Continuous wave metal detector

• US 8,310,236 B2
• Filed: 09/18/2009
• Issued: 11/13/2012
• Est. Priority Date: 09/18/2009
• Status: Active Grant

• Current Assignee: L3harris Fuzing and Ordnance Systems Incorporated
• Original Assignee: L-3 Communications CyTerra Corporation (L3Harris Technologies, Inc.)
• Inventors: Duvoisin, Herbert III
• Primary Examiner(s): Patel, Paresh
• Application Number:
  US12/562,809
• Publication Number:
  US 20110068795A1
• Time in Patent Office: 1,152 Days
• Field of Search: None
• US Class Current: 324/326
• CPC Class Codes: G01V 3/104 using several coupled or uncoupled coils G01V3/101 takes precedence

Abstract

A training set including a target object set and a clutter object set is accessed. It is determined that the training set includes multiple types of targets or multiple types of clutter. The target feature value of a type of target is compared with the clutter feature value. The type of target is associated with the non-target object based on the comparison. A classifier is trained using the target feature value and the clutter feature value of the associated type of target and the non-clutter object. A feature value associated with an unknown object is applied to multiple classifiers to generate a set of metrics for the unknown object. The metrics are aggregated into an overall metric. Whether the unknown object is included in the target set is determined based on the overall metric.

Claims

1. A method comprising:

accessing a training set including a target object set and a clutter object set, the target object set including a target associated with a target feature value and the clutter object set including a non-target object associated with a clutter feature value;

determining that the training set includes multiple types of targets or multiple types of clutter;

comparing the target feature value of a type of target with the clutter feature value;

associating the type of target with the non-target object based on the comparison;

training a classifier using the target feature value and the clutter feature value of the associated type of target and the non-clutter object such that the classifier produces a metric that indicates that an object associated with the type of target is a target;

generating multiple classifiers, the multiple classifiers including the trained classifier;

applying a feature value associated with an unknown object to the multiple classifiers to generate a set of metrics for the unknown object;

aggregating the metrics into an overall metric; and

determining whether the unknown object is included in the target set based on the overall metric.

2. The method of claim 1, wherein aggregating the metrics into an overall metric comprises summing the metrics included in the set of metrics.

3. The method of claim 2, wherein, prior to summing the metrics, the metrics are normalized.

4. The method of claim 1, wherein the multiple classifiers include at least two different types of classifiers.

5. The method of claim 1, wherein the clutter set includes data representing multiple different types of soils.

6. The method of claim 1, wherein the target set includes data representing multiple different types of land mines.

7. The method of claim 1, wherein comparing the target feature value of a type of target with the clutter feature value comprises determining a measure of similarity between the target feature value and the clutter feature value, and associating the type of target with the non-target object based on the comparison comprises associating the type of target and the non-target object when the measure of similarity is below a threshold value.

8. A computer-readable non-transitory medium encoded with a computer program comprising instructions that, when executed, operate to cause a computer to perform operations comprising:

accessing a training set including a target object set and a clutter object set, the target object set including a target associated with a target feature value and the clutter object set including a non-target object associated with a clutter feature value;

determining that the training set includes multiple types of targets or multiple types of clutter;

comparing the target feature value of a type of target with the clutter feature value;

associating the type of target with the non-target object based on the comparison;

training a classifier using the target feature value and the clutter feature value of the associated type of target and the non-clutter object such that the classifier produces a metric that indicates that an object associated with the type of target is a target;

generating multiple classifiers, the multiple classifiers including the trained classifier;

applying a feature value associated with an unknown object to the multiple classifiers to generate a set of metrics for the unknown object;

aggregating the metrics into an overall metric; and

determining whether the unknown object is included in the target set based on the overall metric.