Multi-Information Fusion Indoor Localization Using Smartphones

Abstract

Accurate indoor localization estimation has important social and commercial values, such as indoor location services and pedestrian retention times. Acoustic-based methods can achieve high localization accuracies in specific scenarios with special equipment; however, it is a challenge to obtain accurate localization with general equipment in indoor environments. To solve this problem, we propose a novel fusion CHAN and the improved pedestrian dead reckoning (PDR) indoor localization system (CHAN-IPDR-ILS). In this system, we propose a step length estimation method that adds the previous two steps for extracting more accurate information to estimate the current step length. The maximum influence factor is set for the previous two steps to ensure the importance of the current step length. We also propose a heading direction correction method to mitigate the errors in sensor data. Finally, pedestrian localization is achieved using a motion model with acoustic estimation and dynamic improved PDR estimation. In the fusion localization, the threshold and confidence level of the distance between estimation base-acoustic and improved PDR estimation are set to mitigate accidental and cumulative errors. The experiments were performed at trial sites with different users, devices, and scenarios, and experimental results demonstrate that the proposed method can achieve a higher accuracy compared with the state-of-the-art methods. The proposed fusion localization system manages equipment heterogeneity and provides generality and flexibility with different devices and scenarios at a low cost.

A Positioning and Navigation Method Combining Multimotion Features Dead Reckoning with Acoustic Localization

“…To verify the positioning performance, we carried out multiple experiments on the PDR algorithm, CHAN–Taylor hybrid algorithm, CHAN–IPDR–ILS [ 34 ], improved PDR algorithm, and proposed algorithm using different phones in different scenarios. Figure 13 presents the localization performance of the above-mentioned algorithms for two volunteers using the Vivo X30 and OPPO K5 mobile phones in scene 1.…”

“…Wang, M., et al [ 33 ] proposed a method that combines the Hamming distance-based acoustic estimation with PDR. Yan proposed a CHAN–IPDR–ILS method in reference [ 34 ], which combines the CHAN algorithm and PDR algorithm. Al Mamun et al [ 35 ] presented a lightweight fusion technique combining the PDR algorithm with the RSSI fingerprinting method.…”

Deep Learning-Based Geomagnetic Navigation Method Integrated with Dead Reckoning

“…In ref. [53], a novel fusion indoor localization system, CHAN-IPDR-ILS, based on acoustic signals and PDR was proposed. In this system, an improved step estimation method and a heading direction correction method were introduced to solve the problem of accumulated errors in the PDR algorithm.…”

A Fusion Positioning Method for Indoor Geomagnetic/Light Intensity/Pedestrian Dead Reckoning Based on Dual-Layer Tent–Atom Search Optimization–Back Propagation

“…Method 1 uses gyroscope data to integrate and solve the angular velocity, resulting in the heading angle [ 30 ]. Method 2 uses magnetometer XYZ data to determine the angle between the geomagnetic north and the phone, thereby obtaining the heading angle [ 31 ]. Among these methods, Method 1 has the advantage of being unaffected by external environmental disturbances and remaining stable over short periods.…”

“…Step 3: Determine the fitness function of the atom and calculate it to obtain the current optimal position of the atom and the optimal solution. In this paper, the positioning accuracy is used as the fitness function, and the expression is shown in Equation (31):…”