Implementation and test of an RSSI-based indoor target localization system: Human movement effects on the accuracy

Booranawong, Apidet; Sengchuai, Kiattisak; Jindapetch, Nattha

doi:10.1016/j.measurement.2018.10.031

Cited by 33 publications

(18 citation statements)

References 41 publications

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“…The result showed that people's presence in the Line of Sight (LOS) between the AP and the Mobile Device (MD) decreased the RSSI by 2 dBm to 5 dBm. This decline in RSSI could result in a position error of more than 2 m. Hence, an adaptive IPS that can adapt to environmental changes, including user orientation [89] and people's presence [90], [91], is needed to improve the accuracy of IPS.…”

Section: Future Developmentmentioning

confidence: 99%

A Review of Hybrid Indoor Positioning Systems Employing WLAN Fingerprinting and Image Processing

Firdaus

Ahmad

Sahibuddin

2020

Int. j. electr. comput. eng. syst. (Online)

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Location-based services (LBS) are a significant permissive technology. One of the main components in indoor LBS is the indoor positioning system (IPS). IPS utilizes many existing technologies such as radio frequency, images, acoustic signals, as well as magnetic sensors, thermal sensors, optical sensors, and other sensors that are usually installed in a mobile device. The radio frequency technologies used in IPS are WLAN, Bluetooth, Zig Bee, RFID, frequency modulation, and ultra-wideband. This paper explores studies that have combined WLAN fingerprinting and image processing to build an IPS. The studies on combined WLAN fingerprinting and image processing techniques are divided based on the methods used. The first part explains the studies that have used WLAN fingerprinting to support image positioning. The second part examines works that have used image processing to support WLAN fingerprinting positioning. Then, image processing and WLAN fingerprinting are used in combination to build IPS in the third part. A new concept is proposed at the end for the future development of indoor positioning models based on WLAN fingerprinting and supported by image processing to solve the effect of people presence around users and the user orientation problem.

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Section: Future Developmentmentioning

confidence: 99%

A Review of Hybrid Indoor Positioning Systems Employing WLAN Fingerprinting and Image Processing

Firdaus

Ahmad

Sahibuddin

2020

Int. j. electr. comput. eng. syst. (Online)

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“…The human activity around MS affects WLAN signal strength [26]. One past study observed a relationship between the fluctuations in RSSI and people activity within a WLAN coverage area.…”

Section: People Presence Effectmentioning

confidence: 99%

“…Booranawong et al [26] proposed well-known filtering methods (i.e., the moving average and the exponentially weighted moving average filters) and the span thresholding filter to reduce RSSI variations and obtained an estimated position error because of human movement. Unfortunately, the size of the room used for the test was quite small and the error was still above 1 m.…”

Section: People Presence Effectmentioning

confidence: 99%

Accurate Indoor-Positioning Model Based on People Effect and Ray-Tracing Propagation

Firdaus

Ahmad

Sahibuddin

2019

Sensors

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Wireless local area networks (WLAN)-fingerprinting has been highlighted as the preferred technology for indoor positioning due to its accurate positioning and minimal infrastructure cost. However, its accuracy is highly influenced by obstacles that cause fluctuation in the signal strength. Many researchers have modeled static obstacles such as walls and ceilings, but few studies have modeled the people’s presence effect (PPE), although the human body has a great impact on signal strength. Therefore, PPE must be addressed to obtain accurate positioning results. Previous research has proposed a model to address this issue, but these studies only considered the direct path signal between the transmitter and the receiver whereas multipath effects such as reflection also have a significant influence on indoor signal propagation. This research proposes an accurate indoor-positioning model by considering people’s presence and multipath using ray-tracing, we call it (AIRY). This study proposed two solutions to construct AIRY: an automatic radio map using ray tracing and a constant of people’s effect for the received signal strength indicator (RSSI) adaptation. The proposed model was simulated using MATLAB software and tested at Level 3, Menara Razak, Universiti Teknologi Malaysia. A K-nearest-neighbor (KNN) algorithm was used to define a position. The initial accuracy was 2.04 m, which then reduced to 0.57 m after people’s presence and multipath effects were considered.

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“…However, the RANSAC algorithm is not applicable to an indoor tracking system since the actual distance between such devices is unknown. The work presented in [13] by Booranawong et al presents an analysis between three filter algorithms: Window Moving Average Filter (WMAF), Exponentially Weighted Moving Average Filter (EWMAF) and Span Thresholding Filter (STF). These algorithms are suitable for tracking since the current position estimate is obtained using previous RSSI values, i.e., the position estimates are determined using actual RSSI samples and not by applying a signal propagation model.…”

Section: Factors Affecting Wi-fi Signalmentioning

confidence: 99%

“…Due to the noisy nature of the Wi-Fi signal, which is reflected in continuous variations of RSSI value, RSSI-based indoor tracking systems can considerably vary. To overcome this problem, a smoothing algorithm can be used in order to reduce the RSSI value noise [13].…”

Section: Rssi Smoothingmentioning

confidence: 99%

Calibration of Wi-Fi-Based Indoor Tracking Systems for Android-Based Smartphones

2019

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With the growing development of smartphones equipped with Wi-Fi technology and the need of inexpensive indoor location systems, many researchers are focusing their efforts on the development of Wi-Fi-based indoor localization methods. However, due to the difficulties in characterizing the Wi-Fi radio signal propagation in such environments, the development of universal indoor localization mechanisms is still an open issue. In this paper, we focus on the calibration of Wi-Fi-based indoor tracking systems to be used by smartphones. The primary goal is to build an accurate and robust Wi-Fi signal propagation representation in indoor scenarios.We analyze the suitability of our approach in a smartphone-based indoor tracking system by introducing a novel in-motion calibration methodology using three different signal propagation characterizations supplemented with a particle filter. We compare the results obtained with each one of the three characterization in-motion calibration methodologies and those obtained using a static calibration approach, in a real-world scenario. Based on our experimental results, we show that the use of an in-motion calibration mechanism considerably improves the tracking accuracy.

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Implementation and test of an RSSI-based indoor target localization system: Human movement effects on the accuracy

Cited by 33 publications

References 41 publications

A Review of Hybrid Indoor Positioning Systems Employing WLAN Fingerprinting and Image Processing

A Review of Hybrid Indoor Positioning Systems Employing WLAN Fingerprinting and Image Processing

Accurate Indoor-Positioning Model Based on People Effect and Ray-Tracing Propagation

Calibration of Wi-Fi-Based Indoor Tracking Systems for Android-Based Smartphones

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