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Use of the interacting multiple model algorithm with multiple sensors

Authors
Journal
Mathematical and Computer Modelling
0895-7177
Publisher
Elsevier
Publication Date
Volume
44
Identifiers
DOI: 10.1016/j.mcm.2006.01.020
Keywords
  • Target Tracking
  • Multi-Sensor Fusion
  • Interacting Multiple Model
  • Kalman Filter
  • Performance Prediction
Disciplines
  • Computer Science

Abstract

Abstract In a tracking system with multiple sensors, fusion usually plays a critical role in combining information. There exist many fusion techniques and most of them fall into two categories — measurement fusion and track fusion, depending on what kind of information is to be shared among sensors. These two techniques are typically applied to centralized or distributed systems, respectively. In this paper, our main interest lies in the centralized systems which generally have a central processor where measurement fusion is performed. A common question arising in measurement fusion is the following: is it necessary for the system to process all the measurements, or does more data always mean better estimates? Blair et al. have shown that the use of multiple sensor data can sometimes degrade performance when a single model filter is used, and that the use of a multiple model filter, such as the interacting multiple model (IMM) algorithm, is preferred in order to best utilize the multiple sensor data. Since the IMM algorithm is the state-of-the-art technique for tracking maneuvering targets, we investigate the IMM algorithm for multiple sensor tracking in more detail. We have found that, even with the IMM, more data may not give us a better estimate and, in some cases, it may give us a worse estimate.

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