ADAPTIVE RADAR

• US 20100109938A1
• Filed: 01/31/2008
• Published: 05/06/2010
• Est. Priority Date: 01/31/2007
• Status: Active Grant

• Current Assignee: Aveillant Limited (Thales SA)
• Original Assignee: Aveillant Limited (Thales SA)
• Inventors: Smithson, Allan Geoffrey, Utsi, Per Arne Vincent, Phillips, Desmond Keith, Carter, Edwin Christopher, Edgecombe, Jonathan Peter, Pumphrey, Samuel Julius, Oswald, Gordon Kenneth Andrew, Burchett, Michael Hugh
• Granted Patent: US 8,314,732 B2
• Time in Patent Office: Days
• US Class Current: 342/90
• CPC Class Codes: G01S 13/0209 Systems with very large relative bandwidth, i.e. larger than 10 %, e.g. baseband, pulse, carrier-free, ultrawideband
• CPC Class Codes: G01S 13/522 using transmissions of interrupted pulse modulated waves
• CPC Class Codes: G01S 13/5248 combining a coherent MTI processor with a zero Doppler processing channel and a clutter mapped memory, e.g. MTD (Moving target detector), area MTI G01S13/538
• CPC Class Codes: G01S 13/726 Multiple target tracking
• CPC Class Codes: G01S 13/886 for alarm systems alarms with electrical actuation G08B13/22
• CPC Class Codes: G01S 2013/466 by Trilateration, i.e. two antennas or two sensors determine separately the distance to a target, whereby with the knowledge of the baseline length, i.e. the distance between the antennas or sensors, the position data of the target is determined
• CPC Class Codes: G01S 7/2926 by integration
• CPC Class Codes: G01S 7/411 Identification of targets based on measurements of radar reflectivity G01S7/415 takes precedence
• CPC Class Codes: G01S 7/415 Identification of targets based on measurements of movement associated with the target

Abstract

A method of classifying items from reflected signals returned from said items is disclosed, the method comprising: processing said return signals to discriminate between a first set of signals indicative of items of interest and a further set of signals indicative of clutter; identifying items from said first set of signals and classifying them as a first class of item; processing said further set of signals to identify a second set of signals indicative of further items of interest; identifying items from said second set of signals and classifying them as a second class of item.

Claims

1. A method of classifying items from reflected signals returned from said items, the method comprising:

processing said return signals to discriminate between a first set of signals indicative of items of interest and a further set of signals indicative of clutter;

identifying items from said first set of signals and classifying them as a first class of item;

processing said further set of signals to identify a second set of signals indicative of further items of interest;

identifying items from said second set of signals and classifying them as a second class of item.

2. A method according to claim 1 wherein the signals of said first set are indicative of items at least part of each of which is moving at least in part.

3. A method according to claim 1 wherein the signals of said second set are indicative of substantially stationary items.

16. A method according to claim 1 comprising determining at least one parameter comprising at least one of:

(a) at least one characteristic of a return signal from an identified item; and

(b) at least one attribute of an identified item;

and classifying the item accordingly.

17. A method according to claim 16 comprising maintaining a history of the at least one determined parameter for an identified item and classifying the item in dependence on said history.

18. A method according to claim 17 wherein said determined parameter history is maintained for an item from when it is first detected.

19. A method according to claim 16 comprising determining a prediction of the at least one determined parameter and classifying said item in dependence on the conformity of a signal return from the item with said prediction.

20. A method according to claim 19 wherein said prediction is refined over time in dependence on historical data.

21. A method according to claim 16 comprising comparing said determined parameter or history thereof with a modelling function and classifying the item in dependence on said comparison.

22. A method according to claim 21 comprising iteratively comparing said determined parameter or history thereof with modelling functions of increasingly higher order and classifying the item in dependence on said comparison.

26. A method according to claim 16 comprising comparing the at least one determined parameter or history thereof with a stored parameter or parameter history and classifying the item in dependence on said comparison.

27. A method according to claim 16 wherein the at least one determined parameter comprises a characteristic comprising at least one of phase, phase mismatch, and amplitude or signal strength/intensity.

28. A method according to claim 1 comprising determining a velocity of at least part of each identified item from said return signals and classifying the item accordingly.

35. A method according to claim 1 wherein an item classified as said first class of item remains so classified for at least a predetermined number of scans after reflected signals from said subject are no longer received.

36. A method according to claim 1 wherein said classification is dependent on a confidence level that said item has been correctly classified as a first or second class of item.

37. A method according to claim 1 wherein when an item is classified said classification persists in dependence on a confidence level that said item has been correctly classified.

38. A method according to claim 1 comprising processing signals returned from a classified item over time and maintaining said classification, reclassifying said item, or declassifying said item in dependence on said processing.

39. A method according to claim 38 wherein said classified item is initially classified as a second class of item and as a result of said processing over time said item is reclassified as a first class of item.

40. A method according to claim 1 comprising processing return signals from items classified as items of said first class or said second class and further classifying said items into different classes or sub-classes.

41. A method according to claim 1 wherein each classification, reclassification, declassification, and/or classification into a sub-class is stored in a classification history for the item to which they relate.

42. A method according to claim 1 wherein said classification comprises deriving an indication of a material type for an item from the corresponding return signals and classifying the item accordingly.

43. A method according to claim 1 comprising classifying an item in dependence on its interaction with another item.

44. A method according to claim 43 wherein said interaction comprises one of the items moving behind the other.

48. A method according to claim 1 comprising identifying a cluster of responses from different parts of an item and classifying the item in dependence on the nature of said cluster.

49. A method according to claim 48 wherein said cluster comprises between three and fifteen responses and preferably ten responses.

50. A method according to claim 1 comprising determining a threat level for an item and classifying said item into a class or sub-class accordingly.

144. A method according to claim 16 wherein the at least one determined parameter comprises an attribute comprising at least one of position, range, range rate, velocity, acceleration, track, and trajectory.

145. A method according to claim 28 wherein an item is classified as said first class of item if at least one of the following applies:

(a) said velocity is non-zero;

(b) said velocity is non-zero for a predetermined number or percentage of scans; and

(c) said velocity is non-zero when averaged over time.

146. A method according to claim 28 wherein an item is classified as said second class of item if at least one of the following applies:

(d) said velocity is zero;

(e) said velocity is zero for a predetermined number or percentage of scans; and

(f) said velocity is below a predetermined threshold when averaged over time.

147. A method according to claim 21 wherein the or at least one modelling function is at least one of:

(g) mathematical function (for example a polynomial);

(h) an oscillatory function; and

(i) adaptive over time in dependence on historical parameter data.

4-15. -15. (canceled)

23-25. -25. (canceled)

29-34. -34. (canceled)

45-47. -47. (canceled)

51-64. -64. (canceled)

65. Apparatus for classifying items from reflected signals returned from said items, the apparatus comprising:

means for receiving said return signals (for example a receiver);

means for processing said return signals (for example a processor) said processing means being configured for;

processing said return signals to discriminate between a first set of signals indicative of items of interest and a further set of signals indicative of clutter;

identifying items from said first set of signals and classifying them as a first class of item;

processing said further set of signals to identify a second set of signals indicative of further items of interest;

identifying items from said second set of signals and classifying them as a second class of item.

67. Apparatus according to claim 65 comprising means for outputting a representation of each classified item for display, and means for displaying said representation.

68. Apparatus according to claim 65 comprising means for outputting an alert signal if the classification of an item changes and means for issuing an associated alert.

69. Apparatus according to claim 65 wherein the processing means comprises means for extracting at least one parameter comprising at least one of:

(a) a characteristic of a return signal from an identified item; and

(b) an attribute of an identified item; and

for classifying the item accordingly.

66. (canceled)

70-143. -143. (canceled)