WIPP: Wi-Fi Compass for Indoor Passive Positioning with Decimeter Accuracy

Zhang, Zhenyuan; Tian, Zengshan; Zhou, Mu; Li, Ze; Wu, Zipeng; Jin, Yue

doi:10.3390/app6040108

Cited by 7 publications

(5 citation statements)

References 39 publications

(52 reference statements)

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“…The advantage of the CSI is in additional information on fading channel included in amplitude and phase, while RSSI is only the average value of the received signal and is severely affected by multipath effects. Several groups of researchers used Wi-Fi RSSI/CSI for localization [ 6 , 7 , 8 , 9 ], motion detection [ 10 , 11 ], people counting [ 12 ], activity recognition [ 13 , 14 , 15 , 16 ], and gesture recognition [ 17 , 18 , 19 ]. In the following, the latest and most relevant research work on exploring Wi-Fi RF properties is briefly summarized.…”

Section: Related Workmentioning

confidence: 99%

UWB Radio-Based Motion Detection System for Assisted Living

Bregar

Hrovat

Mohorčič

2021

Sensors

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Because of the ageing population, the demand for assisted living solutions that can help prolonging independent living of elderly at their homes with reduced interaction with caregivers is rapidly increasing. One of the most important indicators of the users’ well-being is their motion and mobility inside their homes, used either on its own or as contextual information for other more complex activities such as cooking, housekeeping or maintaining personal hygiene. In monitoring users’ mobility, radio frequency (RF) communication technologies have an advantage over optical motion detectors because of their penetrability through the obstacles, thus covering greater areas with fewer devices. However, as we show in this paper, RF links exhibit large variations depending on channel conditions in operating environment as well as the level and intensity of motion, limiting the performance of the fixed motion detection threshold determined on offline or batch measurement data. Thus, we propose a new algorithm with an online adaptive motion detection threshold that makes use of channel impulse response (CIR) information of the IEEE 802.15.4 ultra-wideband (UWB) radio, which comprises an easy-to-install robust motion detection system. The online adaptive motion detection (OAMD) algorithm uses a sliding window on the last 100 derivatives of power delay profile (PDP) differences and their statistics to set the threshold for motion detection. It takes into account the empirically confirmed observation that motion manifests itself in long-tail samples or outliers of PDP differences’ probability density function. The algorithm determines the online threshold by calculating the statistics on the derivatives of the 100 most recent PDP differences in a sliding window and scales them up in the suitable range for PDP differences with multiplication factors defined by a data-driven process using measurements from representative operating environments. The OAMD algorithm demonstrates great adaptability to various environmental conditions and exceptional performance compared to the offline batch algorithm. A motion detection solution incorporating the proposed highly reliable algorithm can complement and enhance various assisted living technologies to assess user’s well-being over long periods of time, detect critical events and issue warnings or alarms to caregivers.

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Section: Related Workmentioning

confidence: 99%

UWB Radio-Based Motion Detection System for Assisted Living

Bregar

Hrovat

Mohorčič

2021

Sensors

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“…To generalize map learning, suppose that we sample a set of n learned trajectories h 1:n = {h (1) , h (2) , · · · , h (n) }, where each h (·) might have different start and end points and each h 1:n is exploited to obtain P TL in Equation (41). We assume that P TL follows the Gaussian distribution given by…”

Section: Trajectory Learning From a Crowdmentioning

confidence: 99%

Indoor Localization Based on Wi-Fi Received Signal Strength Indicators: Feature Extraction, Mobile Fingerprinting, and Trajectory Learning

Yoo

Park

2019

Applied Sciences

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This paper studies the indoor localization based on Wi-Fi received signal strength indicator (RSSI). In addition to position estimation, this study examines the expansion of applications using Wi-Fi RSSI data sets in three areas: (i) feature extraction, (ii) mobile fingerprinting, and (iii) mapless localization. First, the features of Wi-Fi RSSI observations are extracted with respect to different floor levels and designated landmarks. Second, the mobile fingerprinting method is proposed to allow a trainer to collect training data efficiently, which is faster and more efficient than the conventional static fingerprinting method. Third, in the case of the unknown-map situation, the trajectory learning method is suggested to learn map information using crowdsourced data. All of these parts are interconnected from the feature extraction and mobile fingerprinting to the map learning and the estimation. Based on the experimental results, we observed (i) clearly classified data points by the feature extraction method as regards the floors and landmarks, (ii) efficient mobile fingerprinting compared to conventional static fingerprinting, and (iii) improvement of the positioning accuracy owing to the trajectory learning.Wi-Fi RSSI based indoor localization [1-4] is one of the standard approaches for indoor localization. It is able to utilize the RSSI measurements received from a large number of access points (APs) that are already built in construction. However, the Wi-Fi RSSI as a function of distance between a receiver (smartphone) and a transmitter (wireless AP) is nonlinear and varying due to interference of the indoor environments such as the other radio signals, walls, and obstacles. To address this problem, many machine learning based localization methods [5-8] have been developed, which learn a pattern of the RSSI measurements corresponding locations across the interested positioning area. In addition, due to its unbiased estimation capability, it is likely to be combined with other kinds of localization, such as pedestrian localization using inertial measurement unit (IMU) [9,10], visual localization [11,12], and magnetic sensor-based localization [13,14].In particular, semisupervised learning algorithms have been recently suggested for efficient indoor localization, which reduce the human effort necessary for collecting training data [15][16][17][18][19][20]. For example, for indoor localization, a large amount of unlabeled data can be easily collected by recording only Wi-Fi RSSI measurements without requiring position labels, which can save resources for collection and calibration. By contrast, labeled training data have to be created manually. Adding a large amount of Appl. Sci. 2019, 9, 3930 2 of 21 unlabeled data in the semisupervised learning framework can prevent the decrement in the estimation accuracy that occurs when using only a small amount of labeled data.Given the advantage of semisupervised learning, this study describes (i) feature extraction, (ii) mobile fingerprinting, and (iii) mapless loc...

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“…In this way, sub-meter level localization precision can be achieved. WIPP [ 11 ] reconstructs the CSI matrix to guarantee that the number of direction measurement units is larger than the number of signal paths and uses the affinity propagation clustering algorithm to identify the direct signal path from the target to each Access Point (AP). PILA [ 12 ] uses two-dimensional spatial smoothing to rebuild the CSI matrix and verifies its effectiveness by AoA estimation.…”

Section: Introductionmentioning

confidence: 99%

A High Accuracy Time-Reversal Based WiFi Indoor Localization Approach with a Single Antenna

Zheng

Chen

2018

Sensors

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In this paper, we study the influence of multipath magnitude, bandwidth, and communication link number on the performance of the existing time-reversal (TR) based fingerprinting localization approach and find that the localization accuracy deteriorates with a limited bandwidth. To improve the localization performance, by exploiting two unique location-specified signatures extracted from Channel State Information (CSI), we propose a high accuracy TR fingerprint localization approach, HATRFLA. Furthermore, we employ a density-based spatial clustering algorithm to minimize the storage space of the fingerprint database by adaptively selecting the optimal number of fingerprints for each location. Experimental results confirm that the proposed approach can efficiently mitigate accuracy deterioration caused by a limited bandwidth and consequently, achieve higher accuracy compared with the existing TR localization approach.

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WIPP: Wi-Fi Compass for Indoor Passive Positioning with Decimeter Accuracy

Cited by 7 publications

References 39 publications

UWB Radio-Based Motion Detection System for Assisted Living

UWB Radio-Based Motion Detection System for Assisted Living

Indoor Localization Based on Wi-Fi Received Signal Strength Indicators: Feature Extraction, Mobile Fingerprinting, and Trajectory Learning

A High Accuracy Time-Reversal Based WiFi Indoor Localization Approach with a Single Antenna

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