Contact management model assessment system for contact tracking in the presence of model uncertainty and noise
First Claim
1. A system for assessing accuracy of selected models of physical phenomena and for determining selection of alternate models in response to a data sequence representing values of a signal in the presence of noise comprising:
- a residual value generator for generating a residual sequence in response to difference values between the data sequence and an expected data sequence as would be represented by a selected model;
a feature estimate determination module for generating feature estimate values of a plurality of predetermined data features in the residual sequence generated by the residual value generator;
a threshold determination module for generating, in response to the feature estimate values generated by the feature estimate determination module, a threshold value for each feature at an estimated ratio of data to noise;
a feature existence probability value generator for generating, in response to the threshold value, probability values 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;
a feature amplitude probability value generator for generating an amplitude belief value indicating the belief of the amplitude of the respective feature in the data sequence;
a model selection module for selecting a model in response to the probability values generated by the feature existence probability value generator and the feature amplitude probability value generator; and
a control module for controlling the operations of residual value generator, the feature estimate determination module, the threshold determination module, the feature existence probability value generator, the feature amplitude probability value generator and the model selection module in a plurality of iterations, during each iteration the residual value generator module using the model selected by the model selection module during the previous iteration.
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Accused Products
Abstract
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.
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Citations
21 Claims
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1. A system for assessing accuracy of selected models of physical phenomena and for determining selection of alternate models in response to a data sequence representing values of a signal in the presence of noise comprising:
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a residual value generator for generating a residual sequence in response to difference values between the data sequence and an expected data sequence as would be represented by a selected model; a feature estimate determination module for generating feature estimate values of a plurality of predetermined data features in the residual sequence generated by the residual value generator; a threshold determination module for generating, in response to the feature estimate values generated by the feature estimate determination module, a threshold value for each feature at an estimated ratio of data to noise; a feature existence probability value generator for generating, in response to the threshold value, probability values 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; a feature amplitude probability value generator for generating an amplitude belief value indicating the belief of the amplitude of the respective feature in the data sequence; a model selection module for selecting a model in response to the probability values generated by the feature existence probability value generator and the feature amplitude probability value generator; and a control module for controlling the operations of residual value generator, the feature estimate determination module, the threshold determination module, the feature existence probability value generator, the feature amplitude probability value generator and the model selection module in a plurality of iterations, during each iteration the residual value generator module using the model selected by the model selection module during the previous iteration. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A method of assessing accuracy of selected models of physical phenomena and for determining selection of alternate models in response to a data sequence representing value of a signal in the presence of noise, the method comprising the steps of iteratively:
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generating a residual sequence in response to difference values between the data sequence and an expected data sequence as would be represented by a selected model; generating feature estimate values of a plurality of predetermined data features in the residual sequence; generating, in response to the feature estimate values, a threshold value for each feature at an estimated ratio of data to noise; generating, in response to the threshold value, feature existence probability values 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; generating an amplitude belief value indicating the belief of the amplitude of the respective feature in the data sequence; and selecting a model in response to the probability values and the amplitude belief value for use during a subsequent iteration. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A system for use in connection with a computer to assess accuracy of selected models of physical phenomena and for determining selection of alternate models in response to a data sequence representing value of a signal in the presence of noise comprising:
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a residual value generator module for controlling the computer to generate a residual sequence in response to difference values between the data sequence and an expected data sequence as would be represented by a selected model; a feature estimate determination module for controlling the computer to generate feature estimate values of a plurality of predetermined data features in the residual sequence; a threshold determination module for controlling the computer to generate, in response to the feature estimate values, a threshold value for each feature at an estimated ratio of data to noise; a feature probability value generator module for controlling the computer to generate, in response to the threshold value, probability values 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; a feature amplitude probability value module for controlling the computer to generate an amplitude belief value indicating the belief of the amplitude of the respective feature in the data sequence; a model selection module for controlling the computer to select a model in response to the feature existence probability values and feature amplitude probability values; and a control module for controlling the operations of the computer in response to the residual value generator module, the feature estimate determination module, the threshold determination module, the feature existence probability value generator module, the feature amplitude and the model selection module in a plurality of iterations, during each iteration the computer in response to the residual value generator module using the model selected by the model selection module during the previous iteration. - View Dependent Claims (16, 17, 18, 19, 20, 21)
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Specification