&lt;title&gt;Coastal battlefield reconnaissance and analysis program for minefield detection&lt;/title&gt;

Witherspoon, Ned H.; Holloway, John H.; Davis, Kenn S.; Miller, Richard W.; Dubey, Abinash C.

doi:10.1117/12.211346

Cited by 20 publications

(24 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This environment consists of a U.S. Navy minefield data set called the COBRA data with 39 disks (Witherspoon et al 1995, Fishkind et al 2007, Aksakalli et al 2011, Aksakalli and Ceyhan 2012, Aksakalli and Ari 2014. The COBRA data set is the only publicly available real-world instance of SOSP within the context of maritime minefield countermeasures, which constitute a rather important application area of SOSP and CTP in general.…”

Section: Simulation Environment Cmentioning

confidence: 99%

An AO^* Based Exact Algorithm for the Canadian Traveler Problem

Aksakallı

Sahin

Arı

2016

INFORMS Journal on Computing

View full text Add to dashboard Cite

T he Canadian traveler problem (CTP) is a simple, yet challenging, stochastic optimization problem wherein an agent is given a graph where some edges are blocked with certain probabilities and the status of these edges can be disambiguated dynamically upon reaching an incident vertex. The goal is to devise a traversal policy that results in the shortest expected walk length between a given starting vertex and a termination vertex. CTP has been shown to be intractable in many broad settings. In this paper, we introduce an optimal algorithm for the problem based on a Markov decision process formulation, which is a new improvement on AO * search that takes advantage of the special problem structure in CTP. We call our algorithm CAO * , which stands for AO * with caching. CAO * uses a caching mechanism to avoid re-expansion of previously visited states and makes use of admissible upper bounds at a node level for dynamic state-space pruning. CAO * is not polynomial time, but it can dramatically shorten the execution time needed to find an exact solution for moderately sized instances. We present computational experiments on a realistic variant of the problem involving an actual maritime minefield data set.

show abstract

Section: Simulation Environment Cmentioning

confidence: 99%

An AO^* Based Exact Algorithm for the Canadian Traveler Problem

Aksakallı

Sahin

Arı

2016

INFORMS Journal on Computing

View full text Add to dashboard Cite

show abstract

“…The specific application domain we consider is maritime minefield navigation, which has received considerable attention from scientific and engineering communities recently (Witherspoon et al 1995, Muhandiramge 2008. A particular instance we consider is a United States Navy minefield data set (called the COBRA data) that first appeared in Witherspoon et al (1995) and was later referred to in Priebe et al (1997Priebe et al ( , 2005, Fishkind et al (2007), Ye and Priebe (2010), Ye et al (2011), andAksakalli et al (2011). The COBRA data is illustrated in Figure 5 and tabulated in Table 2.…”

Section: Computational Experimentsmentioning

confidence: 99%

“…In CTP, the goal is to find the minimum expected length path over a finite graph whose edges are marked with their respective probabilities of being traversable and each edge's status can be discovered dynamically when encountered. SOSP and CTP have practical applications in important probabilistic path-planning environments such as robot navigation in stochastic domains (Blei and Kaelbling 1999, Ferguson et al 2004, Likhachev et al 2005, minefield countermeasures (Smith 1995, Witherspoon et al 1995, and adaptive traffic routing (Fawcett andRobinson 2000, Gao andChabini 2006). In fact, both problems as well as closely related ones have gained considerable attention recentlysee, e.g., Nikolova and Karger (2008), Eyerich et al (2009), Likhachev and Stentz (2009), Xu et al (2009), Aksakalli and Ceyhan (2012).…”

Section: Introductionmentioning

confidence: 99%

Penalty-Based Algorithms for the Stochastic Obstacle Scene Problem

Aksakallı¹,

Arı²

2014

INFORMS Journal on Computing

View full text Add to dashboard Cite

W e consider the stochastic obstacle scene problem wherein an agent needs to traverse a spatial arrangement of possible obstacles, and the status of the obstacles may be disambiguated en route at a cost. The goal is to find an algorithm that decides what and where to disambiguate en route so that the expected length of the traversal is minimized. We present a polynomial-time method for a graph-theoretical version of the problem when the associated graph is restricted to parallel avenues with fixed policies within the avenues. We show how previously proposed algorithms for the continuous space version can be adapted to a discrete setting. We propose a generalized framework encompassing these algorithms that uses penalty functions to guide the navigation in real time. Within this framework, we introduce a new algorithm that provides near-optimal results within very short execution times. Our algorithms are illustrated via computational experiments involving synthetic data as well as an actual naval minefield data set.Data, as supplemental material, are available at http://dx

show abstract

“…The output from the camera is the standard RS-170 interlaced video, which is recorded on a Hi-8 video recorder. For standoff mine detection ground (SMDG) test [1], [2] the camera and targets are stationary and therefore the images from six spectral bands are coregistered. These images are digitized and then used for subsequent processing.…”

Section: A Multispectral Imaging Sensormentioning

confidence: 99%

Detection of mines and minelike targets using principal component and neural-network methods

Miao

Azimi-Sadjadi

Tan³

et al. 1998

IEEE Trans. Neural Netw.

View full text Add to dashboard Cite

Abstract-This paper introduces a new system for real-time detection and classification of arbitrarily scattered surface-laid mines from multispectral imagery data of a minefield. The system consists of six channels which use various neural-network structures for feature extraction, detection, and classification of targets in six different optical bands ranging from near UV to near IR. A single-layer autoassociative network trained using the recursive least square (RLS) learning rule was employed in each channel to perform feature extraction. Based upon the extracted features, two different neural-network architectures were used and their performance was compared against the standard maximum likelihood (ML) classification scheme. The outputs of the detector/classifier network in all the channels were fused together in a final decision-making system. Two different final decision making schemes using the majority voting and weighted combination based on consensual theory were considered. Simulations were performed on real data for six bands and on several images in order to account for the variations in size, shape, and contrast of the targets and also the signalto-clutter ratio. The overall results showed the promise of the proposed system for detection and classification of mines and minelike tagets.

show abstract

<title>Coastal battlefield reconnaissance and analysis program for minefield detection</title>

Cited by 20 publications

References 0 publications

An AO^* Based Exact Algorithm for the Canadian Traveler Problem

An AO^* Based Exact Algorithm for the Canadian Traveler Problem

Penalty-Based Algorithms for the Stochastic Obstacle Scene Problem

Detection of mines and minelike targets using principal component and neural-network methods

Contact Info

Product

Resources

About

<title>Coastal battlefield reconnaissance and analysis program for minefield detection</title>

Cited by 20 publications

References 0 publications

An AO* Based Exact Algorithm for the Canadian Traveler Problem

An AO* Based Exact Algorithm for the Canadian Traveler Problem

Penalty-Based Algorithms for the Stochastic Obstacle Scene Problem

Detection of mines and minelike targets using principal component and neural-network methods

Contact Info

Product

Resources

About

An AO^* Based Exact Algorithm for the Canadian Traveler Problem

An AO^* Based Exact Algorithm for the Canadian Traveler Problem