Optimal Placement of Heterogeneous Sensors for Targets with Gaussian Priors

Yang, Chun; Kaplan, Lance; Blasch, Erik; Bakich, Michael

doi:10.1109/taes.2013.6558009

Cited by 77 publications

(49 citation statements)

References 24 publications

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“…Other methods included learning target labeling [87], vehicle detection [88], and background context [89]. Currently methods include using context for sensor management and placement [90,91], anomaly detection from stochastic free grammars [92], color [93], and finally towards the growth of machine analytics in dictionary learning to support target identification context [94].…”

Section: Background On Contextual Tracking Methodsmentioning

confidence: 99%

Contextual Tracking Approaches in Information Fusion

Blasch

Yang²,

García

et al. 2016

Context-Enhanced Information Fusion

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Many information fusion solutions work well in the intended scenarios; but the applications, supporting data, and capabilities change over varying contexts. One example is weather data for electro-optical target trackers of which standards have evolved over decades. The operating conditions of technology changes, sensor/target variations, and the contextual environment can inhibit performance if not included in the initial systems design. In this chapter, we seek to define and categorize different types of contextual information. We describe five contextual information categories that support target tracking: (1) domain knowledge from a user to aid the information fusion process through selection, cueing, and analysis, (2) environment-to-hardware processing for sensor management, (3) known distribution of entities for situation/threat assessment, (4) historical traffic behavior for situation awareness patterns of life (POL), and (5) road information for target tracking and identification. Appropriate characterization and representation of contextual information is needed for future high-level information fusion designs to take advantage of the large data content available for a priori knowledge target tracking algorithm construction, implementation, and application

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Section: Background On Contextual Tracking Methodsmentioning

confidence: 99%

Contextual Tracking Approaches in Information Fusion

Blasch

Yang²,

García

et al. 2016

Context-Enhanced Information Fusion

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“…Current trends in information fusion include game theory modeling, cloud computing [32], sensor management [33], sparse-data processing [34], machine analytics [35], and multi-intelligence fusion [36]. These developments are applicable to contested environment situations as coordinated with other information sources.…”

Section: Accepted Understanding With Information Fusion Methodsmentioning

confidence: 99%

Issues and challenges of information fusion in contested environments: panel results

Blasch

Kadar

Chong³

et al. 2015

SPIE Proceedings

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With the plethora of information, there are many aspects to contested environments such as the protection of information, network privacy, and restricted observational and entry access. In this paper, we review and contrast the perspectives of challenges and opportunities for future developments in contested environments. The ability to operate in a contested environment would aid societal operations for highly congested areas with limited bandwidth such as transportation, the lack of communication and observations after a natural disaster, or planning for situations in which freedom of movement is restricted. Different perspectives were presented, but common themes included (1) Domain: targets and sensors, (2) network: communications, control, and social networks, and (3) user: human interaction and analytics. The paper serves as a summary and organization of the panel discussion as towards future concerns for research needs in contested environments.

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“…Other methods included learning target labeling [83], vehicle detection [84], and background context [85]. Currently methods include using context for sensor management and placement [86,87], anomaly detection from stochastic free grammars [88], occlusion detection [89], and finally towards the growth of machine analytics in dictionary learning to support target identification context [90].…”

Section: Contextual Tracking Methods (Review)mentioning

confidence: 99%

Overview of contextual tracking approaches in information fusion

et al. 2013

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Many information fusion solutions work well in the intended scenarios; but the applications, supporting data, and capabilities change over varying contexts. One example is weather data for electro-optical target trackers of which standards have evolved over decades. The operating conditions of: technology changes, sensor/target variations, and the contextual environment can inhibit performance if not included in the initial systems design. In this paper, we seek to define and categorize different types of contextual information. We describe five contextual information categories that support target tracking: (1) domain knowledge from a user to aid the information fusion process through selection, cueing, and analysis, (2) environment-to-hardware processing for sensor management, (3) known distribution of entities for situation/threat assessment, (4) historical traffic behavior for situation awareness patterns of life (POL), and (5) road information for target tracking and identification. Appropriate characterization and representation of contextual information is needed for future high-level information fusion systems design to take advantage of the large data content available for a priori knowledge target tracking algorithm construction, implementation, and application.

show abstract

Optimal Placement of Heterogeneous Sensors for Targets with Gaussian Priors

Cited by 77 publications

References 24 publications

Contextual Tracking Approaches in Information Fusion

Contextual Tracking Approaches in Information Fusion

Issues and challenges of information fusion in contested environments: panel results

Overview of contextual tracking approaches in information fusion

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