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.

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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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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