Determining Commercial Parking Vacancies Employing Multiple WiFiRSSI Fingerprinting Method

Magsino, Elmer R.; Barrameda, Juan Miguel Carlo; Puno, Andrei; Ong, Spencer; Siapco, Cyrill; Vibal, Jolo

doi:10.3390/jsan12020022

Cited by 4 publications

(3 citation statements)

References 29 publications

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“…With the different settings of the spatial facilities of the building structure, various physical environmental factors have an impact on the RSSI value during the WIFI signal propagation. Therefore, in the same space, each RSSI value of each location is different, and the location fingerprint positioning method takes advantage of this feature to use each different RSSI value of each location to represent the RSSI database of different locations, which is divided into offline stage and online stage according to the operating mechanism [47].…”

Section: Wifi Fingerprint Database Positioning Technologymentioning

confidence: 99%

Research on the Car Searching System in the Multi-Storey Garage with the RSSI Indoor Locating Based on Neural Network

Ma,

Wang,

Zhu

et al. 2024

Electronics

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To solve the problem of reverse car searching in intelligent multi-story garages or parking lots, the reverse car searching method based on the intelligent garage of the PC client and mobile client APP was studied, and the interface design and function development of the system’s PC and mobile client APP were carried out. YOLOv5 network and LPRNet network were used for license plate location and recognition to realize parking and entry detection. The indoor pedestrian location method based on RSSI fingerprint signal fusion BPNet network and KNN algorithm was studied, and the location accuracy within 2.5 m was found to be 100%. The research on the A* algorithm based on spatial accessibility was conducted to realize the reverse car search function. The research results indicate that the guidance of the vehicle finding path can be completed while the number of invalid search nodes for the example maps was reduced by more than 55.0%, and the operating efficiency of the algorithm increased to 28.5%.

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Section: Wifi Fingerprint Database Positioning Technologymentioning

confidence: 99%

Research on the Car Searching System in the Multi-Storey Garage with the RSSI Indoor Locating Based on Neural Network

Ma,

Wang,

Zhu

et al. 2024

Electronics

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“…Numerous solutions have been developed and presented in literature to detect the robot's position in real-time applications [3]. Radio frequency based systems is widely used for positioning and tracking purposes [4], using technologies such as WIFI [5] and Bluetooth [6]. Radio frequency identification (RFID) is one of the popular solutions used for localization and tracking applications.…”

Section: Introductionmentioning

confidence: 99%

Closed-Loop Optical Tracking of a Micro-Conveyor over a Smart Surface

Malak,

Hajjar,

Dupont

et al. 2024

JSAN

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In this work, a closed loop control system is developed to optically localize and track micro-robots with high precision. These micro-robots (i.e., micro-conveyors) are in motion simultaneously across a smart surface.The developed method’s primary objectives are to optimize their trajectories, avoid collisions between them, and control their position with micrometric resolution. This article presents and characterizes the tracking of a single micro-conveyor, and the method works similarly when multiple micro-robots move over the surface. Our tracking method starts with a scanning phase, where a 2D steering mirror, placed above the smart surface, reflects a laser beam toward the conveying surface seeking for the target. Localization occurs when this light beam reaches the micro-conveyor. By adding a retro-reflective element, that reflects the light in the same direction of the the incident light, onto the surface of the micro-conveyor, the light will be reflected towards a photodetector. Depending on the feedback from the photodetector, the steering mirror rotates to track the trajectory of the micro-conveyor. The tip-tilt angular values of the steering mirror allows the micro-conveyor position to be obtained via calibrated localization system. The aim of this work is to regulate the micro-conveyor, within a closed-loop control system, to reduce the positional error between the actual and desired position. The actual position value is measured in real-time application using our developed optical sensor. Results for tracking in the x-and y-axis have validated the proposed method, with an average tracking error less than 30 µm within a range 150 mm × 150 mm.

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“…Although global navigation satellite systems (GNSS, i.e., BDS and GPS) satisfy users' high-accuracy location service needs in open environments [1], it is difficult for GNSS to provide location services [2] in satellite signals blocked areas, such as indoor environments [3]. To this end, researchers studied indoor positioning methods based on infrared [4], ultrasonic [5], Bluetooth [6], Wi-Fi [7,8], and PDR [9,10]. These techniques have obvious weaknesses of low accuracy and short distance.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Evaluations of NLOS and Linearization Errors on UWB Positioning

Гао

et al. 2023

Applied Sciences

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Currently, ultra-wide band (UWB) is adopted as a useful high-accuracy positioning technique in satellite-blocked areas. However, UWB’s positioning performance would be limited significantly because of non-line of sight (NLOS) errors. Additionally, the truncation errors in these linearization-based adjustments such as least squares (LS) and extended Kalman filter (EKF) would also visibly degrade UWB positioning accuracy. To overcome the impacts of NLOS errors and truncation errors, this paper introduced a robust-theory-based particle filter (RPF) into UWB positioning. In such a method, the IGG-III model and PF were adopted to limit the impacts of NLOS errors and truncation errors, respectively, by introducing a weight inflation factor and particle group. For comparison, the Bancroft, LS, EKF, unscented Kalman filter (UKF), cubature Kalman filter (CKF), PF, and RPF were also presented. Here, the influences of truncation errors were analyzed by comparing the results based on LS and EKF with those calculated by UKF, CKF, and PF. The impacts of NLOS errors were evaluated by making a comparison between the results of PF and RPF. Results based on a set of simulated UWB data and a group of experiment UWB data demonstrated that the RPF can significantly avoid the positioning errors caused by both truncation errors and NLOS errors. In general, position improvements percentages of 57.2%, 52.7%, 39.6%, 38.2%, 26.6%, and 20.4% can be obtained by RPF compared to those calculated by Bancroft, LS, EKF, UKF, CKF, and PF, respectively. As a comparison, the truncation error would lead to about 8.1%, 10.1%, and 33.2% accuracy decrease in the north, east, and vertical directions on average. Such accuracy-decrease rates caused by NLOS were 6.1%, 5.2%, and 25%.

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Determining Commercial Parking Vacancies Employing Multiple WiFiRSSI Fingerprinting Method

Cited by 4 publications

References 29 publications

Research on the Car Searching System in the Multi-Storey Garage with the RSSI Indoor Locating Based on Neural Network

Research on the Car Searching System in the Multi-Storey Garage with the RSSI Indoor Locating Based on Neural Network

Closed-Loop Optical Tracking of a Micro-Conveyor over a Smart Surface

Comprehensive Evaluations of NLOS and Linearization Errors on UWB Positioning

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