A Frequency Modulation Fingerprint-Based Positioning Algorithm for Indoor Mobile Localization of Photoelectric Modules

Optoelectronic modules have a wide range of applications in the field of wireless communication. However, the function of mobile localization has not been realized in optoelectronic modules. In this paper, an indoor positioning algorithm, which was based on frequency modulation (FM) signals, was realized in optoelectronic modules. Firstly, FM monitoring receiver DB4004 was used to collect FM signals; Secondly, FM signals were preprocessed and analyzed to build a FM dataset. Finally, weighted centroid k-nearest neighbors (WC-KNN) precise positioning algorithm was proposed to obtain the position information of the photoelectric module. Experimental results showed that the median location error of the WC-KNN algorithm can reach 0.8 m and additional hardware equipment was not required. The research results provided the feasibility for the practical application of equipment based on optoelectronic devices in various fields.

Underwater Localization Techniques

Underwater acoustic communication uses sound waves to trans-receive information, diving deep inside water, environment scanning, undersea explorations, disaster prevention, etc. In this chapter, an attempt has been made to cover stationary and mobile localization algorithm. They are further subdivided into distributed and centralized. Each one is further subcategorized into estimation-based and prediction-based schemes. The category therefore extends on the basis of ranging method, communication, and synchronization, some of which are area localization, sensor-based localization, forming a sensor array, motion-aware self-localization, silent localization. Each one will be discussed in detail in this chapter. At last, hybrid technique is also discussed, which combines stationary and mobile techniques. The discussion includes various nodes including anchor node, unknown node, sink node, and reference node. Various methods to follow the techniques are also discussed, which include anchor-based method, ranging method, and message communication.

A mobile localization method based on a robust extend Kalman filter and improved M-estimation in Internet of things

As the key technology for Internet of things, wireless sensor networks have received more attentions in recent years. Mobile localization is one of the significant topics in wireless sensor networks. In wireless sensor network, non-line-of-sight propagation is a common phenomenon leading to the growing non-line-of-sight error. It is a fatal impact for the localization accuracy of the mobile target. In this article, a novel method based on the nearest neighbor variable estimation is proposed to mitigate the non-line-of-sight error. First, the linear regression model of the extended Kalman filter is used to obtain the residual of the distance measurement value. After that, the residual analysis is used to complete the identification of the measurement value state. Then, by analyzing the statistical characteristics of the non-line-of-sight residual, the nearest neighbor variable estimation is proposed to estimate the probability density function of residual. Finally, the improved M-estimation is proposed to locate the mobile robot. Experiment results prove that the accuracy and robustness of the proposed algorithm are better than other methods in the mixed line-of-sight/non-line-of-sight environment. The proposed algorithm effectively inhibits the non-line-of-sight error.