A Least Square Dynamic Localization Algorithm Based on Statistical Filtering Optimal Strategy
In wireless sensor network localization, many anchor nodes and target node exchange information at specified time intervals to obtain the distance information between each anchor node and the target node. With this information, the coordinates of the target node can be achieved through the calculation of the positioning algorithm. However, as there are numerous negative factors like non-line-of-sight measurement, complex multipath fading, which leads to high-level localization error. To improve localization accuracy, an improved least square localization algorithm is proposed, which combines the least square localization method with the statistical filtering optimization strategy. The simulation results show that this algorithm can effectively reduce localization error and achieve more accurate localization.
KeywordsWireless sensor networks Localization Least square method Extended Kalman filter Particle filter
The research presented in this paper is supported by the National Natural Science Foundation of China (61671174, 61601142), the Natural Science Foundation of Shandong Province of China (ZR2015FM027), WeiHai Research program of Science and Technology (16), the Laboratory of Satellite Navigation System and Equipment Technology (EX166840037, EX166840044), the Guangxi Key Laboratory of Automatic Detecting Technology and Instruments (YQ18206,YQ15203), the Natural Scientific Research Innovation Foundation of the Harbin Institute of Technology (HIT.NSRIF.2015122), the State Key Laboratory of Geo-information Engineering (SKLGIE2014-M-2-4), and Discipline Construction Guiding Foundation in Harbin Institute of Technology (Weihai) (WH20150211).
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