Performance Of Indoor Positioning Research Articles

Sparse [formula omitted]-Laplace kernel online prediction for indoor localization in the Internet of Things

As an important component of IoT-oriented applications, the indoor positioning estimation is getting increasing concern with IoT’s rapid development. However, the performance of indoor positioning is heavily relied on the complexity of the environment, which is always full of noises, such as Gaussian noise mixed with impulsive noise. These noises can deteriorate the precision performance of indoor positioning identification systems. In order to attack this problem, we propose a new kernel called generalized q-Laplace kernel to produce a new q-Laplace kernel adaptive filtering algorithm (qLaKAF), which is combined with the recently proposed kernel mean p-power error criterion (KMPE). The proposed qLaKAF has two vital features. Firstly, the q-Laplace kernel is employed to combat the Gaussian noise together with abrupt noise in real-world scenarios. Besides, the KMPE is utilized to obtain higher-order information in addition to second-order information which facilitates to suppress mixed noise. Furthermore, a Strengthened Surprise Criterion (SSC) is applied to qLaKAF to reduce the size of neural networks. The qLaKAF algorithm assisted by SSC is called Strengthened Surprise Criterion q-Laplace kernel adaptive filtering algorithm (SSC-qLaKAF). Three experiments are carried out on two real-world scenarios to validate the effectiveness and accuracy performance. The experimental results demonstrate that the accuracy has been improved by at least 3.6%; meanwhile, the SSC-qLaKAF neural network size can be reduced by up to 12.5%, without much loss of accuracy performance compared to qLaKAF.

An Approach to Improve the Indoor Positioning Performance of Pseudolite/UWB System with Ambiguity Resolution

Indoor localization is still an open challenge, and some pseudolites have been developed to achieve seamless positioning service based on some commercialized GNSS chips. However, most of these indoor localization technologies often fail in a reasonable solution to the key problems such as low cost and highly accurate and efficient for users. In this paper, we propose an indoor location method based on integrated pseudolite and UWB; the virtual pseudo-range measurements of UWB are used to replace the pseudo-range measurements of pseudolite to solve the indoor multipath problem, which is tightly coupled with the corrected carrier phase measurements of pseudolite. Then, the channel-difference observation equation and UWB-aided ambiguity resolution are proposed for precise positioning. In order to test the proposed method, several experiments are conducted. The results show that the virtual pseudo-range errors from UWB are smaller than that of GNSS, and such a small bias will be better for the fast fixing of ambiguity. In addition, the positioning accuracy of the proposed indoor location method is improved from cm-level for the float solution to mm-level for the fixed solution; these performances would be more convincing to users than that given in the most pseudolite and UWB.

A Two-Step Fusion Method of Wi-Fi FTM for Indoor Positioning.

The Wi-Fi fine time measurement (FTM) protocol specified in the IEEE 802.11-2016 standard provides a new two-way ranging approach to enhance positioning capability. Similar to other wireless signals, the accuracy of the real-time range measurement of FTM is influenced by various errors. In this work, the characteristics of the ranging errors is analyzed and an abstract ranging model is introduced. From the perspective of making full use of the range measurements from FTM, this paper designs two positioning steps and proposes a fusion method to refine the performance of indoor positioning. The first step is named single-point positioning, locating the position with the real-time range measurements based on the geometric principle. The second step is named the improved matching positioning, which constructs a distance database by utilizing the existing scene information and uses the modified matching algorithm to obtain the position. In view of the different positioning accuracies and error distributions from the results of the aforementioned two steps, a fusion method using the indirect adjustment principle is proposed to adjust the positioning results, and the advantages of the matching scene information and the range measurements are served simultaneously. Finally, a number of tests are conducted to assess the performance of the proposed method. The experimental results demonstrate that the precision and stability of indoor positioning are improved by the proposed fusion method.

Design and Implementation of Indoor Positioning Performance Test System for IoT Intelligent Communication Terminal

Indoor positioning technique is the foundation of Internet of Things (IoT) and location big data, which provides IoT function and location data for retail, manufacturing, energy, medical, emergency, logistics, robotics and other industries. Indoor positioning performance is an important performance indicator of IoT intelligent communication terminals. In order to accurately evaluate the indoor positioning performance of IoT intelligent communication terminals, this paper constructs an indoor positioning performance test system for IoT intelligent communication terminals. The test results show that the uncertainty of horizontal positioning accuracy of Wi-Fi positioning and Bluetooth positioning can be limited within 0.32 m (k=2).