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Sensors and Techniques for Indoor Positioning and Localization

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (20 August 2024) | Viewed by 13825

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Dr. Antonio Moschitta

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Department of Engineering, University of Perugia, 06125 Perugia, Italy
Interests: indoor and short-range positioning; statistical signal processing; battery characterization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Accurate indoor positioning is an interesting topic whose applications have impacts on various fields, that, depending on the targeted accuracy, can include line traceability, telemanipulation, telemedicine, and drone control. By overcoming the limitations of outdoor GNSS, indoor positioning techniques permit seamless indoor/outdoor positioning, and can be a strong enabler for IoT and Industry 4.0 applications. As such, various approaches and methods have been proposed in the literature, with no definite solution being competitive for most conceivable scenarios. At the measurement level, various quantities can be measured, including inertial readings, ultrasound waves, static or AC magnetic fields, radiofrequency EM waves, and image or video recordings. These measurements can be combined using various approaches, such as fingerprinting, sensor fusion between multiple measurements, and, more recently, artificial intelligence. Additional degrees of freedom can be exploited at the system design level, where a specific infrastructure can be deployed, based either on specific proprietary technology or on off-the-shelf devices, such as ultrawide-band (UWB) transceivers. Depending on the targeted accuracy and budget, additional tradeoffs can be realized by using pre-existing RF infrastructures, such as WiFi or Blueooth networks. To this end, new opportunities are offered by modern consumer devices such as tablets and smartphones, which typically have embedded video cameras, multiple sensors, wireless networking capabilities, and powerful processors. Hence, this Special Issue welcomes innovative contributions, advancing the state of the art in indoor or short-range positioning with respect to sensors, measurements, estimation techniques, systems’ architectures, devices, and applications.

Dr. Antonio Moschitta
Guest Editor

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Keywords

indoor positioning
tracking
sensors
smart sensors
algorithms
measurement
accuracy
estimation
artificial intelligence
Internet of Things

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Published Papers (11 papers)

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19 pages, 5345 KiB

Open AccessArticle

Accurate Low Complexity Quadrature Angular Diversity Aperture Receiver for Visible Light Positioning

by Stefanie Cincotta, Adrian Neild, Kristian Helmerson, Michael Zenere and Jean Armstrong

Sensors 2024, 24(18), 6006; https://doi.org/10.3390/s24186006 - 17 Sep 2024

Viewed by 266

Abstract

Despite the many potential applications of an accurate indoor positioning system (IPS), no universal, readily available system exists. Much of the IPS research to date has been based on the use of radio transmitters as positioning beacons. Visible light positioning (VLP) instead uses LED lights as beacons. Either cameras or photodiodes (PDs) can be used as VLP receivers, and position estimates are usually based on either the angle of arrival (AOA) or the strength of the received signal. Research on the use of AOA with photodiode receivers has so far been limited by the lack of a suitable compact receiver. The quadrature angular diversity aperture receiver (QADA) can fill this gap. In this paper, we describe a new QADA design that uses only three readily available parts: a quadrant photodiode, a 3D-printed aperture, and a programmable system on a chip (PSoC). Extensive experimental results demonstrate that this design provides accurate AOA estimates within a room-sized test chamber. The flexibility and programmability of the PSoC mean that other sensors can be supported by the same PSoC. This has the potential to allow the AOA estimates from the QADA to be combined with information from other sensors to form future powerful sensor-fusion systems requiring only one beacon. Full article

(This article belongs to the Special Issue Sensors and Techniques for Indoor Positioning and Localization)

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12 pages, 2558 KiB

Open AccessArticle

Wi-Fi Fingerprint Indoor Localization by Semi-Supervised Generative Adversarial Network

by Jaehyun Yoo

Sensors 2024, 24(17), 5698; https://doi.org/10.3390/s24175698 - 1 Sep 2024

Viewed by 390

Abstract

Wi-Fi fingerprint indoor localization uses Wi-Fi signal strength measurements obtained from a number of access points. This method needs manual data collection across a positioning area and an annotation process to label locations to the measurement sets. To reduce the cost and effort, this paper proposes a Wi-Fi Semi-Supervised Generative Adversarial Network (SSGAN), which produces artificial but realistic trainable fingerprint data. The Wi-Fi SSGAN is based on a deep learning, which is extended from GAN in a semi-supervised learning manner. It is designed to create location-labeled Wi-Fi fingerprint data, which is different to unlabeled data generation by a normal GAN. Also, the proposed Wi-Fi SSGAN network includes a positioning model, so it does not need a external positioning method. When the Wi-Fi SSGAN is applied to a multi-story landmark localization, the experimental results demonstrate a 35% more accurate performance in comparison to a standard supervised deep neural network. Full article

(This article belongs to the Special Issue Sensors and Techniques for Indoor Positioning and Localization)

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20 pages, 3915 KiB

Open AccessArticle

A Study of Improved Two-Stage Dual-Conv Coordinate Attention Model for Sound Event Detection and Localization

by Guorong Chen, Yuan Yu, Yuan Qiao, Junliang Yang, Chongling Du, Zhang Qian and Xiao Huang

Sensors 2024, 24(16), 5336; https://doi.org/10.3390/s24165336 - 18 Aug 2024

Viewed by 431

Abstract

Sound Event Detection and Localization (SELD) is a comprehensive task that aims to solve the subtasks of Sound Event Detection (SED) and Sound Source Localization (SSL) simultaneously. The task of SELD lies in the need to solve both sound recognition and spatial localization problems, and different categories of sound events may overlap in time and space, making it more difficult for the model to distinguish between different events occurring at the same time and to locate the sound source. In this study, the Dual-conv Coordinate Attention Module (DCAM) combines dual convolutional blocks and Coordinate Attention, and based on this, the network architecture based on the two-stage strategy is improved to form the SELD-oriented Two-Stage Dual-conv Coordinate Attention Model (TDCAM) for SELD. TDCAM draws on the concepts of Visual Geometry Group (VGG) networks and Coordinate Attention to effectively capture critical local information by focusing on the coordinate space information of the feature map and dealing with the relationship between the feature map channels to enhance the feature selection capability of the model. To address the limitation of a single-layer Bi-directional Gated Recurrent Unit (Bi-GRU) in the two-stage network in terms of timing processing, we add to the structure of the two-layer Bi-GRU and introduce the data enhancement techniques of the frequency mask and time mask to improve the modeling and generalization ability of the model for timing features. Through experimental validation on the TAU Spatial Sound Events 2019 development dataset, our approach significantly improves the performance of SELD compared to the two-stage network baseline model. Furthermore, the effectiveness of DCAM and the two-layer Bi-GRU structure is confirmed by performing ablation experiments. Full article

(This article belongs to the Special Issue Sensors and Techniques for Indoor Positioning and Localization)

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24 pages, 7202 KiB

Open AccessArticle

A WKNN Indoor Fingerprint Localization Technique Based on Improved Discrimination Capability of RSS Similarity

by Baofeng Wang, Qinghai Li, Jia Liu, Zumin Wang, Qiudong Yu and Rui Liang

Sensors 2024, 24(14), 4586; https://doi.org/10.3390/s24144586 - 15 Jul 2024

Viewed by 636

Abstract

There are various indoor fingerprint localization techniques utilizing the similarity of received signal strength (RSS) to discriminate the similarity of positions. However, due to the varied states of different wireless access points (APs), each AP’s contribution to RSS similarity varies, which affects the accuracy of localization. In our study, we analyzed several critical causes that affect APs’ contribution, including APs’ health states and APs’ positions. Inspired by these insights, for a large-scale indoor space with ubiquitous APs, a threshold was set for all sample RSS to eliminate the abnormal APs dynamically, a correction quantity for each RSS was provided by the distance between the AP and the sample position to emphasize closer APs, and a priority weight was designed by RSS differences (RSSD) to further optimize the capability of fingerprint distances (FDs, the Euclidean distance of RSS) to discriminate physical distance (PDs, the Euclidean distance of positions). Integrating the above policies for the classical WKNN algorithm, a new indoor fingerprint localization technique is redefined, referred to as FDs’ discrimination capability improvement WKNN (FDDC-WKNN). Our simulation results showed that the correlation and consistency between FDs and PDs are well improved, with the strong correlation increasing from 0 to 76% and the high consistency increasing from 26% to 99%, which confirms that the proposed policies can greatly enhance the discrimination capabilities of RSS similarity. We also found that abnormal APs can cause significant impact on FDs discrimination capability. Further, by implementing the FDDC-WKNN algorithm in experiments, we obtained the optimal K value in both the simulation scene and real library scene, under which the mean errors have been reduced from 2.2732 m to 1.2290 m and from 4.0489 m to 2.4320 m, respectively. In addition, compared to not using the FDDC-WKNN, the cumulative distribution function (CDF) of the localization errors curve converged faster and the error fluctuation was smaller, which demonstrates the FDDC-WKNN having stronger robustness and more stable localization performance. Full article

(This article belongs to the Special Issue Sensors and Techniques for Indoor Positioning and Localization)

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19 pages, 480 KiB

Open AccessArticle

An Accurate Anchor-Free Contextual Received Signal Strength Approach Localization in a Wireless Sensor Network

by Nour Zaarour, Nadir Hakem and Nahi Kandil

Sensors 2024, 24(4), 1210; https://doi.org/10.3390/s24041210 - 14 Feb 2024

Cited by 1 | Viewed by 896

Abstract

Sensor localization remains a crucial function within the context of wireless sensor networks (WSNs) and is a delicate concern that has attracted many researchers’ attention. Undoubtedly, a good distance estimation between different wireless sensors allows us to estimate their accurate locations in the network well. In this article, we present a simple but very effective anchor-free localization scheme for wireless sensor networks called the contextual received signal strength approach (CRSSA) localization scheme. We use the received signal strength (RSS) values and the contextual network connectivity within an anchor-free WSN. We present and thoroughly analyze a novel joint estimation methodology for determining the range, path loss exponent (PLE), and inter-node distances in a composite fading model that addresses small-scale multipath fading and large-scale path loss shadowing effects. We formulate analytical expressions for key parameters, the node’s communication range and the PLE value, as functions of the sensor’s number, the network’s connectivity, and the network density. Once these parameters are estimated, we estimate the inter-node distances and the positions of nodes, with relatively high accuracy, based on the assumed propagation model in a two-dimensional anchor-free WSN. The effectiveness of the CRSSA is evaluated through extensive simulations assuring its estimation accuracy in anchor-free localization. Full article

(This article belongs to the Special Issue Sensors and Techniques for Indoor Positioning and Localization)

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16 pages, 18639 KiB

Open AccessArticle

Wand-Based Calibration of Unsynchronized Multiple Cameras for 3D Localization

by Sujie Zhang and Qiang Fu

Sensors 2024, 24(1), 284; https://doi.org/10.3390/s24010284 - 3 Jan 2024

Viewed by 1135

Abstract

Three-dimensional (3D) localization plays an important role in visual sensor networks. However, the frame rate and flexibility of the existing vision-based localization systems are limited by using synchronized multiple cameras. For such a purpose, this paper focuses on developing an indoor 3D localization system based on unsynchronized multiple cameras. First of all, we propose a calibration method for unsynchronized perspective/fish-eye cameras based on timestamp matching and pixel fitting by using a wand under general motions. With the multi-camera calibration result, we then designed a localization method for the unsynchronized multi-camera system based on the extended Kalman filter (EKF). Finally, extensive experiments were conducted to demonstrate the effectiveness of the established 3D localization system. The obtained results provided valuable insights into the camera calibration and 3D localization of unsynchronized multiple cameras in visual sensor networks. Full article

(This article belongs to the Special Issue Sensors and Techniques for Indoor Positioning and Localization)

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24 pages, 4996 KiB

Open AccessArticle

Design of Acoustic Signal for Positioning of Smart Devices

by Veronika Hromadova, Peter Brida and Juraj Machaj

Sensors 2023, 23(18), 7852; https://doi.org/10.3390/s23187852 - 13 Sep 2023

Cited by 1 | Viewed by 1282

Abstract

This paper addresses the limitations of using smartphones in innovative localization systems based on audio signal processing, particularly in the frequency range of 18–22 kHz, due to the lack of technical specifications and noise characterization. We present a comprehensive study on signal design and performance analysis for acoustic communication in air ducts, focusing on signal propagation in indoor environments considering room acoustics and signal behavior. The research aims to determine optimal parameters, including the frequency band, signal types, signal length, pause duration, and sampling frequency, for the efficient transmission and reception of acoustic signals for commercial off-the-shelf (COST) devices. Factors like inter-symbol interference (ISI) and multiple access interference (MAI) that affect signal detection accuracy are considered. The measurements help define the frequency spectrum for common devices like smartphones, speakers, and sound cards. We propose a custom signal with specific properties and reasons for their selection, setting the signal length at 50 ms and a pause time of 5 ms to minimize overlap and interference between consecutive signals. The sampling rate is fixed at 48 kHz to maintain the required resolution for distinguishing individual signals in correlation-based signal processing. Full article

(This article belongs to the Special Issue Sensors and Techniques for Indoor Positioning and Localization)

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32 pages, 6791 KiB

Open AccessArticle

A Hybrid Indoor Positioning System Based on Visible Light Communication and Bluetooth RSS Trilateration

by Lamya Albraheem and Sarah Alawad

Sensors 2023, 23(16), 7199; https://doi.org/10.3390/s23167199 - 16 Aug 2023

Cited by 6 | Viewed by 1744

Abstract

Indoor positioning has become an attractive research topic because of the drawbacks of the global navigation satellite system (GNSS), which cannot detect accurate locations within indoor areas. Radio-based positioning technologies are one major category of indoor positioning systems. Another major category consists of visible light communication-based solutions, as they have become a revolutionary technology for indoor positioning in recent years. The proposed study intends to make use of both technologies by creating a hybrid indoor positioning system that uses VLC and Bluetooth together. The system first collects the initial location information based on VLC proximity, then collects the strongest Bluetooth signals to determine the receiver’s location using Bluetooth RSS (received signal strength) trilateration. This has been inspired by the fact that there have not been any studies that make use of both technologies with the same positioning algorithm, which can lead to pretty high accuracy of up to 0.03 m. Full article

(This article belongs to the Special Issue Sensors and Techniques for Indoor Positioning and Localization)

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19 pages, 2623 KiB

Open AccessArticle

Indoor Visible Light Positioning System Based on Point Classification Using Artificial Intelligence Algorithms

by Qianqian Long, Junyi Zhang, Lu Cao and Wenrui Wang

Sensors 2023, 23(11), 5224; https://doi.org/10.3390/s23115224 - 31 May 2023

Cited by 3 | Viewed by 1640

Abstract

In RSSI-based indoor visible light positioning systems, when only RSSI is used for trilateral positioning, the receiver height needs to be known to calculate distance. Meanwhile, the positioning accuracy is greatly affected by multi-path effect interference, with the influence of the multi-path effect varying across different areas of the room. If only one single processing is used for positioning, the positioning error in the edge area will increase sharply. In order to address these problems, this paper proposes a new positioning scheme, which uses artificial intelligence algorithms for point classification. Firstly, height estimation is performed according to the received power data structure from different LEDs, which effectively extends the traditional RSSI trilateral positioning from 2D to 3D. The location points in the room are then divided into three categories: ordinary points, edge points and blind points, and corresponding models are used to process different types of points, respectively, to reduce the influence of the multi-path effect. Next, processed received power data are used in the trilateral positioning method for calculating the location point coordinates, and to reduce the room edge corner positioning error, so as to reduce the indoor average positioning error. Finally, a complete system is built in an experimental simulation to verify the effectiveness of the proposed schemes, which are shown to achieve centimeter-level positioning accuracy. Full article

(This article belongs to the Special Issue Sensors and Techniques for Indoor Positioning and Localization)

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18 pages, 3221 KiB

Open AccessArticle

UWB Localization Based on Improved Robust Adaptive Cubature Kalman Filter

by Jiaqi Dong, Zengzeng Lian, Jingcheng Xu and Zhe Yue

Sensors 2023, 23(5), 2669; https://doi.org/10.3390/s23052669 - 28 Feb 2023

Cited by 11 | Viewed by 1830

Abstract

Aiming at the problems of Non-Line-of-Sight (NLOS) observation errors and inaccurate kinematic model in ultra-wideband (UWB) systems, this paper proposed an improved robust adaptive cubature Kalman filter (IRACKF). Robust and adaptive filtering can weaken the influence of observed outliers and kinematic model errors on filtering, respectively. However, their application conditions are different, and improper use may reduce positioning accuracy. Therefore, this paper designed a sliding window recognition scheme based on polynomial fitting, which can process the observation data in real-time to identify error types. Simulation and experimental results indicate that compared to the robust CKF, adaptive CKF, and robust adaptive CKF, the IRACKF algorithm reduces the position error by 38.0%, 45.1%, and 25.3%, respectively. The proposed IRACKF algorithm significantly improves the positioning accuracy and stability of the UWB system. Full article

(This article belongs to the Special Issue Sensors and Techniques for Indoor Positioning and Localization)

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Review

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26 pages, 853 KiB

Open AccessReview

A Survey of the Performance-Limiting Factors of a 2-Dimensional RSS Fingerprinting-Based Indoor Wireless Localization System

by Abdulmalik Shehu Yaro, Filip Maly and Pavel Prazak

Sensors 2023, 23(5), 2545; https://doi.org/10.3390/s23052545 - 24 Feb 2023

Cited by 19 | Viewed by 2424

Abstract

A receive signal strength (RSS) fingerprinting-based indoor wireless localization system (I-WLS) uses a localization machine learning (ML) algorithm to estimate the location of an indoor user using RSS measurements as the position-dependent signal parameter (PDSP). There are two stages in the system’s localization process: the offline phase and the online phase. The offline phase starts with the collection and generation of RSS measurement vectors from radio frequency (RF) signals received at fixed reference locations, followed by the construction of an RSS radio map. In the online phase, the instantaneous location of an indoor user is found by searching the RSS-based radio map for a reference location whose RSS measurement vector corresponds to the user’s instantaneously acquired RSS measurements. The performance of the system depends on a number of factors that are present in both the online and offline stages of the localization process. This survey identifies these factors and examines how they impact the overall performance of the 2-dimensional (2-D) RSS fingerprinting-based I-WLS. The effects of these factors are discussed, as well as previous researchers’ suggestions for minimizing or mitigating them and future research trends in RSS fingerprinting-based I-WLS. Full article

(This article belongs to the Special Issue Sensors and Techniques for Indoor Positioning and Localization)

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