Moving Towards Wireless Sensors using RSSI Measurements and Particle Filtering

Radak, Jovan; Baulig, Lukas; Bijak, Dawid; Schowalter, Christian; Frey, Hannes

doi:10.1145/3134829.3134839

Cited by 4 publications

(11 citation statements)

References 20 publications

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“…We say that a target is active when it occasionally [40] or continuously [37] releases information on their presence [47] (e.g., through radio ID [72]) or on their position (e.g., GPS SAT dynamic A > 1 > 1 [22], [39], [60] [41], [61], [62] AL static A 1 1…”

Section: A the Targetsmentioning

confidence: 99%

“…In spite of this, latencies [138], [139] and environmental interference (e.g., cluttering and multi-path distortions) generate strong signal instabilities and limit the accuracy of RF-based solutions [121], [123]. To increase the SNR (often below 10 dB [140]), complex pre-processing algorithms have been suggested [60], [73], [141]. These, however, are computationally demanding and may need dedicated and energy-harvesting hardware, which collides with the typical use of RF architectures.…”

Section: Perceiving Active Targetsmentioning

confidence: 99%

“…To sum up, RSSI-based localization approaches are simple and cost efficient [60], but they suffer from poor localization accuracy [163]. Cluttered scenarios make the log-distance path-loss model (18) weakly reliable.…”

Section: Refmentioning

confidence: 99%

See 2 more Smart Citations

Active Sensing for Search and Tracking: A Review

Varotto¹,

Cenedese²,

Cavallaro³

2021

Preprint

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Active Position Estimation (APE) is the task of localizing one or more targets using one or more sensing platforms. APE is a key task for search and rescue missions, wildlife monitoring, source term estimation, and collaborative mobile robotics. Success in APE depends on the level of cooperation of the sensing platforms, their number, their degrees of freedom and the quality of the information gathered. APE control laws enable active sensing by satisfying either pure-exploitative or pure-explorative criteria. The former minimizes the uncertainty on position estimation; whereas the latter drives the platform closer to its task completion. In this paper, we define the main elements of APE to systematically classify and critically discuss the state of the art in this domain. We also propose a reference framework as a formalism to classify APE-related solutions. Overall, this survey explores the principal challenges and envisages the main research directions in the field of autonomous perception systems for localization tasks. It is also beneficial to promote the development of robust active sensing methods for search and tracking applications.

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Section: A the Targetsmentioning

confidence: 99%

Section: Perceiving Active Targetsmentioning

confidence: 99%

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Active Sensing for Search and Tracking: A Review

Varotto¹,

Cenedese²,

Cavallaro³

2021

Preprint

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“…To achieve this by FRs, the capability to navigate a FR towards a sensor node is required. One approach is based on received signal strength indication (RSSI) [ 99 , 100 ]. Specifically, in reference [ 99 ], the target sensor node periodically sends out beacons, and the FR can measure RSSI to determine the moving direction.…”

Section: Flying Robots Collaborating With Ground Objectsmentioning

confidence: 99%

“…Specifically, in reference [ 99 ], the target sensor node periodically sends out beacons, and the FR can measure RSSI to determine the moving direction. Under the similar setting, reference [ 100 ] presents a reduced particle filtering method, which is well suited for devices with limited computational power and energy resources.…”

Section: Flying Robots Collaborating With Ground Objectsmentioning

confidence: 99%

Towards the Internet of Flying Robots: A Survey

Huang

Savkin

2018

Sensors

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The Internet of Flying Robots (IoFR) has received much attention in recent years thanks to the mobility and flexibility of flying robots. Although a lot of research has been done, there is a lack of a comprehensive survey on this topic. This paper analyzes several typical problems in designing IoFR for real applications, including wireless communication support, monitoring targets of interest, serving a wireless sensor network, and collaborating with ground robots. In particular, an overview of the existing publications on the coverage problem, connectivity of flying robots, energy capacity limitation, target searching, path planning, flying robot navigation with collision avoidance, etc., is presented. Beyond the discussion of these available approaches, some shortcomings of them are indicated and some promising future research directions are pointed out.

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TrinaryMC: Monte Carlo Based Anchorless Relative Positioning for Indoor Positioning

Hernandez

Bulut

2020

2020 IEEE 17th Annual Consumer Communications &Amp; Networking Conference (CCNC)

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Moving Towards Wireless Sensors using RSSI Measurements and Particle Filtering

Cited by 4 publications

References 20 publications

Active Sensing for Search and Tracking: A Review

Active Sensing for Search and Tracking: A Review

Towards the Internet of Flying Robots: A Survey

TrinaryMC: Monte Carlo Based Anchorless Relative Positioning for Indoor Positioning

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