Localization Scheme with MAP Pre-filter in Wireless Sensor Network Combating Intensive Measurement Noise
Increasing advances in building reliable and efficient wireless sensor network (WSN) provide a promising prospect in the applications of monitoring and localization. However, due to the intensive measurement noise, observations from sensors can be severely deteriorated, rendering most existing localization schemes unattractive. In this paper, we propose a new localization scheme, which can obtain more reliable observations from the deteriorated ones, and improve the localization performance. That is, we design a maximum a posterior (MAP) pre-filter, which can filter out the measurement noise, and derive the more reliable filtered observations. Then such filtered observations will be adopted in the sequential two-phase Bayesian process, which combines the priori estimative results and the current filtered observations to derive the current estimative localization. Numerical simulations validate the new localization scheme, which can indeed obtain a better performance than traditional schemes.
KeywordsWireless sensor network (WSN) Localization Two-phase Bayesian process MAP pre-filter
This work is supported by National Science and Technology Major Project 2014ZX03001027 and the Natural Science Foundation of China 61379016.
- 1.Zhang, P., Jing, Y.K., Lin, S., Nevat, I.: Distributed event detection under Byzantine attack in wireless sensor networks. In: IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (2014)Google Scholar
- 2.Patwari, N., Ash, J.N., Kyperountas, S., Hero, A.O., Moses, R.L., Correal, N.S.: Locating the nodes: cooperative localization in wireless sensor networks. IEEE Signal Process. Mag. 22(4), 54–69 (2005)Google Scholar
- 3.Zhou, Q., Li, D., Kar, S., Huie, L.M., Poor, H.V., Cui, S.: Learning-based distributed detection-estimation in sensor networks with unknown sensor defects. IEEE Trans. Signal Process. 65(1), 130–145 (2015)Google Scholar
- 4.Kazemi, M., Mahboobi, B., Ardebilipour, M.: Performance analysis of simultaneous location and power estimation using WLS method for cognitive radio. IEEE Commun. Lett. 15(10), 1062–1064 (2011)Google Scholar
- 5.Guvenc, I., Chong, C.C.: A survey on TOA based wireless localization and NLOS mitigation techniques. IEEE Commun. Surv. Tutor. 11(3), 107–124 (2009)Google Scholar
- 6.Abdolee, R., Saur, S., Champagne, B., Sayed, A.H.: Diffusion LMS localization and tracking algorithm for wireless cellular networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 4598–4602 (2013)Google Scholar
- 7.Rabbat, M.G., Nowak, R.D.: Decentralized source localization and tracking [wireless sensor networks]. In: Proceedings of 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 3, pp. iii-921–iii-924 (2004)Google Scholar
- 8.Coates, M.: Distributed particle filters for sensor networks. In: International Symposium on Information Processing in Sensor Networks, pp. 99–107 (2004)Google Scholar
- 9.Ristic, B., Vo, B.T., Vo, B.N., Farina, A.: A tutorial on bernoulli filters: theory, implementation and applications. IEEE Trans. Signal Process. 61(13), 3406–3430 (2013)Google Scholar
- 10.Li, B., Hou, J., Li, X., Nan, Y., Nallanathan, A., Zhao, C.: Deep sensing for space-time doubly selective channels: when a primary user is mobile and the channel is flat Rayleigh fading. IEEE Trans. Signal Process. 64(13), 3362–3375 (2016)Google Scholar
- 11.Camp, T., Boleng, J., Davies, V.: A survey of mobility models for ad hoc network research. Wirel. Commun. Mob. Comput. 2(5), 483–1502 (2002)Google Scholar
- 12.Figueiras, J., Frattasi, S.: Mobile Positioning and Tracking: From Conventional to Cooperative Techniques. Wiley, New York (2010)Google Scholar