USA As Represented by Secretary of the Navy

David J Ferkinhoff

Kai F Gong

Sherry E Hammel

Steven C Nardone

Kathleen D Keay

John G Baylog

A method for the selection of hypotheses for modeling physical phenomena, includes detecting if selected features are present by analyzing actual sensed data and parameter values of an initial physical phenomena model; comparing feature estimating hypotheses to the actual data for determining a belief probability assignment value (bpa) for each of the hypotheses which indicates the likelihood that the selected features exist in the actual data and the likelihood that such selected features cannot accurately be determined as existing due to the presence of noise; selecting a set of the hypotheses most accurately modeling the physical phenomena based on the bpa of each selected hypotheses meeting a predetermined criteria; generating evidential support values and lack of evidential support values for subsets of the set having non-zero subset bpa's; ranking the subsets having non-zero subset bpa's in order of decreasing subset bpa; unioning subsets of the power set for forming unioned subsets and determining support values and plausibility values for the unioned subsets; comparing the unioned evidential support values to a predefined threshold value; and using at least one of the unioned subsets having a unioned evidential support value most closely approximating or exceeding the threshold value for selecting alternate models having selected features which more closely approximate the actual data.

Hypothesis selection for evidential reasoning systems

The present invention relates to a method and a system for providing an estimate of the state of a contact. The method includes the steps of sensing the state of the contact; generating signals representative of the state of the contact; and processing the signals using a random search procedure to arrive at an estimate of the state of the contact. The random search procedure may employ the simulated annealing-based algorithm methodology or the genetic-based algorithm methodologies. The system includes sensors for sensing the state of the contact and a pre-programmed computer for generating the desired contact state estimates.

System and method for tracking vehicles using random search algorithms

A method for performing mutations in a genetic algorithm-based underwater oustic contact tracking system includes the steps of: (i) providing an initially ordered list of bit numbers; (ii) selecting a random number from a uniform distribution of numbers; (iii) performing an inverse mapping of the selected random number via a binomial distribution, to determine a number of bits to mutate; (iv) determining if mutation is to be performed; (v) selecting particular bits to mutate; (vi) complementing selected bits; and (vii) outputting mutated contact state variables. The invention further contemplates a system for performing the above method.

Method and apparatus for preforming mutations in a genetic

A system for providing an iterative method of assessing accuracy of selected models of physical phenomena and for determining selection of alternate models in response to a data sequence in the presence of noise. Initially, a residual sequence is generated reflecting difference values between in response to said data sequence and an expected data sequence as would be represented by a selected model. Feature estimate values of a plurality of predetermined data features in the residual sequence are then generated. In response to the feature estimate values, a threshold value is generated for each feature at an estimated ratio of data to noise power. Probability values are generated in response to the threshold value, representing the likelihood that the feature exists in the data sequence, does not exist in the data sequence, and that the existence or non-existence in the data sequence is not determinable, along with an amplitude probability value indicating the belief of the amplitude of the respective feature in the data sequence. Probability values are generated in response to the feature existence and amplitude probability values, representing the likelihood that various modelling hypotheses are represented by the observed features, or are not ruled out by the observed features in the presence of the given noise level. Finally, a model is selected in response to the probability values for use during a subsequent iteration.

Contact management model assessment system for contact tracking in the

A system for providing an iterative method of assessing accuracy of selec models of physical phenomena and for determining selection of alternate models in response to a data sequence in the presence of noise. Initially, a residual sequence is generated reflecting difference values between in response to said data sequence and an expected data sequence as would be represented by a selected model. Feature estimate values of a plurality of predetermined data features in the residual sequence are then generated. In response to the feature estimate values, a threshold value is generated for each feature at an estimated ratio of data to noise. Probability values are generated in response to the threshold value, representing the likelihood that the feature exists in the data sequence, does not exist in the data sequence, and that the existence or non-existence in the data sequence is not determinable. Finally, a model is selected in response to the probability values for use during a subsequent iteration.

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Middletown, RI, United States of America

David J Ferkinhoff