Real-time adaptive fuzzy density clustering for multi-target data association

Abstract

The problem of data association for tracking multiple targets based on using the ship-borne radar is addressed in this study. A robust fuzzy density clustering algorithm is proposed, that contains three steps. At first, a customized form of adaptive density clustering is used to determine valid measurements for each target’s state. In the second step, the degree of fuzzy membership for each valid measurement is determined based on the maximum entropy approach. At the final step, the measurements with a maximum degree of membership are used for updating the position of the targets. The proposed approach does not require gating techniques and led to the reduction of steps in comparison with other data association methods. In addition, the effect of ship movement in the performance of the tracking filter, based on the adaptive extended Kalman filter (AEKF) was studied. The efficiency and effectiveness of the proposed algorithm are compared with the nearest neighbor (NN) with Mahalanobis distance and Fuzzy nearest neighbor (FNN) methods. The results demonstrate the main advantages of the proposed algorithm, including its simplicity and suitability for real-time target tracking in cluttered environments.

Data Mining-Based Tracking Method for Multisource Target Data of Heterogeneous Networks

“…Generally, the visible light sensor with higher accuracy has higher priority. Then, according to the change of data, the validity and stability of each priority data are analyzed [17]. Finally, on the basis of correctly tracking the target, the photoelectric tracking system selects the optimal sensor data for tracking.…”

Multiple hypothesis S‐dimensional assignment algorithm for data association of angle‐only sensors with limited fields of view

“…In the first stage, the threshold-based method (e.g. fuzzy clustering [18,35]) is used for coarser association, and then the optimisation method is used for finer association.…”

Research on Risk Assessment of Multitarget Wireless Sensing Detection Auxiliary Engineering in Mine Geological Environment

“…In largescale wireless sensor networks, nodes collect small packets of varying lengths, and multiple nodes waste a lot of resources by frequently interacting with aggregation nodes. Packet aggregation refers to the aggregation of multiple packets of shorter length to be forwarded and the packets to be sent by themselves to form an aggregated packet before transmission, which is very suitable for packet group forwarding in large-scale wireless sensor networks and can significantly reduce the transmission delay, reduce the energy consumption of forwarded data, save the power of nodes, and extend the network life cycle [5]. Therefore, this paper combines selective collaboration technology with packet aggregation technology and applies it to wireless sensor networks, which can effectively improve network performance, ensure reliable data transmission, and reduce the consumption of unnecessary network resources.…”