Object classification

• US 9,104,911 B2
• Filed: 05/13/2010
• Issued: 08/11/2015
• Est. Priority Date: 05/14/2009
• Status: Expired due to Fees

• Current Assignee: BAE Systems Plc
• Original Assignee: BAE Systems Plc
• Inventors: Barr, Jordi McGregor, Lloyd, Christopher Mark, Nicholson, David, Williams, Mark Lawrence
• Primary Examiner(s): HULKA, JAMES R
• Application Number:
  US13/319,903
• Publication Number:
  US 20120059829A1
• Time in Patent Office: 1,916 Days
• Field of Search: 367/88
• US Class Current: 1/1
• CPC Class Codes: B63G 2007/005 Unmanned autonomously operating mine sweeping vessels
• CPC Class Codes: B63G 7/00 Mine-sweeping; Vessels characterised thereby
• CPC Class Codes: G01S 15/88 Sonar systems specially adapted for specific applications seismic or acoustic prospecting or detecting G01V1/00
• CPC Class Codes: G01S 7/539 using analysis of echo signal for target characterisation; Target signature; Target cross-section
• CPC Class Codes: G06F 18/241 relating to the classification model, e.g. parametric or non-parametric approaches
• CPC Class Codes: G06F 2218/00 Aspects of pattern recognition specially adapted for signal processing

Abstract

A method and apparatus for processing an observation of an object to be classified, the method comprising using a particle filter in which an observation of an object to be classified is used to weight particles on a plurality of models for the object. The method may further comprise performing at least one further iteration of using the particle filter to weight the particles on the plurality of models for the object using a further observation of the object to be classified. The method may further comprise providing respective classification probabilities that each respective model corresponds to the object based on the weightings of the particles of the respective model. Each of the respective particles may be divided into a respective plurality of sub-particles.

Claims

1. A method of processing an observation of an object to be classified by a mine classifier, the method comprising:

providing at least one processor of said mine classifier in operative communication with at least one particle filter;

providing a storage means containing a plurality of empirically derived models of mines, wherein each said empirically derived model in said plurality comprise a physical quantity that characterizes exterior shape and size of mines belonging to a class of mines and a plurality of angles from which said physical quantity is measured;

observing with at least one sensor in operative communication with said mine classifier an object to be classified, wherein said object to be classified is of the same type as at least one of those previously empirically derived models of mines;

storing said at least one observation of said object to be classified in memory or on a non-transitory storage medium;

using said particle filter to compare said empirically derived models to said at least one stored observation; and

providing a probability of a match between said at least one empirically derived model and said at least one stored observation.

2. A method of processing an observation of an object to be classified according to claim 1, further comprising performing at least one further iteration of using the particle filter to weight the particles on the plurality of models of mines using a further observation of the object to be classified.

3. A method of processing an observation of an object to be classified according to claim 1, wherein a particle is ignored or discarded if its weighting is below a threshold.

4. A method of processing an observation of an object to be classified according to claim 1, the method further comprising dividing each of the respective particles into a respective plurality of sub-particles;

wherein the number of sub-particles in each of the respective pluralities of sub-particles is dependent upon the weighting of the respective particle from which the respective plurality of sub-particles was divided.

5. A method of processing an observation of an object to be classified according to claim 4, wherein each of the respective pluralities of sub-particles is positioned in the vicinity of the respective particle from which the respective plurality of sub-particles was divided on the respective model upon which the respective particle (a₁-a₅, b₁-b₅) was defined.

6. A method of processing an observation of an object to be classified according to claim 1, the method further comprising providing respective classification probabilities that each respective model corresponds to the object based on the weightings of the particles of the respective model.

7. A method of processing an observation of an object to be classified according to claim 1, the method further comprising:

receiving a plurality of models of mines defined in terms of an observation parameter as a function of the measurement variable;

selecting a plurality of values of the measurement variable or change in the measurement variable;

for each model, determining a respective corresponding value of the observation parameter for each of the selected values of the measurement variable/changes in the measurement variable;

for each selected value of the measurement variable/change in the measurement variable, determining a value of a function using the determined values of the observation parameter corresponding to each of the models, wherein the function provides that each determined value of the function is indicative of an expected classification potential level; and

selecting the value or change in value of a measurement variable for the observation of the object dependent upon the determined classification potential level values;

wherein the classification potential level is a relative expected level of;

(i) the information, for use in a process leading toward classification of the object as one of the models, that is to be obtained if a measurement of the observation parameter were taken of the object at a respective value of the measurement variable; and

/or

(ii) the certainty with which the object could be classified as one of the models if a measurement of the observation parameter were taken of the object at the respective value of the measurement variable.

8. A method of processing an observation of an object to be classified according to claim 7, wherein the classification potential level is an expected entropy value.

9. The method of claim 1 wherein said observation means comprises at least one acoustic transmitter and at least one acoustic receiver.

10. A method of processing an observation of an object to be classified by a mine classifier, the method comprising:

Providing a data processing system within said mine classifier capable of;

receiving one or more possible empirically derived models each said model representing a class of mine, said model being defined in terms of an observation parameter characterizing the exterior shape and size of a measured object in said class of mine as a function of a measurement variable;

for each model, defining a respective set of one or more particles in the model;

performing an observation of the object at a value of the measurement variable to provide a sensed value of the observation parameter; and

weighting the particles according to the sensed value of the observation parameter.

11. A method of processing an observation of an object to be classified according to claim 10, the method further comprising at least one iteration of:

changing the value of the measurement variable;

adjusting the particles on the models in correspondence to the new measurement variable;

performing a further observation of the object at the new value of the measurement variable to provide a new sensed value of the observation parameter; and

weighting the particles according to the new sensed value of the observation parameter.

12. A method of processing an observation of an object to be classified according to claim 10, wherein the measurement variable is the angle from which the observation of the target is made, wherein said angle is a two-dimensional angle taken without regard to height differences between the target and the point from which the observation is made and comprises angles between 0 and 360 degrees.

13. A method of processing an observation of an object to be classified according to claim 10, wherein the process of weighting the particles comprises using error bounds on an observation of the object to be classified.

14. A method of processing an observation of an object to be classified according to claim 10, wherein a particle is ignored or discarded if its weighting is below a threshold.

15. A method of processing an observation of an object to be classified according to claim 10, the method further comprising dividing each of the respective particles into a respective plurality of sub-particles;

wherein the number of sub-particles in each of the respective pluralities of sub-particles is dependent upon the weighting of the respective particle from which the respective plurality of sub-particles was divided.

16. A method of processing an observation of an object to be classified according to claim 15, wherein each of the respective pluralities of sub-particles is positioned in the vicinity of the respective particle from which the respective plurality of sub-particles was divided on the respective model upon which the respective particle was defined.

17. A method of processing an observation of an object to be classified according to claim 10, the method further comprising providing respective classification probabilities that each respective model corresponds to the object based on the weightings of the particles of the respective model.

18. A method of processing an observation of an object to be classified according to claim 10, the method further comprising:

receiving a plurality of models for the object defined in terms of an observation parameter as a function of the measurement variable;

selecting a plurality of values of the measurement variable or change in the measurement variable;

for each model, determining a respective corresponding value of the observation parameter for each of the selected values of the measurement variable/changes in the measurement variable;

for each selected value of the measurement variable/change in the measurement variable, determining a value of a function using the determined values of the observation parameter corresponding to each of the models, wherein the function provides that each determined value of the function is indicative of an expected classification potential level; and

selecting the value or change in value of a measurement variable for the observation of the object dependent upon the determined classification potential level values;

wherein the classification potential level is a relative expected level of;

(i) the information, for use in a process leading toward classification of the object as one of the models, that is to be obtained if a measurement of the observation parameter were taken of the object at a respective value of the measurement variable; and

/or

(ii) the certainty with which the object could be classified as one of the models if a measurement of the observation parameter were taken of the object at the respective value of the measurement variable.

19. A method of processing an observation of an object to be classified according to claim 18, wherein the classification potential level is an expected entropy value.

20. The method of claim 10 wherein said observations are performed using at least one acoustic transmitter and at least one acoustic receiver.