An effective algorithm to overcome the practical hindrance for Wi-Fi based indoor positioning system

Abstract

<jats:title>Abstract</jats:title><jats:p>Indoor tracking has evolved with various methods. The most popular method is using signal strength measuring techniques like triangulation, trilateration and fingerprinting, etc. Generally, these methods use the internal sensors of the smartphone. All these techniques require an adequate number of access point signals. The estimated positioning accuracy depends on the number of signals received at any point and precision of its signal (Wi-Fi radio waves) strength. In a practical environment, the received signal strength indicator (RSSI) of the access point is hindered by obstacles or blocks in the direct path or Line of sight. Such access points become an anomaly in the calculation of position. By detecting the anomaly access points and neglecting it during the computation of an indoor position will improve the accuracy of the positioning system. The proposed method, Practical Hindrance Avoidance in an Indoor Positioning System (PHA-IPS), eliminate the anomaly nodes while estimating the position, so then enhances the accuracy.</jats:p>

Combining Wi-Fi Fingerprinting and Pedestrian Dead Reckoning to Mitigate External Factors for a Sustainable Indoor Positioning System

“…In the practical scenario, when implementing the fingerprint algorithm for multiple floors and locations, the complexity of running the algorithm is high since the KNN matching algorithm must compare fingerprints with all data in the dataset and, in the presence of new, temporary, or permanent obstacles [20,23], the K-NN fingerprinting method generates a high distance error. This condition requires a repetition of the offline phase, a laborious and tedious process.…”

“…In a practical environment, achieving sustainable indoor positioning is complex. The Wi-Fi-based positioning system suffers from substantial errors due to many external factors, such as signal inconsistency, signal loss caused by the external barrier, and the NLOS propagation signal [20,34] weakness.…”

“…It has a distance error range from 2 to 3 m in normal circumstances, without any new barriers to the access points. Nevertheless, the fingerprinting methodology's accuracy is affected by any new obstacles, such as temporary or permanent constructions after the fingerprint dataset is recorded, any moment of furniture or access point location, and even human intervention [5,20] in the line of the signal [21][22][23]. Any change that occurs in the IPS environment after the offline phase would require that the fingerprint radio map is recreated by repeating the offline recording phase [13,18], which is cumbersome.…”

“…In comparison to existing IPS such as Horus, UnLoc, HALLWAY, VC, other IPS using Wi-Fi fingerprint techniques have limitations in terms of accuracy, robustness, resilience, and applicability to specific building structures. In contrast, the proposed method offers novel contributions to address each limitation by introducing a robust anomaly node [20] detection mechanism by combining Wi-Fi fingerprinting and PDR, leveraging their complementary abilities to enhance sustainable indoor positioning with improved accuracy, resilience, and robustness in various building structures. The experimental results have demonstrated the effectiveness of the proposed method in improving IPS accuracy and reducing the impact of anomaly nodes.…”

“…The static method is simple to calculate, whereas the dynamic method provides a more robust step-length measurement. The static method assumes that any step detected has the same step length, and, surprisingly, is the constant human natural walking step length [20]. In the static method, the step length is calculated using Equation (3) [39,44,45], which measures the mobile phone acceleration signal (using the accelerometer) to determine the step frequency f t at time t, as shown below:…”