Target Tracking Using
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Abstract Tracking a target in the presence of a large amount of clutter is a difficult task. A common technique that is used to accomplish this is the Kalman filter, which predicts the state of the target at the next time period, and chooses the detection that best matches this predicted state. This is called the nearest neighbor approach. However, nearest neighbor association is not always successful, especially when there are other detections from which to choose. One such case where this is true is in a shallow water environment. In this type of application, multistatic active sonar systems are used to try to ensonify quiet targets, and detect the returned signal. Unfortunately, many other returns are received as well, especially from bathymetric features. Therefore, it is critical that the system can distinguish the real target from the clutter, and associate the correct detection with the correct track. Equally important is the ability to suppress the tracks formed by the clutter, while at the same time allowing the true target track to be promoted through the system. To accomplish this, additional information will be used which is not used in the nearest neighbor association technique. The Kalman filter only uses kinematic information on which to base its decision, but this is just a small subset of the information that is available to the tracker. Additionally, there is information about the SNR of each detection, the location of the source and the receiver, the quality of the detection, and the history of detections from this particular location. An effective way of combining all this information to make a decision is to use an association technique which employs fuzzy logic. By using fuzzy logic, the target of interest will be tracked correctly, and at the same time, clutter tracks will be substantially reduced.
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