Localizing RF Targets with Cooperative Unmanned Aerial Vehicles

Toussaint, Gregory J.; Lima, Pedro; Pack, Daniel J.

doi:10.1109/acc.2007.4282657

Cited by 23 publications

(13 citation statements)

References 8 publications

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“…This is the same program that was used to generate the results in Ref. 16, so we believe the results to be directly comparable. For the purpose of clarity, the simulator was run only in the LT mode, with the targets continuously emitting a detectable signal, and with the UAV positions initialized by randomly placing them within a 5 km radius of the target, and sensor ranges set to 5 km.…”

Section: Resultsmentioning

confidence: 94%

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Target Localization Using Multiple UAVs with Sensor Fusion via Sigma-Point Kalman Filtering

Plett

DeLima

Pack

2007

AIAA Infotech@Aerospace 2007 Conference and Exhibit

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This paper proposes a sensor-fusion methodology based on sigma-point Kalman filtering (SPKF) techniques, where the SPKF is applied to the particular problem of localizing a mobile target using uncertain nonlinear measurements relating to target positions. This SPKF-based sensor fusion is part of our larger research program in which the particular target localization solution of interest must address a number of challenging requirements: (a) covert/passive sensing means must be used; (b) the dynamic characteristics of the target are unknown; (c) the target is episodically mobile; and (d) the target is intermittently occluded from one or more sensors. Our research efforts seek to meet these requirements using a flight of small autonomous unmanned aerial vehicles (UAVs) with heterogeneous sensing capabilities. Each UAV carries one or more sensors, which include: radio frequency sensors, infrared sensors, and image sensors. Due to cost considerations and to payload constraints, it is not desirable for every UAV to have a full complement of sensing capability. In our implementation, the UAVs first search a predefined region for targets. Upon target detection, a small formation of UAVs comprising complementary heterogeneous sensors is autonomously assembled to localize the target. The UAVs then cooperatively locate the target by combining the sensor information collected by heterogeneous sensors onboard the UAVs. The output of the localization process is an estimate of the target's position and velocity as well as error bounds on that estimate. The primary purpose of this paper is to describe SPKF-based sensor-fusion for target localization using sensors that only provide direction-of-arrival information. Results of simulations are compared to prior work where linear Kalman filters were used to localize stationary targets. The SPKF gives much more accurate and consistent results, due mostly to its better handling of the nonlinear measurement relationship. Results are also given for the application of SPKF to mobile targets.

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Section: Resultsmentioning

confidence: 94%

“…Ref. 16), the actual sensor-error distribution is taken into account. However the KF state must be constantly updated to reflect a moving UAV and target dynamics are not simple to add (except by increasing process noise).…”

Section: Three Approaches To "Locate Target" Modementioning

confidence: 99%

Target Localization Using Multiple UAVs with Sensor Fusion via Sigma-Point Kalman Filtering

Plett

DeLima

Pack

2007

AIAA Infotech@Aerospace 2007 Conference and Exhibit

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“…Ultimately, we seek to investigate whether a set of inferior sensors configured and scheduled appropriately can outperform a set of sophisticated sensors if the total number of sensor readings is fixed in locating a target using Kalman filter estimation techniques. Results for the study of the impact of additional sensors over a fixed time window can be found in [23]. Fig.…”

Section: ) Triangulation and Angle-rate Techniquesmentioning

confidence: 99%

Cooperative Control of UAVs for Localization of Intermittently Emitting Mobile Targets

Pack

DeLima

Toussaint

et al. 2009

IEEE Trans. Syst., Man, Cybern. B

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Compared with a single platform, cooperative autonomous unmanned aerial vehicles (UAVs) offer efficiency and robustness in performing complex tasks. Focusing on ground mobile targets that intermittently emit radio frequency signals, this paper presents a decentralized control architecture for multiple UAVs, equipped only with rudimentary sensors, to search, detect, and locate targets over large areas. The proposed architecture has in its core a decision logic which governs the state of operation for each UAV based on sensor readings and communicated data. To support the findings, extensive simulation results are presented, focusing primarily on two success measures that the UAVs seek to minimize: overall time to search for a group of targets and the final target localization error achieved. The results of the simulations have provided support for hardware flight tests.

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“…The target's RCS is assumed to be σ b = 100m 2 and its trajectory is generated according to (7). For the Kalman Filter, we set the initial state estimate to be the true target location, and the covariance matrix associated with the initial position to be the identity matrix.…”

Section: A Simulation Setupmentioning

confidence: 99%

Adaptive Mobile Sensor Positioning for Multi-Static Target Tracking

Zhan

Casbeer

Swindlehurst

2010

IEEE Trans. Aerosp. Electron. Syst.

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Unmanned Air Vehicles (UAVs) are playing an increasingly prominent role in both military and civilian applications. In this paper, we focus on the use of multiple UAV agents in a target tracking application where performance is improved by exploiting each agent's maneuverability. Local time-delay and Doppler measurements made at each UAV are used as inputs to an Extended Kalman Filter (EKF) which tracks the target's position and velocity. Two simple metrics are defined to quantify the accuracy of the tracking algorithm, and heading feedback to the UAVs is used to minimize the metric and improve tracking performance. A simplified version of one of the algorithms that reduces computational complexity is also presented. Simulations demonstrate the significant improvement that results when the UAV sensors are allowed to be optimally positioned during tracking.

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Localizing RF Targets with Cooperative Unmanned Aerial Vehicles

Cited by 23 publications

References 8 publications

Target Localization Using Multiple UAVs with Sensor Fusion via Sigma-Point Kalman Filtering

Target Localization Using Multiple UAVs with Sensor Fusion via Sigma-Point Kalman Filtering

Cooperative Control of UAVs for Localization of Intermittently Emitting Mobile Targets

Adaptive Mobile Sensor Positioning for Multi-Static Target Tracking

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