RSSI informed phase method for distance calculations

Whitney, Ann; Parker, Johné M.; Engelhard, Mallory; Kratzer, Zach; Fessler, J.T.

doi:10.1109/aim.2015.7222693

Cited by 7 publications

(5 citation statements)

References 13 publications

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“…According to the previous description, there are two main problems to be solved in the chemical warehouse: One is detecting the remaining amount of chemicals, and the other is locating these chemicals. For the first problem, we can use indicators such as the reading frequency, phase value, and RSSI of the tag [14][15][16]. The phase difference and the received signal strength indication reflect the difference between the real received signal and the antenna transmitted signal [17][18][19].…”

Section: Related Workmentioning

confidence: 99%

Intelligent Management of Chemical Warehouses with RFID Systems

Zhao

Xue

2019

Sensors

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At present, most chemical warehouses rely on human management, which is a time-consuming and laborious process. Therefore, it is very meaningful to use radio frequency identification (RFID) systems for the intelligent management of chemicals. Detecting the remaining amount of chemicals is an important process in the management of a chemical warehouse. It helps managers find the chemicals that are going to run out and replenish them in time. However, in a traditional chemical warehouse, managers usually inspect each chemical on the shelf in turn manually, which is a waste of time and labor. Although some solutions using RFID technology have been proposed, they are expensive and difficult to deploy in a real environment. In order to solve this problem, we propose an intelligent system called the RF-Detector in this paper, which combines robotics and RFID technology. An RFID reader and an antenna are installed on the robot, which achieves automatic scanning of the chemicals. The RF-Detector can achieve two functions: One function is to detect the remaining amount of chemicals using the changes in received signal strength indication (RSSI) and read rate, and the other is to locate chemicals using the phase curve, so that managers can quickly find the chemicals with an insufficient amount remaining. In this paper we implement the RF-Detector and evaluate its performance. The experimental results show that the RF-Detector achieves about 93% detection accuracy and 92% positioning accuracy for chemicals.

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Section: Related Workmentioning

confidence: 99%

Intelligent Management of Chemical Warehouses with RFID Systems

Zhao

Xue

2019

Sensors

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“…The positioning of both RFID tag and reader have been discussed in different studies, and there have been many solutions based on various kinds of information such as frequency, phase, and RSSI. Popular RFID localization methods could be divided into three main categories: 1) a time-based method [4], 2) phase-based methods [5][6][7][8], 3) RSSI-based (Received Signal Strength Indicator) methods [9][10][11][12]. The time-based method from a budget perspective is relatively costly, as well as the synchronization requirements, which increase the system complexity.…”

Section: Introductionmentioning

confidence: 99%

Indoor Localization Systems for Passive Uhf Rfid Tag Based on Rssi Radio Map Database

Omer¹,

Ran²,

Tian³

2019

PIER M

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A classification algorithm is applied to UHF radio frequency identification (RFID) system to estimate the indoor position of a passive tag utilizing a received signal strength indicator (RSSI). Passive tag signals are collected by conventional UHF RFID reader antenna located at two different positions. K-NN and curve fitting are used for distance estimation and examined at different frequencies within the range 902-928 MHz to find suitable frequencies which can minimize the average error. In the proposed method, the measured RSSI values are compared with the fingerprint database in different frequencies to find the nearest neighbor. The best accuracy achieved at frequency range 926-928 MHz is 18.3 cm. The monitoring system is composed of a reader and tags under test, which makes the proposed system robust, easy to set up, and with low cost. The limitation of the proposed method is also discussed.

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“…This can be due to the presence of several kinds of noises in the measurements of the signal strength, which makes it difficult to use the signal strength as the only variable to evaluate the distance to a target. Examples can be found in [4], in which the authors employed variance and its relation with RSSI (received signal strength indication) to improve accuracy in distance assessment (at large distances, with a large set of measurements), and in [5], which uses the modulation phase to improve the accuracy at small distances (in very controlled ambient conditions).…”

Section: Introductionmentioning

confidence: 99%

“…In the case of wireless sensor networks (WSN) operating at 2. 5 GHz in open fields at 2 m height at most, models must be capable of operating with a small quantity of measurements and be lightweight to allow fast computing with small resources. Proposed models such as Ray-Tracing [11],…”

Section: Introductionmentioning

confidence: 99%

Implementing a Distance Estimator for a Wildlife Tracking System Based on 802.15.4

et al. 2019

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In this work, a novel distance estimation mechanism using received signal strength indication (RSSI) signals with ZigBee modules is designed, implemented and tested in several scenarios. This estimator was used for a research project focused on a wildlife behavioral classification system deployed in Doñana's National Park. As a supporting feature for that project, this work was implemented for locating animal's collars acting as wireless nodes in order to find those who went outside of the coverage area of the network or that were accidentally detached from animals. This work describes the system architecture and the implementation of a mobile assistant capable of recovering devices located beyond the coverage of the network. The analytical model needed for distance estimation and the signal filtering are described, as well as the difficulties that the researchers must deal when building robust location estimators. This theoretical model was applied to three different scenarios and tested with two validation experiments.Finite-difference time-domain [12], or Artificial Networks [13] are much better when estimating position in terms of accuracy than other models, but, as mentioned above, at the expense of a high computation cost [14]. Other models (e.g., Reference [15]) are well accepted for network deployment of cellular devices (≤1.5 GHz) but not suitable for higher frequencies.The log-normal model, together with the free-space model [16] are some of the most used models operating at 2.5 GHz for being the calculation procedure simple [4] (at distances greater than the length of the antenna and the wavelength) but at the expense of a lower accuracy. One alternative is 2RM (2-Ray Model) [4], which includes this length as one of the parameters to calculate two possible signal paths. An improved version of this model can be found at [17] but the improvements over the log-normal model are only present at d ≥ 4πH rx H tx , which in our case is more than 250 m (when setting the maximum height of the antennas that were used in this work at 160 cm). Further details about the position of the antenna can be found in Section 2.5.This work presents a relative distance estimator for Wireless Sensor Network devices operating at 2.5 GHz using the 802.15.4 standard [18], on which the target device is a wildlife tracking and behavior classifying device that is attached to an animal by means of a collar. The distance estimator is used to locate collars belonging to animals that are not moving, or collars that have been abandoned by the animals to which they belonged, or even collars that have gone outside of the coverage area of the WSN. On the other hand, the locating device (that uses the distance estimator algorithm) is used by an operator that moves overland with a WSN node that acts as a 802.15.4 wireless packet sniffer. To justify this development, the propagation model and filters used are shown together with two ways to deal with signal measurement variability and uncertainty. In Section 3, we present the data obtained fro...

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RSSI informed phase method for distance calculations

Cited by 7 publications

References 13 publications

Intelligent Management of Chemical Warehouses with RFID Systems

Intelligent Management of Chemical Warehouses with RFID Systems

Indoor Localization Systems for Passive Uhf Rfid Tag Based on Rssi Radio Map Database

Implementing a Distance Estimator for a Wildlife Tracking System Based on 802.15.4

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