In this paper we discusses an improved algorithm for sensor-specific data processing and unsupervised data clustering for landmine discrimination. Pre-processor and data-clustering modules form a central part of modular sensor fusion architecture for landmine detection and discrimination. The dynamic unsupervised clustering algorithm (DUCA) is based on Dignet clustering. The self-organizing capability of Dignet is based on the idea of competitive generation and elimination of attraction wells. The center, width and depth characterize each attraction well. The Dignet architecture assumes prior knowledge of the data characteristics in the form of predefined well width. In this paper some modifications to Dignet architecture are presented in order to make Dignet truly self-organizing and data independent clustering algorithm. Information theoretic pre-processing is used to capture underlying statistical properties of the sensor data which in turn is used to define important parameter for Dignet clustering such as similarity metrics, initial cluster width etc. The width of the cluster is also adapted online so that a fixed width is not enforced. A suitable procedure for online merge and clean operations is defined to re-organize the cluster development. A concept of dual width is employed to satisfy the competing requirements of compact clusters and high coverage of the data space. The performance of the improved clustering algorithm is compared with base-line Dignet algorithm using simulated data.
|Journal||Proceedings of SPIE - The International Society for Optical Engineering|
|State||Published - 2000|
|Event||Detection and Remediation Technologies for Mines and Minelike Targets V - Orlando, FL, USA|
Duration: Apr 24 2000 → Apr 28 2000