Joint Coordinate Optimization in Fingerprint-Based Indoor Positioning

Nabati, Mohammad; Ghorashi, Seyed Ali; Shahbazian, Reza

doi:10.1109/lcomm.2020.3047352

Cited by 15 publications

(13 citation statements)

References 12 publications

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“…In this section, we evaluate the performance of the proposed MC-ELM-based DFL method in typical indoor environments, by comparing with selected state-of-the-art methods, including ELM, kernel ELM (K-ELM), support vector machine (SVM) [24], Kullback-Leibler (KL) divergence [25], 2D-GPR [26], random forest (RF) [27], online sequential ELM (OS-ELM) [23], FOS-ELM [28] and DU-OS-ELM [29].…”

Section: Performance Evaluationmentioning

confidence: 99%

Online Spatiotemporal Modeling for Robust and Lightweight Device-Free Localization in Nonstationary Environments

Zhang

Xiao

et al. 2023

IEEE Trans. Ind. Inf.

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Recent advances in WiFi-based device-free localization (DFL) mainly focus on stationary scenarios, and ignore the environmental dynamics, hindering the large-scale implementation of the DFL technique. In order to enhance the localization performance in nonstationary environments, in this paper, a novel multidomain collaborative extreme learning machine (MC-ELM)-based DFL framework is proposed. Specifically, the whole environment is first divided into several sub-domains depending on the distributions of the collected data using clustering algorithm, and a corresponding number of local DFL models are then built to represent these sub-domains separately. Finally, a global DFL model is achieved through seamlessly integrating all the local DFL models in a global optimization manner. The created MC-ELM-based DFL model also can be incrementally updated with sequentially coming data without retraining to track the environmental dynamics. Extensive experiments in several indoor environments demonstrate the robustness and generalization of the proposed MC-ELM-based DFL framework.

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Section: Performance Evaluationmentioning

confidence: 99%

Online Spatiotemporal Modeling for Robust and Lightweight Device-Free Localization in Nonstationary Environments

Zhang

Xiao

et al. 2023

IEEE Trans. Ind. Inf.

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“…Benefiting from the robustness of the RSS tread to the device diversity, an integrated ranging algorithm combining the Wi-Fi RTT and the RSS was proposed to enhance the localization performance. In recent years, many feature matching-based indoor localization solutions have been introduced [27]. To estimate the unmanned ground vehicle (UGV)'s location on a pre-built semantic map, Yan et al [20] proposed a novel localization method based on the similarity between local and global nodes.…”

Section: B Indoor Localizationmentioning

confidence: 99%

Multi-Sensor Fusion-Based Indoor Single-Track Semantic Map Construction and Localization

Chai

2023

IEEE Sensors J.

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The advancement of smartphones has been a prerequisite for multi-sensor fusion-based indoor mapping due to their high penetration and low cost. This paper constructs a sparse single-track semantic map by fusing semantic landmarks, Wi-Fi landmarks, and waypoints, which are scene-representable and navigational in unknown indoor environments. The pedestrian dead reckoning (PDR)-aided visual-inertial simultaneous localization and mapping (PDR-aided VI-SLAM) method uses the PDR velocity as an external observation to constrain the preintegration of inertial measurement unit (IMU) measurements. The semantic objects detected by the YOLO V4 are prefiltered by the proposed semantic object filtering algorithm before semantics matching. The centroid of feature points clustered by the density-based spatial clustering of applications with noise (DBSCAN) algorithm characterizes the location of a semantic landmark relative to the waypoints estimated by the PDR-aided VI-SLAM. To enhance the stability of Wi-Fi landmarks, Wi-Fi signals are processed by the proposed sliding window-based Wi-Fi fusion algorithm. By using waypoints as an intermediate quantity, the association relationships between waypoints, semantic landmarks, and Wi-Fi landmarks are established. After that, a lightweight single-track semantic map is constructed. A landmark matching-based localization method is proposed to evaluate the similarity between the local map and the constructed scene map to infer the location of a pedestrian in the scene map. Experiments are conducted in the office building and mall scenes under various illumination conditions and semantic density. Results demonstrate the high quality of the single-track semantic map and high-precision localization of a local map in the constructed scene map.

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“…Available services that use the global positioning system can be employed for most of the application requirements. However, such methods do not provide high accuracy performance in indoor environments due to the limited coverage of satellites and non-line-of-sight (NLOS) errors [3,4]. Satellite-based methods regularly use ranging information that can be obtained from techniques such as time of arrival (TOA), angle of arrival (AOA), and received signal strength (RSS) [2].…”

Section: Introductionmentioning

confidence: 99%

“…in training data can be considered to have a joint Gaussian distribution. For simplicity and without loss of generality, we assume a zero-mean Gaussian process, and therefore, the vector f = [ f1 , f2 , … , f Ñ ] 𝑇 has the following distribution [32] f ∼(𝟎, 𝐂), (4) where 𝐂 ∈ R Ñ× Ñ is the covariance matrix of the training data. Each element of this matrix demonstrates the similarity between two elements of the vector f. This similarity can be captured by different kernel functions such as linear, squared exponential, and Noise kernel, as depicted in Table 1 [32].…”

mentioning

confidence: 99%