Collecting data like location information is an essential part of concepts like the “IoT” or “Industry 4.0”. In the case of the development of a precise localization system and an integrated navigation system, indoor location technology receives more and more attention and has become a hot research topic. Common indoor location techniques are mainly based on wireless local area network, radio frequency tag, ZigBee technology, Bluetooth technology, infrared technology and ultra-wideband (UWB). However, these techniques are vulnerable to various noise signals and indoor environments, and also the positioning accuracy is easily affected by the complicated indoor environment. We studied the problem of real-time location tracking based on UWB in an indoor environment in this paper. We have proposed a combinational filtering algorithm and an improved Two-Way Ranging (ITWR) method for indoor real-time location tracking. The simulation results prove that the real-time performance and high accuracy of the presented algorithm can improve location accuracy. The experiment shows that the combinational algorithm and ITWR method which are applied to the positioning and navigation of the smart supermarket, have achieved quiet good results in positioning accuracy. The average positioning error is less than 10[Formula: see text]cm, some of the improvements can elevate the positioning accuracy by 17.5%. UWB is a suitable method for indoor real-time location tracking and has important theoretic value and practical significance.

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For energy-based multi-source localization in wireless sensor networks (WSNs), multi-resolution search and expectation maximization algorithms are of either high energy consumption or low localization accuracy. In this paper, we propose a multisource localization algorithm via sparse reconstruction based on fully distributed structure (MLSR-FD) in WSNs. The fully distributed structure is used to reduce the energy consumption of WSNs. It requires the local data transmission and processing. Each sensor node transmits energy observations to its nearest neighbors (nodes within one-hop communication range) and also receives energy observations from its nearest neighbors. In order to reduce the computational complexity, multi-resolution redundant dictionary is used to locate sources using sparse reconstruction. Finally, the robust consensus algorithm fuses the local source position estimates of the nearest nodes and converges to the global estimates. Simulation results show that the proposed MLSR-FD algorithm provides a good tradeoff between localization accuracy and energy consumption.

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