]	Xiong Jinyu, Wang Wei, Zhu Zhongliang. A new TDOA algorithm based on Taylor series expansion in cellular networks[J]. Journal of China Institute of Communications, 2004, 25(4): 144−150
[45]	Hua Cheng, Zhao Kun, Dong Danan, et al. Multipath map method for tdoa based indoor reverse positioning system with improved Chan-Taylor algorithm[J]. Sensors, 2020, 20(11): 3223−3238 doi: 10.3390/s20113223
[46]	Zang Lan, Shen Chong, Zhang Kun, et al. Research on hybrid algorithm based on TDOA[C]//Proc of the 20th IEEE Int Conf on Communication Technology (ICCT). Piscataway, NJ: IEEE, 2020: 539−542
[47]	黄冬梅,张宁宁,胡安铎,等. 基于双层Xgboost和数据增强的空间负荷预测方法[J]. 电力工程技术,2023,42(1):201−208 doi: 10.12158/j.2096-3203.2023.01.024 Huang Dongmei, Zhang Ningning, Hu Anduo. Spatial load forecasting method based on double-layer Xgboost and data enhancement[J]. Electric Power Engineering Technology, 2023, 42(1): 201−208 (in Chinese) doi: 10.12158/j.2096-3203.2023.01.024
[48]	Chen Tianqi, Guestrin C. Xgboost: A scalable tree boosting system[C]//Proc of the 22nd ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York: ACM, 2016: 785–794
[49]	Yang Jinshan, Zhao Chenyue, Yu Haotong, et al. Use GBDT to predict the stock market[J]. Procedia Computer Science, 2020, 174: 161−171 doi: 10.1016/j.procs.2020.06.071
[50]	Ke Guolin, Meng Qi, Finley T, et al. LightGBM: A highly efficient gradient boosting decision tree[C]//Proc of the 31st Int Conf on Neural Information Processing Systems. New York: ACM, 2017: 3149−3157
[51]	李丽颖,张润泽,魏同权. 面向边缘计算的服务解耦与部署策略[J]. 计算机研究与发展,2023,60(5):1073−1085 Li Liying, Zhang Runze, Wei Tongquan. Service decoupling and deployment strategies for edge computing[J]. Journal of Computer Research and Development, 2023, 60(5): 1073−1085 (in Chinese)
[52]	Qu Yi, Lin Zhengkui, Li Honglei, et al. Feature recognition of urban road traffic accidents based on GA-Xgboost in the context of big data[J]. IEEE Access, 2019, 7: 170106−170115 doi: 10.1109/ACCESS.2019.2952655
[53]	严忻恺,霍宇驰,鲍虎军. 神经渲染及其硬件加速综述[J]. 计算机研究与发展,2024,61(11):2846−2869 Yan Xinkai, Huo Yuchi, Bao Hujun. Survey on neural rendering and its hardware acceleration[J]. Journal of Computer Research and Development, 2024,61(11):2846−2869 (in Chinese)

[1]	Xie Wenbing, Guan Ruixue, Zhang Yiming, Li Jiamei, Wang Jun. Efficient Optimization of Erasure Coding for Storage Library[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202440091
[2]	Yan Zhiyuan, Xie Biwei, Bao Yungang. HVMS: A Hybrid Vectorization-Optimized Mechanism of SpMV[J]. Journal of Computer Research and Development, 2024, 61(12): 2969-2984. DOI: 10.7544/issn1000-1239.202330204
[3]	Wang Chuang, Ding Yan, Huang Chenlin, Song Liantao. Bitsliced Optimization of SM4 Algorithm with the SIMD Instruction Set[J]. Journal of Computer Research and Development, 2024, 61(8): 2097-2109. DOI: 10.7544/issn1000-1239.202220531
[4]	Shen Jie, Long Biao, Jiang Hao, Huang Chun. Implementation and Optimization of Vector Trigonometric Functions on Phytium Processors[J]. Journal of Computer Research and Development, 2020, 57(12): 2610-2620. DOI: 10.7544/issn1000-1239.2020.20190721
[5]	Yan Hongfei, Zhang Xudong, Shan Dongdong, Mao Xianling, Zhao Xin. SIMD-Based Inverted Index Compression Algorithms[J]. Journal of Computer Research and Development, 2015, 52(5): 995-1004. DOI: 10.7544/issn1000-1239.2015.20131548
[6]	Zhao Long, Han Wenbao, and Yang Hongzhi. Research on ECC Attacking Algorithm Based on SIMD Instructions[J]. Journal of Computer Research and Development, 2012, 49(7): 1553-1559.
[7]	He Yi, Ren Ju, Wen Mei, Yang Qianming, Wu Nan, Zhang Chunyuan, and Guo Min. Research on FPGA-Based Paging-Simulation Model for SIMD Architecture[J]. Journal of Computer Research and Development, 2011, 48(1): 9-18.
[8]	Huang Shuangqu, Xiang Bo, Bao Dan, Chen Yun, and Zeng Xiaoyang. VLSI Implementation of Multi-Standard LDPC Decoder Based on SIMD Architecture[J]. Journal of Computer Research and Development, 2010, 47(7): 1313-1320.
[9]	Li Zhaopeng, Chen Yiyun, Ge Lin, and Hua Baojian. A Formal Certifying Framework for Assembly Programs[J]. Journal of Computer Research and Development, 2008, 45(5): 825-833.
[10]	Lin Jiao, Chen Wenguang, Li Qiang, Zheng Weimin, Zhang Yimin. A New Data Clustering Algorithm for Parallel Whole-Genome Shotgun Sequence Assembly[J]. Journal of Computer Research and Development, 2006, 43(8): 1323-1329.