The effects of interference on the RSSI values of a ZigBee based indoor localization system

Konings, Daniel; Faulkner, Nathaniel; Alam, Fakhrul; Noble, Frazer; Lai, Eseng

doi:10.1109/m2vip.2017.8211460

Cited by 15 publications

(5 citation statements)

References 17 publications

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“…These are mostly limited to indoor environments. Existing solutions use data collected from numerous directional antennas [13] or exploit the frequency hopping capabilities to avoid the interference [14]. A UAV-based wireless sensor network was simulated in [15], and compressed sensing was applied to RSSI for joint estimation of multiple emitters.…”

Section: Localization Of Rf Transmitters: Related Workmentioning

confidence: 99%

Differentiation and Localization of Ground RF Transmitters Through RSSI Measures from a UAV

Teeda,

Moro,

Scazzoli

et al. 2023

Preprint

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<p>This paper explores the experimental localization of single and multiple ground RF transmitters using both traditional localization and machine learning algorithms. For the localization of a single transmitter, the setup is evaluated in two unlicensed frequency bands with and without interference. A threshold approach is proposed to improve accuracy in the presence of interference. To localize multiple transmitters, the RSSI data are divided into clusters by a k-means clustering algorithm and fed into a localization algorithm. These experimental results are preceded by an analysis phase where the UAV flight path and data collection are simulated using the QuaDRiGa channel model.</p>

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Section: Localization Of Rf Transmitters: Related Workmentioning

confidence: 99%

Differentiation and Localization of Ground RF Transmitters Through RSSI Measures from a UAV

Teeda,

Moro,

Scazzoli

et al. 2023

Preprint

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“…First, the operating frequency of the on-board microcontroller caps the energy sampling rate. More specifically, as the RSSI is typically a heavily-filtered real valued version of raw I/Q samples, it undergoes a significant loss of information on the envelope of the acquired signals [17].…”

Section: Motivationmentioning

confidence: 99%

Machine learning based wireless interference estimation in a robotic force-seeking application

Candell¹,

Kashef²,

Montgomery³

et al. 2022

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Wide deployment of wireless communications plays an essential role in the vision of future cyber-physical systems (CPSs), which includes massive transfer of automation information. Many practical considerations of industrial CPSs affect the success of the deployment of industrial wireless networks, and, thus, can inhibit their widespread adoption. These considerations include multi-path propagation, network congestion, and jamming interference. Jamming is of chief concern when wireless is used for mission critical or safety integrated systems. In this paper, an experimental platform consisting of a robot arm depressing a spring mechanism with a wireless force-feedback control algorithm is constructed. The robot applies downward pressure on a spring assembly until a predetermined force is detected and transmitted successfully to the controller under varying levels of sustained interference. Machine learning is used to learn and predict the signal-to-interference level of the communication link solely using position information from an independent vision tracking system. Various supervised learning algorithms are investigated and rated according to their performance.

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“…In [6], it is studied a localization method where three fixed-wing UAVs cooperate for scanning a wide area of 10 × 10 km 2 and looking for an RF emitter with unknown transmit power. On the other hand, articles that tackle RSSI localization in the presence of interference focus mainly on indoor scenarios, with solutions often based on measures taken from multiple directional antennas [12] or on the use of frequency hopping capability to avoid the interference [13].…”

Section: A Related Workmentioning

confidence: 99%

Experimental UAV-Aided RSSI Localization of a Ground RF Emitter in 865 MHz and 2.4 GHz Bands

Moro

Teeda

Scazzoli

et al. 2022

2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)

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Unmanned Aerial Vehicles (UAVs) can be used as low altitude platforms in several applications. In this paper, we propose their use to localize a ground Radio Frequency (RF) emitter by collecting measures of the Received Signal Strength Indicator (RSSI) at different positions. The main contribution of the work consists in the definition of an experimental setup for the simultaneous measures of RSSI and receiver position. The RSSI is measured by an actual transceiver, the Adalm Pluto Software Defined Radio (SDR) development board, programmed with the open-source software GNU Radio. The position is provided by GPS and Inertial Measuring Unit (IMU) sensors on the drone. The measures are acquired in the 865 MHz Short Range Device (SRD) and 2.4 GHz Industrial Scientific Medical (ISM) unlicensed frequency bands. Since the ISM measures can be affected by interference generated by different sources (e.g. Wi-Fi access points and UAV controller), the SRD band is exploited for collecting the RSSI measures with less interference. A maximum likelihood (ML) algorithm is applied to the collected data for estimating the transmitter location. For the considered setup we show that the mean absolute localization error is around 4 m without interference and 5 m with interference. A thresholdbased technique is proposed to improve the accuracy in presence of interference.

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The effects of interference on the RSSI values of a ZigBee based indoor localization system

Cited by 15 publications

References 17 publications

Differentiation and Localization of Ground RF Transmitters Through RSSI Measures from a UAV

Differentiation and Localization of Ground RF Transmitters Through RSSI Measures from a UAV

Machine learning based wireless interference estimation in a robotic force-seeking application

Experimental UAV-Aided RSSI Localization of a Ground RF Emitter in 865 MHz and 2.4 GHz Bands

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