Learning sensor-detection policies

Malhotra, Raj; Blasch, Erik; Johnson, Jay

doi:10.1109/naecon.1997.622727

Cited by 13 publications

(7 citation statements)

References 6 publications

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“…A wide variety of approaches to SRM exist, including operations research and decision theoretic approaches [2] [17]. One way of considering the SRM problem is the need to use a computational decision approach to create an effective policy or decision for a given sensor exploitation task [8]. When stated this way, SRM sounds akin to a typical RL problem, but RL has seen limited prior use in SRM [8] [17].…”

Section: Sensor Resource Management (Srm)mentioning

confidence: 99%

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Assessing Multi-Agent Reinforcement Learning Algorithms for Autonomous Sensor Resource Management

Bihl¹,

Jones²,

Farr³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Unmanned aerial vehicles (UAVs) have applications in search and rescue operations and such operations could be more efficient by using appropriate artificial intelligence (AI) to enable a UAV agent to operate autonomously. Sensor resource management (SRM), which leverages capabilities across location intelligence, facilitates the efficient and effective use of UAVs and their sensors to complete a set of tasks. Furthermore, multiple UAVs, each with different sensor configurations, must be considered when maximizing mission effects. Instantiating operational autonomy for such teams requires considerable coordination. One AI approach relevant to this task is multi-agent reinforcement learning (MARL). However, MARL has seen limited prior use in SRM. This work evaluates the trade-space of MARL algorithms with respect to performing heterogeneous sensor resource management (SRM) tasks, considers the concept of evaluating MARL in a test and evaluation framework and compares a suit of algorithms with random and Bayesian hyperparameter optimization methods.

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Section: Sensor Resource Management (Srm)mentioning

confidence: 99%

“…While such a problem has direct parallels to typical RL problems, RL has seldom been applied to sensor resource management, e.g. [8].…”

Section: Introductionmentioning

confidence: 99%

Assessing Multi-Agent Reinforcement Learning Algorithms for Autonomous Sensor Resource Management

Bihl¹,

Jones²,

Farr³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…Reinforcement learning has also been used for several other sensor management tasks. In [11] temporal difference learning (TD(λ)) has been used to optimize target detection in discrete cells. In [12] a policy was learned to decide on using a shortrange and a long-range mode for a radar to track a target.…”

Section: Related Workmentioning

confidence: 99%

Sensor Path Planning Using Reinforcement Learning

Hoffmann

Charlish

Ritchie

et al. 2020

2020 IEEE 23rd International Conference on Information Fusion (FUSION)

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Reinforcement learning is the problem of autonomously learning a policy guided only by a reward function. We evaluate the performance of the Proximal Policy Optimization (PPO) reinforcement learning algorithm on a sensor management task and study the influence of several design choices about the network structure and reward function. The chosen sensor management task is optimizing the sensor path to speed up the localization of an emitter using only bearing measurements. Furthermore, we discuss generic advantages and challenges when using reinforcement learning for sensor management.

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“…The concept of using RL with UAVs or radars is not new. In [13]- [16], RL-based approaches for multi-target detection problems have been proposed through the optimization of MIMO radar waveforms. In [17], the Markov decision process (MDP) has been exploited for dual radar-communication problems, whereas in [18], [19] RL has been adopted for autonomous navigation in unknown environments, and in [20] for UAV trajectory planning.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning for UAV Autonomous Navigation, Mapping and Target Detection

Guerra,

Guidi,

Dardari

et al. 2020

Preprint

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In this paper, we study a joint detection, mapping and navigation problem for a single unmanned aerial vehicle (UAV) equipped with a low complexity radar and flying in an unknown environment. The goal is to optimize its trajectory with the purpose of maximizing the mapping accuracy and, at the same time, to avoid areas where measurements might not be sufficiently informative from the perspective of a target detection. This problem is formulated as a Markov decision process (MDP) where the UAV is an agent that runs either a state estimator for target detection and for environment mapping, and a reinforcement learning (RL) algorithm to infer its own policy of navigation (i.e., the control law). Numerical results show the feasibility of the proposed idea, highlighting the UAV's capability of autonomously exploring areas with high probability of target detection while reconstructing the surrounding environment.

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Learning sensor-detection policies

Cited by 13 publications

References 6 publications

Assessing Multi-Agent Reinforcement Learning Algorithms for Autonomous Sensor Resource Management

Assessing Multi-Agent Reinforcement Learning Algorithms for Autonomous Sensor Resource Management

Sensor Path Planning Using Reinforcement Learning

Reinforcement Learning for UAV Autonomous Navigation, Mapping and Target Detection

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