A dynamic approach to efficiently clustering and predicting groups for moving targets

Daoying Ma; Aidong Zhang

doi:10.1117/12.542417

25 August 2004 A dynamic approach to efficiently clustering and predicting groups for moving targets

Daoying Ma, Aidong Zhang

Proceedings Volume 5428, Signal and Data Processing of Small Targets 2004; (2004) https://doi.org/10.1117/12.542417
Event: Defense and Security, 2004, Orlando, Florida, United States

Abstract

Effective and efficient approaches to monitor and manage maneuvering objects are of great importance in various applications, such as wide battlefields, traffics, and wireless communications. Modern airborne radar sensors can provide wide-area surveillance coverage of ground activities. The huge volume of radar data renders it impractical and inefficient to examine all the activities of individual moving object. Clustering moving objects and predicting motion tendencies of large groups are becoming a crucial issue for optimizing resource distribution and formulating sound decisions. However, most traditional clustering techniques are static-object-oriented and not effective at clustering maneuvering objects. In addition, the radar data intermittence and noise data, which are caused by extraneous objects and stationary clutter background, are major difficulties in clustering and predicting groups. In this paper, we present a dynamic-object-oriented clustering approach to detecting and predicting large group activities over time. We propose a "core member" concept to support dynamic-object-oriented clustering and to mitigate the effects of data intermittence and noise data. In general, some special targets always tend to remain in a constant group and stay near the center of that group. To a large extent, the movement of these targets represents the activity of the entire group. To exploit this characteristic, we consider these special targets to be core members of their own cluster. The movements of the core members can help us detect clusters and predict their future movements. The performance and results of the application of our approach to CASTFOREM data sets are also presented.

Citation Download Citation

Daoying Ma and Aidong Zhang "A dynamic approach to efficiently clustering and predicting groups for moving targets", Proc. SPIE 5428, Signal and Data Processing of Small Targets 2004, (25 August 2004); https://doi.org/10.1117/12.542417

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KEYWORDS

Radar

Detection and tracking algorithms

Filtering (signal processing)

Target detection

Electronic filtering

Sensors

Data modeling

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