RF Source Localization using Unmanned Aerial Vehicle with Particle Filter

Hasanzade, Mehmet; Herekoglu, Omer; Yeniceri, Ramazan; Koyuncu, Emre; Tsourdos, Antonios

doi:10.1109/icmae.2018.8467555

Cited by 14 publications

(12 citation statements)

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“…In this way, it is possible to encode and keep updated the knowledge about potential target locations as a probability distribution, also referred to as belief or probabilistic map [58]; this is done by treating no-detection observations (i.e., measurements with no information on target position) as negative likelihood [102]. PTS methods consider optimization of the expected value of a search objective [46], such as the probability of detection [55], time to detection [82], information gain [18], or distance to the target [45], [103], [104]. Probabilistic approaches are suitable to real-world scenarios, especially when resource consumption (energy and time) is critical [82]; this is due to the use of stochastic target motion models [105], combined with the capability of representing realistic perception uncertainties [13], [83].…”

Section: Active Search and Probabilistic Target Searchmentioning

confidence: 99%

“…The sensing platform selects the information to extract from the belief map based on a predefined criterion (e.g. reaching a goal position [103], maximizing the information gain [18], [22], maximizing the probability of detection [58]) and injects it into the controller, in order to generate the next sensor action.…”

Section: Active Localization and Trackingmentioning

confidence: 99%

“…In particular, a target-representative setpoint (also known as reference point) is extracted from the belief map; then, the platform is driven towards the setpoint. The MAP [60] or the MMSE 13 [103] target position estimates are typical setpoint choices. In source seeking, the cost function is the distance between the current platform position 14 and the setpoint itself, namely…”

Section: B Task-driven Controlmentioning

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: Active Search and Probabilistic Target Searchmentioning

confidence: 99%

Section: Active Localization and Trackingmentioning

confidence: 99%

Section: B Task-driven Controlmentioning

confidence: 99%

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Active Sensing for Search and Tracking: A Review

Varotto¹,

Cenedese²,

Cavallaro³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Consequently, if the localization procedure is accurate (i.e., pt ≈ p t ), the condition p t ∈ Φ(s t ) is likely to be satisfied and d(c t , p + t ) is small, which is necessary to have high detection probabilities, according to (16). Finally, it is important to remark that J(•) is purely-exploitative 2 An alternative would be the MMSE estimate [6], namely…”

Section: Controller -mentioning

confidence: 99%

“…This control framework has been largely used to solve autonomous target search and tracking [3], often relying on probabilistic approaches [4]: data from onboard sensors and Recursive Bayesian Estimation (RBE) schemes [5] are used to generate a probabilistic map (also known as belief map), encoding the knowledge about potential target locations. The control problem is then cast as the optimization of a suitable objective function built upon the probabilistic map (e.g., time to detection [2], estimate uncertainty [3], distance to the target [6]). Stochastic motion and observation models [1] account for the uncertainties on target dynamics and on the perception process, and allow to treat nodetection observations [7].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Radio-Visual Active Sensing for Search and Tracking

Varotto¹,

Cenedese²,

Cavallaro³

2020

Preprint

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Active Search and Tracking for search and rescue missions or collaborative mobile robotics relies on the actuation of a sensing platform to detect and localize a target. In this paper we focus on visually detecting a radio-emitting target with an aerial robot equipped with a radio receiver and a camera. Visualbased tracking provides high accuracy, but the directionality of the sensing domain often requires long search times before detecting the target. Conversely, radio signals have larger coverage, but lower tracking accuracy. Thus, we design a Recursive Bayesian Estimation scheme that uses camera observations to refine radio measurements. To regulate the camera pose, we design an optimal controller whose cost function is built upon a probabilistic map. Theoretical results support the proposed algorithm, while numerical analyses show higher robustness and efficiency with respect to visual and radio-only baselines.

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Particle filter‐based aerial tracking for moving targets

Yılmaz

Bayram

2022

Journal of Field Robotics

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This paper considers the problem of tracking a moving target with a radio transmitter using an aerial robot in an online manner. The aerial robot is equipped with a low‐cost directional antenna and Software Defined Radio receiver to obtain the signal emitted by the target. The aerial robot rotates around itself and collects a predefined number of signal recordings from each direction to determine the bearing angle to the target in which the received signal strength is maximized. The measurement uncertainty is assumed to be bounded and represented by two triangular areas divided by a bisector. To localize and track the target, a particle filter‐based approach is proposed. In this approach, we integrate the discrete and bounded measurement model with the particle filter in such a way that the particles' weights are updated based on a novel method which considers the measurement wedge and the particle locations with respect to this wedge along with a logistic function. We also incorporate the doubling strategy into the particle filter to determine the next measurement locations and avoid arbitrarily large number of measurements. We choose wildlife monitoring as a use case scenario in which a radio transmitter is put on the animal under consideration to allow wildlife researchers to track it. Since each animal has its own motion behavior, we consider different motion models for the target, which are used in modeling animal movements in wildlife studies. Therefore, the proposed approach is validated using a target moving with varying velocity and acceleration. We verified the tracking performance of the approach through a series of extensive simulations. We compared the proposed approach with the optimal offline strategy in terms of the empirical competitive ratio of the total distance traveled and the tracking distance. We also developed a low‐cost hardware platform and software infrastructure for the proposed tracking system. Using this platform, we conducted field experiments for the stationary and moving targets.

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RF Source Localization using Unmanned Aerial Vehicle with Particle Filter

Cited by 14 publications

References 12 publications

Active Sensing for Search and Tracking: A Review

Active Sensing for Search and Tracking: A Review

Probabilistic Radio-Visual Active Sensing for Search and Tracking

Particle filter‐based aerial tracking for moving targets

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