Welcome to Francis Academic Press

Academic Journal of Computing & Information Science, 2021, 4(7); doi: 10.25236/AJCIS.2021.040709.

Human Positioning System based on Pyroelectric Infrared Sensor and GA-BP Neural Network


Xuneng Tong, Wufeng Wang, Zhigang Han

Corresponding Author:
Zhigang Han

Integrated Circuit Engineering Department, Tongji University, Shanghai, China


In order to use the PIR sensor to position the human body more effectively, this article proposes a human target positioning system based on multiple sensors layouts and GA-BP neural network. On the hardware, an PIR peripheral circuit is designed to detect, amplify, and filter infrared signals. On the software, this article uses genetic algorithm to optimize BP neural network, and designs a trilateration algorithm to convert the distances from the three sensors to  human into coordinates. The experimental results show that the GA-BP neural network converges faster, the mean square error of the X-axis and Y-axis are reduced by 90.3% and 81.9%, and the positioning error is reduced by 0.24m.


PIR, Human target positioning, amplifying and filtering circuit, GA-BP neural network, trilateration

Cite This Paper

Xuneng Tong, Wufeng Wang, Zhigang Han. Human Positioning System based on Pyroelectric Infrared Sensor and GA-BP Neural Network. Academic Journal of Computing & Information Science (2021), Vol. 4, Issue 7: 59-66. https://doi.org/10.25236/AJCIS.2021.040709.


[1] Xiong J, Li F, Zhao N, et al. Tracking and recognition of multiple human targets moving in a wireless pyroelectric infrared sensor network[J]. Sensors, 2014, 14(4): 7209-7228.

[2] Hao, Qi, Fei Hu, and Jiang Lu. "Distributed multiple human tracking with wireless binary pyroelectric infrared (PIR) sensor networks." SENSORS, 2010 IEEE. IEEE, 2010.

[3] Hao, Qi, et al. "Human tracking with wireless distributed pyroelectric sensors." IEEE Sensors Journal 6.6 (2006): 1683-1696.

[4] Haoran L, Cuixiang Z, Xuan L, et al. Study on a neural network optimization algorithm based on improved genetic algorithm[J]. chinese journal of scientific instrument, 2016, 37(7): 1573-1580.

[5] Ding, Shifei, Chunyang Su, and Junzhao Yu. "An optimizing BP neural network algorithm based on genetic algorithm." Artificial intelligence review 36.2 (2011): 153-162.

[6] Cui X, Yang J, Li J, et al. Improved genetic algorithm to optimize the Wi-Fi indoor positioning based on artificial neural network[J]. IEEE Access, 2020, 8: 74914-74921.

[7] Yun J, Song M H. Detecting direction of movement using pyroelectric infrared sensors[J]. IEEE Sensors Journal, 2014, 14(5): 1482-1489.

[8] Zappi P, Farella E, Benini L. Tracking motion direction and distance with pyroelectric IR sensors[J]. IEEE Sensors Journal, 2010, 10(9): 1486-1494.

[9] Xuhui, Zhang, et al. "The optimization of RSSI-neural network positioning algorithm." 2014 Fourth International Conference on Instrumentation and Measurement, Computer, Communication and Control. IEEE, 2014.

[10] Kehua M, YaoDong C, Xiao M. An indoor positioning technology based on GA-BP neural network[C]//2011 6th International Conference on Computer Science & Education (ICCSE). IEEE, 2011: 305-309.

[11] Mehmood, Hamid, and Nitin K. Tripathi. "Optimizing artificial neural network-based indoor positioning system using genetic algorithm." International Journal of Digital Earth 6.2 (2013): 158-184.

[12] Oguejiofor, O. S., et al. "Trilateration based localization algorithm for wireless sensor network." International Journal of Science and Modern Engineering (IJISME) 1.10 (2013): 2319-6386.