Contact management model assessment system for contact tracking in the presence of model uncertainty and noise

• US 5,581,490 A
• Filed: 12/09/1994
• Issued: 12/03/1996
• Est. Priority Date: 12/09/1994
• Status: Expired due to Fees

• Current Assignee: the united states of america as represented by the secretary of the navy
• Original Assignee: the united states of america as represented by the secretary of the navy
• Inventors: Keay, Kathleen D., Baylog, John G., Gong, Kai F., Hammel, Sherry E., Ferkinhoff, David J.
• Primary Examiner(s): Chin, Gary
• Assistant Examiner(s): FREJD, RUSSELL WARREN
• Application Number:
  US08/353,853
• Time in Patent Office: 725 Days
• Field of Search: 364/578, 364/567, 364/516, 364/574, 364/421, 364/517, 395/50
• US Class Current: 703/2
• CPC Class Codes: G01S 11/14 using ultrasonic, sonic, or infrasonic waves
• CPC Class Codes: G01S 13/723 by using numerical data
• CPC Class Codes: G01S 7/292 Extracting wanted echo-signals

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.

Claims

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.

2. A system as defined in claim 1 in which said threshold determination module includes:

a test statistic value generator for generating, for each feature, a test statistic value in response to a difference between a regression fit to the residual sequence both with and without regard to the feature, and in further response to a predetermined noise value; and

a threshold value generator for generating said threshold value as a function of said test statistic.

3. A system as defined in claim 2 in which said function is selected so that a statistical distribution of parameter estimates of the regression fit to the residual values with regard to the feature and the corresponding statistical distribution of parameter estimates of the regression fit to the residual values without regard to the feature are the same.

4. A system as defined in claim 1 in which said feature existence probability value generator includes:

a feature present probability value generator for generating, in response to a statistical distribution function representing a statistical distribution of the residual values with regard to the feature and the corresponding statistical distribution of the residual values without regard to the feature, and the threshold value, a feature present probability value representing a likelihood that the feature is present in the data sequence;

a feature absence probability value generator for generating, in response to the statistical distribution function and the threshold value, a feature absent probability value representing a likelihood that the feature is absent from the data sequence; and

a feature indeterminable probability value generator for generating, in response to the feature present probability value and the feature absent probability value, a feature indeterminable probability value representing a likelihood that the presence or absence of the feature in the data sequence is indeterminable.

5. A system as defined in claim 4 in which the feature existence probability value generator further includes:

a feature detection probability generator for generating a feature detect probability value in response to the statistical distribution function and the threshold value; and

a feature detection false alarm probability generator for generating a feature detection false alarm probability value in response to the statistical distribution function and the threshold value.

6. A system as defined in claim 5 in which the feature present probability value generator generates said feature present probability value as a difference between the feature detect probability value and the feature detection false alarm probability value.

7. A system as defined in claim 5 in which the feature detection probability generator and said feature detection false alarm probability generator further generate a reference feature detect probability value and a reference feature detection false alarm probability value in response to a reference statistical distribution function and a threshold value, the feature absence probability value generator generating the feature absence probability value in response to the reference feature detect probability value and a reference feature false alarm probability value and the feature detection false alarm probability value.

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:

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.

9. A method as defined in claim 8 in which said threshold value generating step includes the steps of:

generating, for each feature, a test statistic value in response to a difference between regression fits to the residual sequence with and without regard to the feature, and in further response to a predetermined noise value; and

generating said threshold value as a function of said test statistic.

10. A method as defined in claim 9 in which said function is selected so that a statistical distribution of the residual values with regard to the feature and the corresponding statistical distribution of the residual values without regard to the feature have a selected relationship.

11. A method as defined in claim 8 in which said feature existence probability value generating step includes the steps of:

for generating, in response to a statistical distribution function representing a statistical distribution of the residual values with regard to the feature and the corresponding statistical distribution of the residual values without regard to the feature, and the threshold value, a feature present probability value representing a likelihood that the feature is present in the data sequence;

generating, in response to the statistical distribution function and the threshold value, a feature absent probability value representing a likelihood that the feature is absent from the data sequence; and

generating, in response to the feature present probability value and the feature absent probability value, a feature indeterminable probability value representing a likelihood that the presence or absence of the feature in the data sequence is indeterminable.

12. A method as defined in claim 11 in which the feature existence probability value generating step further includes the steps of:

generating a feature detect probability value in response to the statistical distribution function and the threshold value; and

generating a feature detection false alarm probability value in response to the statistical distribution function and the threshold value.

13. A method as defined in claim 12 in which the feature present probability value generating step includes the step of generating said feature present probability value as a difference between the feature detect probability value and the feature detection false alarm probability value.

14. A method as defined in claim 12 in which the feature detection probability generating step and said feature detection false alarm probability generating step further include the steps of generating a reference feature detect probability value and a reference feature detection failure probability value in response to a statistical distribution function and a threshold value for a reference feature and the predetermined noise value, the feature absence probability value being generated in response to the reference feature detect reference probability value and a reference feature detection false alarm probability value and the feature false alarm probability value.

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:

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.

16. A system as defined in claim 15 in which said threshold determination module includes:

a test statistic value generator module for controlling the computer to generate, for each feature, a test statistic value in response to a difference between a regression fit to the residual sequence both with and without regard to the feature, and in further response to a predetermined noise value; and

a threshold value generator module for controlling the computer to generate said threshold value as a function of said test statistic.

17. A system as defined in claim 16 in which said function is selected so that a statistical distribution of parameter estimates of the regression fit to the residual values with regard to the feature and the corresponding statistical distribution of the parameter estimates of the regression fit to the residual values without regard to the feature are the same.

18. A system as defined in claim 15 in which said feature existence probability value generator module includes:

a feature present probability value generator module for controlling the computer to generate, in response to a statistical distribution function representing a statistical distribution of the regression fit to the residual values with regard to the feature and the corresponding statistical distribution of the regression fit to the residual values without regard to the feature, and the threshold value, a feature present probability value representing a likelihood that the feature is present in the data sequence;

a feature absence probability value generator module for controlling the computer to generate, in response to the statistical distribution function and the threshold value, a feature absent probability value representing a likelihood that the feature is absent from the data sequence; and

a feature indeterminable probability value generator module for controlling the computer to generate, in response to the feature present probability value and the feature absent probability value, a feature indeterminable probability value representing a likelihood that the presence or absence of the feature in the data sequence is indeterminable.

19. A system as defined in claim 18 in which the feature existence probability value generator module further includes:

a feature detection probability generator module for controlling the computer to generate a feature detect probability value in response to the statistical distribution function and the threshold value; and

a feature detection false alarm probability generator module for controlling the computer to generate a feature false alarm probability value in response to the statistical distribution function and the threshold value.

20. A system as defined in claim 19 in which the feature present probability value generator module controls the computer to generate said feature present probability value as a difference between the feature detect probability value and the feature detection false alarm probability value.

21. A system as defined in claim 19 in which the feature detection probability generator module and said feature detection false alarm probability generator module further control the computer to generate a reference feature detect probability value and a reference feature detection false alarm probability value in response to a reference statistical distribution function and a threshold value, the feature absence probability value generator module controlling the computer to generate the feature absence probability value in response to the reference feature detect probability value and a reference feature false alarm probability value and the feature false alarm probability value.