Temporal considerations in sensor management

Malhotra, Raj

doi:10.1109/naecon.1995.521917

Cited by 18 publications

(9 citation statements)

References 7 publications

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“…There are many methods for sensor management. In [10] and [11], the temporal characteristics of sensor management are presented and the sequence decision process is described as a general Markov process. In [12], a sensor management framework for mobile robot is established using Bayesian Confidence Network and decision theory.…”

Section: Classic Sensor Managementmentioning

confidence: 99%

A Role Based Framework for Robot Sensor Management

Wang

Shi

et al. 2017

ITM Web Conf.

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Abstract:Sensors play an important role in the execution of robot tasks as an important component of understanding the world. In this paper, we propose a role-based sensor management framework on ROS (Robot Operating System), in which the role is a set of information needed for robot sensor development. Firstly, we develop a sensor management interface to facilitate the development of sensor applications on robot. And a subscriber module is proposed to shield the details of the sensor driver and the communication between processes, the sensor drivers on the robot are effectively managed. Robot sensors have different executive ability in different situations when robot performing tasks. For example, we know that RGB camera can collect a clear image when the light is strong, while generate a blurred image in weak light environment. Secondly, we propose an environment based sensor role dynamic evaluation mechanism to get the confidence of sensor in a certain environment. The confidence represents the executive ability of sensor when performing tasks. The confidence provides the basis for switching the task schemes. In the mechanism we can dynamically configure the environment information and confidence calculation method. In the experiment phase, we verify the effectiveness of the framework through a robot patrol task. Experiment results show that our framework can effectively manage the robot sensors with a certain environmental adaptability.

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Section: Classic Sensor Managementmentioning

confidence: 99%

A Role Based Framework for Robot Sensor Management

Wang

Shi

et al. 2017

ITM Web Conf.

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“…Sensor management has been approached in a number of contexts, from target tracking in tactical aircraft 1, 2 , autonomous vehicles and robotics 3 , anti-submarine warfare (ASW) 4 and computer vision 5 . Sensor management is analogous to Level 4 Process refinement as reflected in the well known JDL Data Fusion model 1,6 and seeks to optimize the multitude of configuration options a modern sensor suite provides.…”

Section: Introductionmentioning

confidence: 99%

Multiple objective optimization for active sensor management

et al. 2005

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The performance of a multi-sensor data fusion system is inherently constrained by the configuration of the given sensor suite. Intelligent or adaptive control of sensor resources has been shown to offer improved fusion performance in many applications. Common approaches to sensor management select sensor observation tasks that are optimal in terms of a measure of information. However, optimising for information alone is inherently sub-optimal as it does not take account of any other system requirements such as stealth or sensor power conservation. We discuss the issues relating to developing a suite of performance metrics for optimising multisensor systems and propose some candidate metrics. In addition it may not always be necessary to maximize information gain, in some cases small increases in information gain may take place at the cost of large sensor resource requirements. Additionally, the problems of sensor tasking and placement are usually treated separately, leading to a lack of coherency between sensor management frameworks. We propose a novel approach based on a high level decentralized information-theoretic sensor management architecture that unifies the processes of sensor tasking and sensor placement into a single framework. Sensors are controlled using a minimax multiple objective optimisation approach in order to address probability of target detection, sensor power consumption, and sensor survivability whilst maintaining a target estimation covariance threshold. We demonstrate the potential of the approach through simulation of a multi-sensor, target tracking scenario and compare the results with a single objective information based approach.

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“…Nash [39] uses linear programming to determine sensor-to-target assignment by using the trace of the Kalman filter error covariance matrices as objective function. Malhotra [40] uses Dynamic Programming for solving a Markov process that determines minimum costs based on the final state and works backwards. Washburn, et al [1] present a sensor management approach based on dynamic programming to predict the effects of future sensor management decisions.…”

mentioning

confidence: 99%

“…This method involves setting up artificial potentials and letting these virtual force models converge to a global solution. Maholtra [8] addresses the impact of receiving measurements at different times, and how to properly model temporal effects.…”

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confidence: 99%

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Distributed sensor resource management and planning

Khosla

Guillochon

2007

SPIE Proceedings

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The goal of sensor resource management (SRM) is to allocate resources appropriately in order to gain as much information as possible about a system. In our previous paper, we introduced a centralized non-myopic planning algorithm, C-SPLAN, that uses sparse sampling to estimate the value of resource assignments. Sparse sampling is related to Monte Carlo simulation. In the SRM problem we consider, our network of sensors observes a set of tracks; each sensor can be set to operate in one of several modes and/or viewing geometries. Each mode incurs a different cost and provides different information about the tracks. Each track has a kinematic state and is of a certain class; the sensors can observe either or both of these, depending on their mode of operation. The goal is to maximize the overall rate of information gain, i.e. rate of improvement in kinematic tracking and classification accuracy of all tracks in the Area of Interest. We compared C-SPLAN's performance on several tracking and target identification problems to that of other algorithms. In this paper we extend our approach to a distributed framework and present the D-SPLAN algorithm. We compare the performance as well as computational and communications costs of C-SPLAN and D-SPLAN as well as near-term planners.

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Temporal considerations in sensor management

Cited by 18 publications

References 7 publications

A Role Based Framework for Robot Sensor Management

A Role Based Framework for Robot Sensor Management

Multiple objective optimization for active sensor management

Distributed sensor resource management and planning

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