WiFi Indoor Localization with CSI Fingerprinting-Based Random Forest

Abstract

WiFi fingerprinting indoor positioning systems have extensive applied prospects. However, a vast amount of data in a particular environment has to be gathered to establish a fingerprinting database. Deficiencies of these systems are the lack of universality of multipath effects and a burden of heavy workload on fingerprint storage. Thus, this paper presents a novel Random Forest fingerprinting localization (RFFP) method using channel state information (CSI), which utilizes the Random Forest model trained in the offline stage as fingerprints in order to economize memory space and possess a good anti-multipath characteristic. Furthermore, a series of specific experiments are conducted in a microwave anechoic chamber and an office to detail the localization performance of RFFP with different wireless channel circumstances, system parameters, algorithms, and input datasets. In addition, compared with other algorithms including K-Nearest-Neighbor (KNN), Weighted K-Nearest-Neighbor (WKNN), REPTree, CART, and J48, the RFFP method provides far greater classification accuracy as well as lower mean location error. The proposed method offers outstanding comprehensive performance including accuracy, robustness, low workload, and better anti-multipath-fading.

Fusion of Channel State Information and Received Signal Strength for Indoor Localization Using a Single Access Point

“…Keeping in mind that the evaluation setup, the collection procedures and the granularity of fingerprinting is different in each scenario of the related papers, a direct comparison should be not carried out. However, in order to highlight the results of the fusion of CSI and RSS, it can be observed that our work yields similar and comparable results when the model is evaluated with training points [28,29,46]. In addition, when independent points are used to evaluate the model, the other works yield best results but employing two or more access points [22,23,32] or using a low granularity for the fingerprinting [25,26,30,35].…”

Neural Network-Based Alzheimer’s Patient Localization for Wireless Sensor Network in an Indoor Environment

“…The mean localization error obtained using type-2 FL was about 0.43 m, with a navigation accuracy of 98.2%. The Random Forest (RF)-based fingerprinting localization technique using Wi-Fi channel information for indoor positioning was proposed in [28] and compared with other localization techniques such as KNN and weighted KNN (WKNN). The RF algorithm outperformed the KNN and WKNN in terms of localization accuracy, achieving 0.4033 m compared to KNN's results of 1.7782 m and WKNN's of 1.0517 m in a non-line-of-sight circumstance.…”

Is Centimeter Accuracy Achievable for LTE-CSI Fingerprint-Based Indoor Positioning?

“…Meanwhile, RF does not require data preprocessing, e.g., normalization, so that the channel amplitude and phase information can be directly used as attributes. The implementation of RF follows the work in [8]. Since RF works on real-valued attributes [8], we set the amplitudes and phases of H p (t s ) as the input attributes.…”

“…The implementation of RF follows the work in [8]. Since RF works on real-valued attributes [8], we set the amplitudes and phases of H p (t s ) as the input attributes. Based on the subsets created by Bootstrap Aggregation, T decision trees are trained in parallel, and the outputs of these trees are finally combined by the voting algorithm.…”

“…We conjecture that the very little attention paid to the LTE-CSI fingerprinting in the literature is mainly caused by the difficulty of acquiring the LTE CSI. For Wi-Fi systems the CSI can be easily extracted by, e.g., the Intel 5300 network interface card [4], [8]. For LTE systems, although the RSRP and RSRQ can be readily obtained, e.g., via the application program interface (API) of some mobile phones, off-theshelf equipment for CSI acquisition is still unavailable.…”