Parallel Processing, Ranging, and Clustering for Bee Tracing Systems

Several years ago, the bee tracing system was set up by researchers from both National Taiwan University and National Chung Cheng University. Technics involve: harmonic radar, micro transponder, and pseudorandom noise ranging. However, the heavy load of correlation calculations caused bottlenecks, and further limited the rotation speed of the antenna. Such issue led to the lack of bees’ location information. Moreover, the target identification in original system relied on manual work, which is neither effective nor precise. In this paper, we hereby introduce a new processing structure (parallel processing) and a series of algorithms (detection, ranging and clustering) with respect to the mentioned problem in original system. The parallel processing structure is mainly aim at solving the high computational complexity of our system, so that the result could be real-time presented. This structure properly utilized all four cores by parallel programming, thus increasing the scanning speed by 14 times, and the scanning of a 40° range will cost only 3 seconds. For the detection, we are going to use the Artificial Neural Network as our classifier, and the feature extraction is based on: 1) “Cumulant”, a higher-order statistic quantity, and 2) “Resemblance Coefficient”, an effective approach for radar signal feature. Its accuracy could be as high as 99.6%. We will also further discuss the statistical properties of Pseudorandom Noise under the serious noise situation, giving prominence to the utilization of cumulant here. Sometimes we may suffer from reflective propagations, we will introduce Hampel Identifier method to deal with such ranging problem. What comes next is the repeating of single target’s signals due to limited directivity of our antenna. We modified the DBSCAN algorithm, creating a novel method which is especially useful for clustering the spreading points such as our case.