Target Tracking Process Research Articles

Sensor Data Fusion for Accurate Cloud Presence Prediction Using Dempster-Shafer Evidence Theory

Sensor data fusion technology can be used to best extract useful information from multiple sensor observations. It has been widely applied in various applications such as target tracking, surveillance, robot navigation, signal and image processing. This paper introduces a novel data fusion approach in a multiple radiation sensor environment using Dempster-Shafer evidence theory. The methodology is used to predict cloud presence based on the inputs of radiation sensors. Different radiation data have been used for the cloud prediction. The potential application areas of the algorithm include renewable power for virtual power station where the prediction of cloud presence is the most challenging issue for its photovoltaic output. The algorithm is validated by comparing the predicted cloud presence with the corresponding sunshine occurrence data that were recorded as the benchmark. Our experiments have indicated that comparing to the approaches using individual sensors, the proposed data fusion approach can increase correct rate of cloud prediction by ten percent, and decrease unknown rate of cloud prediction by twenty three percent.

Auction-Based Consensus Mechanism for Cooperative Tracking in Multi-Sensor Surveillance Systems

This paper presents an auction-based consensusmechanism for cooperative targets tracking using minimum numbers of mobile sensors in order to reduce energy consumption due to sensor mobilization. After targets are detected, they are clustered using hybrid subtractive K-means clustering technique to reduce the number of trackers needed to track these detected targets. The proposed target tracking process is based on an Extended Kohonen neural network. In order to decrease the network sensitivity to initial conditions, a supervised learning technique is used to get the initial weights of unsupervised Extended Kohonen Map instead of random initialization. An auction-based consensus mechanism is used as a cooperation methodology between trackers during tracking. Monitoring sensors either remain stationary or begin following their targets is based on this mechanism. The simulation results confirms that the proposed approach outperforms other approaches in energy saving and achieves better coverage.

Artificial Neural Networks and Their Application to Weapons

ABSTRACTComplex processing algorithms associated with requirements for real‐time target detection, acquisition and recognition have far outdistanced our ability to package the necessary processing power into real‐time weapon‐sized hardware. Weapon developers need new, radically different, approaches to solve these difficult weapon signal processing problems. Artificial neural networks (ANN) is one of the new, radically different, signal processing approaches that is currently receiving much attention for weapon applications. The human brain is a powerful image and pattern recognition processor whose basic processing element is the neuron. Synapses are the weighted interconnections between neurons that permit learning and communication between the neurons. Artificial neural network technology attempts to mathematically and/or electrically model neurons and synapses and then interconnect these models in architectures suitable for signal processing tasks.ANN technology is particularly applicable to pattern recognition, speech recognition, machine vision, robotics, and optimization signal processing tasks. Specific military applications include missile seekers, missile fuzing, sonar target discrimination, automatic target recognition, and autopilots. Two unique characteristics of ANN processors are that they are non‐linear processors and that they are trained, not programmed, to accomplish processing tasks in a manner analogous to the way the human brain learns. Learning is achieved by modifying the synaptic weights of each artificial neuron until the final desired system processing response is achieved.Several years ago the Naval Weapons Center (NavWpnCen) identified ANN technology as a high risk‐high payoff approach to missile signal processing requirements and initiated several research and development efforts in this area. One result of this work is the new 80170NW ANN analog VLSI chip produced by Intel Inc. Another is the current Missileborne Integrated Neural Network Demonstration (MINND) Advanced Technology Demonstration (ATD) project to develop a hybrid analog/digital artificial neural computer for real‐time processing of data from imaging missile seekers. The neural computer will use several Intel chips interconnected in a flexible architecture, which is loosely based on the operation of the human retina, to demonstrate desired target/background discrimination and target detection and tracking processing capabilities. This paper provides an introduction to artificial neural network technology and discusses some of the results that have been achieved by applying ANN technology to weapon system problems at the Naval Weapons Center.