Quadrotor control for RF source localization and tracking

Isaacs, Jason T.; Quitin, François; Carrillo, Luis Rodolfo García; Madhow, Upamanyu; Hespanha, João P.

doi:10.1109/icuas.2014.6842262

Cited by 32 publications

(18 citation statements)

References 14 publications

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“…Two paired waypoints that are used in DRSSI calculation should be far enough apart, as it has large impact on the performance of SLUS. Although a typical AOA sensor is costly, a less costly with lower resolution AOA sensor is considered, which has shown that can be helpful in improving the SLUS accuracy and convergence ratio as it was shown for localization in our previous research (Dehghan et al, 2012;Dehghan et al, submitted for publication;Isaacs et al, 2014). The more number of RF sources available in the search area causes the more accuracy and the better convergence ratio of the SLUS approach.…”

Section: Conclusion On the Simulation Resultsmentioning

confidence: 92%

SLAM-inspired simultaneous localization of UAV and RF sources with unknown transmitted power

Dehghan

Moradi

2015

Transactions of the Institute of Measurement and Control

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In the unmanned aerial vehicle (UAV) based localization of slow-moving radio frequency (RF) sources with unknown transmitted strength of signal, such as a person with a cell phone in a search and rescue mission, the UAV navigation errors are significant sources of localization error. Although the use of a global positioning system (GPS) can reduce the UAV's localization error significantly resulting in more accurate RF source localization, if the GPS signal is lost temporarily or permanently, the accuracy of the UAV-based localization decreases rapidly. In this paper, a simultaneous localization and mapping (SLAM)-inspired approach for simultaneous localization of UAV and RF sources (SLUS) is proposed. The proposed approach solves these two connected problems, i.e. the UAV localization and RF source localization, simultaneously to decrease the error of the UAV position estimation and the error of the RF source localization. In the proposed approach, beside the UAV position prediction, the RF source position prediction is also performed. Then the predicted states are augmented and the augmented predicted state information is corrected using range-ratio and bearing observations, i.e. RF source features, considering the unknown transmitted power. The proposed approach is simulated and the results show that the normal divergence of a target localization and the divergence of the UAV navigation in latitude and longitude channels have been eliminated using this approach. In other words, simultaneous localization of the UAV and RF sources uses the RF sources, as features in the environment, to aid the navigation system. Although the approach is similar to the mapping of RF sources in the environment, the created map would be useless after finding the RF sources. That is why SLUS has been used instead of SLAM. The main contributions of this work are: 1) performing simultaneous localization of a UAV and targets using RF signals, especially in a non-line of sight (NLOS) condition; 2) using difference of signal strength, i.e. differential received strength signal indicator (DRSSI), to eliminate the impact of unknown target signal power; and 3) simultaneous multi-target localization and tracking.

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Section: Conclusion On the Simulation Resultsmentioning

confidence: 92%

SLAM-inspired simultaneous localization of UAV and RF sources with unknown transmitted power

Dehghan

Moradi

2015

Transactions of the Institute of Measurement and Control

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“…This research studied the real-time behavior of the consensus algorithm when applied to the heading and altitude dynamics of a group of four holonomic UAS. On the other hand, our preliminary work [40] presented experimental results on single-agent UAS control strategies enabling the real-time localization and tracking of static and/or dynamic RF sources. A controller for continuous heading rotation was implemented, which allowed rotation of the UAS about its vertical axis, while maintaining a constant altitude hover or a translational displacement.…”

Section: Preliminary Resultsmentioning

confidence: 99%

“…However, in these works the consensus protocols were not implemented in a fully distributed way In this stage, each agent makes use of its onboard sensors to estimate a bearing angle associated with the direction at which the asset is located. A control strategy is implemented for this specific stage, which allows the agent to rotate several times about its own´-axis (vertical axis) until the sensing device determines a bearing angle at which the source can be encountered, for example, the bearing angle at which the received signal strength of an RF source or the concentration of a chemical exhibits a maximum value [40]. Figure 2 illustrates the agents' behavior during this stage.…”

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confidence: 99%

Adaptive consensus algorithms for real‐time operation of multi‐agent systems affected by switching network events

Muñoz

Espinoza

et al. 2016

Intl J Robust & Nonlinear

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We propose a control strategy based on distributed adaptive leader-follower consensus algorithms for multiagent systems (MAS) affected by switching network events. The strategy allows each agent in the MAS to compute its own control input based on local information and information coming from its neighbors. In this sense, MAS distributed control laws are obtained where the coupling gain of the associated communication graph is adapted dynamically in real-time. The consensus algorithm is extended with a switching network topology approach, which ensures appropriate performance even when the MAS network topology is prone to arbitrary switching. A real-time experimental application is presented, where a MAS consisting of four rotorcraft UAS successfully performed the tasks of autonomously approaching and escorting a leader, even in the situation when the network topology was arbitrarily changing. Additionally, a Lyapunov stability analysis is included, which demonstrates that the tracking errors between leader and follower agents converge asymptotically to zero.Diverse efforts have been placed on the development of distributed coordination strategies. In general, MAS consensus methodologies [10] are the basis behind these strategies because they enable autonomous formation [11,12], flocking [13,14] rendezvous [15,16] and position synchronization [17].In a realistic scenario, the agents in a MAS are not homogeneous. For example, in a cooperative MAS team, it could be that a sensing agent performs the measurements, and sends its data to a data processing agent, which will perform estimation tasks and will possibly transmit an actuation command back to the sensing agent. Therefore, an effective communication is essential in the overall performance of the team. In general, a communication link between any two agents can be established when they explicitly exchange information by means of wireless communication links. In addition to enabling sharing data between agents, these links make possible, for example, RF-based agent detection and identification by means of teams of dynamic sensors [18]. On the other hand, an autonomous agent using on-board sensors (imaging, proximity or RF sensors) for estimating the position of a neighboring agent or target is also establishing an implicit unidirectional communication link among them [3]. Unfortunately, unreliable wireless channels and heavy computational loads may affect every kind of communication link and degrade the MAS performance.Some of the issues introduced by unreliable wireless transmission and their impact on estimation and control have been studied in [19,20]. Promising solutions for overcoming network issues have been presented in [21,22]; however, the studied scenarios considered static-agents exclusively. Most of the works on MAS assume ideal communication links, or ideal within a radius and nonexistent outside of it, which cannot be ensured in real-time implementation. The impact of unreliable communication channels on decision-making over a wireless network is s...

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“…The DOA estimation is performed by analysis of the received signal at the sensor or sensor array. Although massive sensor array is the trend of the current research due to the decreasing price of sensors, there are still applications where the number of array elements is limited due to space or weight constraints, for example, multiple sensors mounted on an unmanned aerial vehicle (UAV) (Isaacs et al 2014). Hence, It is of interest to discriminate more signals in terms of DOA with fewer sensors in an array.…”

Section: Introductionmentioning

confidence: 99%

DOA estimation with a rotational uniform linear array (RULA) and unknown spatial noise covariance

Lin

Razul³

et al. 2016

Multidim Syst Sign Process

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Direction of Arrival (DOA) estimation is one of the major tasks in array signal processing. In this paper, a new DOA estimation method is proposed using a rotational uniform linear array (RULA) consisting of omnidirectional sensors. The main contribution of the proposed method is that the number of distinguishable signals is larger than the methods in the literature with a uniform linear array consisting of the same number of omnidirectional sensors. Moreover, the new method can effectively reduce unknown spatial noises using a generalized complement projection matrix under the RULA framework. Simulations are presented to illustrate the effectiveness of the proposed method and comparison with some existing DOA estimation methods is also made.

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Quadrotor control for RF source localization and tracking

Cited by 32 publications

References 14 publications

SLAM-inspired simultaneous localization of UAV and RF sources with unknown transmitted power

SLAM-inspired simultaneous localization of UAV and RF sources with unknown transmitted power

Adaptive consensus algorithms for real‐time operation of multi‐agent systems affected by switching network events

DOA estimation with a rotational uniform linear array (RULA) and unknown spatial noise covariance

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