Investigation of Weighted Least Squares Methods for Multitarget Tracking with Multisensor Data Fusion
Tóm tắt
Target localization in a wireless sensor network (WSN) has received more and more attention in recent years, and has promoted many new applications due to the low cost, low bandwidth, low energy consumption, and collision avoidance mechanism. How to provide accurate location information has always been a hot research topic in 5G/B5G application scenarios. In this paper, the path loss information or received signal strength (RSS) of the received signal is considered in a WSN for the extended Kalman filter (EKF) to realize trajectory tracking of multiple targets, and the tracked targets are then localized through multiple sensors. Moreover, since there may be several objects or clutter interference in the communication environment, in order to reduce the impact of interference, we consider the probabilistic data association filter (PDAF) or probability hypothesis density filter (PHDF) to improve the tracking performance. Each sensor sends the received distance estimation information to the fusion center (FC), which calculates the optimal position for each target. Through simulation results, the proposed weighted least squares (WLS) trilateration method in this paper can effectively improve the average root mean squared error (RMSE) performance as sensors are evenly distributed around the tracking trajectories.
Từ khóa
Tài liệu tham khảo
Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on sensor networks. IEEE Communications Magazine, 40(8), 102–114.
Patwari, N., Ash, J. N., Kyperountas, S., Hero, A. O., Moses, R. L., & Correal, N. S. (2005). Locating the nodes: Cooperative localization in wireless sensor networks. IEEE Signal Processing Magazine, 22(4), 54–69.
He, T., Vicaire, P., Yan, T., Cao, Q., Zhou, G., Gu, L., Luo, L., Stoleru, R., Stankovic, J. A., & Abdelzaher, T. F. (2006). Achieving long-term surveillance in vigilnet. In Proceedings IEEE INFOCOM 2006. 25th IEEE International Conference on Computer Communications, pp. 1–12.
Wood, A. D., Stankovic, J. A., Virone, G., Selavo, L., He, Z., Cao, Q., Doan, T., Wu, Y., Fang, L., & Stoleru, R. (2008). Context-aware wireless sensor networks for assisted living and residential monitoring. IEEE Network, 22(4), 26–33.
Mittal, R., & Bhatia, M. P. S. (2010). Wireless sensor networks for monitoring the environmental activities. In 2010 IEEE International Conference on Computational Intelligence and Computing Research, pp. 1–5.
Lee, H. C., Banerjee, A., Fang, Y. M., Lee, B. J., & King, C. T. (2010). Design of a multifunctional wireless sensor for in-situ monitoring of debris flows. IEEE Transactions on Instrumentation and Measurement, 59(11), 2958–2967.
Mahfouz, S., Mourad-Chehade, F., Honeine, P., Farah, J., & Snoussi, H. (2016). Non-parametric and semi-parametric RSSI/distance modeling for target tracking in wireless sensor networks. IEEE Sensors Journal, 16(7), 2115–2126.
Wang, Y., Jin, Q., & Ma, J. (2013). Integration of range-based and range-free localization algorithms in wireless sensor networks for mobile clouds. In 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, pp. 957–961.
He, T., Huang, C., Blum, B. M., Stankovic, J. A., & Abdelzaher, T. F. (2003). Range-free localization schemes for large scale sensor networks. In MobiCom.
Liberti, J. C., & Rappaport, T. S. (1999). Smart Antennas for Wireless Communication: IS-95 and third Generation CDMA Application. Prentice Hall.
Chan, Y. T., & Ho, K. C. (1994). A simple and efficient estimator for hyperbolic location. IEEE Transactions on Signal Processing, 42(8), 1905–1915.
Peng, R., & Sichitiu, M. L. (2006). Angle of arrival localization for wireless sensor networks. In 2006 3rd Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks, vol. 1, pp. 374–382.
Ouyang, R. W., Wong, A. K. S., & Lea, C. T. (2010). Received signal strength-based wireless localization via semidefinite programming: Noncooperative and cooperative schemes. IEEE Transactions on Vehicular Technology, 59(3), 1307–1318.
Wang, G., & Yang, K. (2011). A new approach to sensor node localization using RSS measurements in wireless sensor networks. IEEE Transactions on Wireless Communications, 10(5), 1389–1395.
Li, X. (2006). RSS-based location estimation with unknown pathloss model. IEEE Transactions on Wireless Communications, 5(12), 3626–3633.
Cheng, X., Shu, F., Li, Y., Zhuang, Z., Di, W., & Wang, J. (2023). Optimal measurement of drone swarm in RSS-based passive localization with region constraints. IEEE Open Journal of Vehicular Technology, 4, 1–11.
Bai, L., Ciravegna, F., Bond, R., & Mulvenna, M. (2020). A low cost indoor positioning system using bluetooth low energy. IEEE Access, 8, 136858–136871.
Yue, Y., Chen, R., Chen, L., Zheng, X., Dewen, W., Li, W., & Yuan, W. (2021). A novel 3-D indoor localization algorithm based on BLE and multiple sensors. IEEE Internet of Things Journal, 8(11), 9359–9372.
Yao, H., Shu, H., Liang, X., Yan, H., & Sun, H. (2020). Integrity monitoring for bluetooth low energy beacons RSSI based indoor positioning. IEEE Access, 8, 215173–215191.
Ghosh, P., Tran, J. A., & Krishnamachari, B. (2020). Arrest: A RSSI based approach for mobile sensing and tracking of a moving object. IEEE Transactions on Mobile Computing, 19(6), 1260–1273.
Sadowski, S., & Spachos, P. (2018). RSSI-based indoor localization with the internet of things. IEEE Access, 6, 30149–30161.
Cai, S., Liao, W., Luo, C., Li, M., Huang, X., & Li, P. (2017). CRIL: An efficient online adaptive indoor localization system. IEEE Transactions on Vehicular Technology, 66(5), 4148–4160.
Zhu, H., Haibo, W., & Luo, M. (2023). Environmentally adaptive event-driven robust cubature Kalman filter for RSS-based targets tracking in mobile wireless sensor network. IEEE Internet of Things Journal, 10(6), 5530–5542.
Shen, Y., Hwang, B., & Jeong, J. P. (2020). Particle filtering-based indoor positioning system for beacon tag tracking. IEEE Access, 8, 226445–226460.
Julier, S. J., & Uhlmann, J. K. (1997). A new extension of the Kalman filter to nonlinear systems. In Proceedings of SPIE, vol. 3068, pp. 182–193.
Singer, R. A. (1970). Estimating optimal tracking filter performance for manned maneuvering targets. IEEE Transactions on Aerospace and Electronic Systems, AES–6(4), 473–483.
Singer, R., & Sea, R. (1973). New results in optimizing surveillance system tracking and data correlation performance in dense multitarget environments. IEEE Transactions on Automatic Control, 18(6), 571–582.
Bar-Shalom, Y., Daum, F., & Huang, J. (2009). The probabilistic data association filter. IEEE Control Systems, 29(6), 82–100.
Mahler, R. P. S. (2003). Multitarget Bayes filtering via first-order multitarget moments. IEEE Transactions on Aerospace and Electronic Systems, 39(4), 1152–1178.
Vo, B. N., & Ma, W. K. (2006). The gaussian mixture probability hypothesis density filter. IEEE Transactions on Signal Processing, 54(11), 4091–4104.
Chang, D. C., & Fang, M. W. (2014). Bearing-only maneuvering mobile tracking with nonlinear filtering algorithms in wireless sensor networks. IEEE Systems Journal, 8(1), 160–170.
Pahlavan, K., & Levesque, A. (1995). Wireless information networks. NY, USA: Wiley-Interscience New York.
Da, K., Li, T., Zhu, Y., & Qiang, F. (2020). A computationally efficient approach for distributed sensor localization and multitarget tracking. IEEE Communications Letters, 24(2), 335–338.
Cheng, L., Li, Y., Xue, M., & Wang, Y. (2021). An indoor localization algorithm based on modified joint probabilistic data association for wireless sensor network. IEEE Transactions on Industrial Informatics, 17(1), 63–72.
Bar-Shalom, Y., & Li, X. R. (1995). Multitarget-multisensor tracking: Principles and techniques. Storrs, CT: YBS Publishing.
Longqiang, N., Shesheng, G., & Li, X. (2011). Improved probabilistic data association and its application for target tracking in clutter. In 2011 International Conference on Electronics, Communications and Control (ICECC), pp. 293–296.
Choi, M. E., & Seo, S. W. (2016). Robust multitarget tracking scheme based on gaussian mixture probability hypothesis density filter. IEEE Transactions on Vehicular Technology, 65(6), 4217–4229.