Detection, discrimination and tracking of a target under a given dynamic environment is one of the main challenges of an integrated sensory system. A combination of two or more sensors will always provide a better position estimate rather than a single sensor. The advantages of multi-sensor data fusion over a conventional single sensor tracking are presented in this paper. Using state estimators from simple linear rustic filters to a complex nonlinear filter, the tracking of target performing three different motions with sensor noises are presented in this paper. RADAR and the infrared search and track (IRST) are the two sensors considered based on which a complete mathematical modelling and simulation of the sensor measurements and tracking methodologies are utilised. The extension of the paper also presents the image fusion techniques using a widely known technique known as principle component analysis method. The RMS errors in position as well as image error measurements are performed that shows the superiority of the multi sensor data fusion process.
Detection, discrimination and tracking of a target under a given dynamic environment is one of the main challenges of an integrated sensory system. A combination of two or more sensors will always provide a better position estimate rather than a single sensor. The advantages of multi-sensor data fusion over a conventional single sensor tracking are presented in this paper. Using state estimators from simple linear rustic filters to a complex nonlinear filter, the tracking of target performing three different motions with sensor noises are presented in this paper. RADAR and the infrared search and track (IRST) are the two sensors considered based on which a complete mathematical modelling and simulation of the sensor measurements and tracking methodologies are utilised. The extension of the paper also presents the image fusion techniques using a widely known technique known as principle component analysis method. The RMS errors in position as well as image error measurements are performed that shows the superiority of the multi sensor data fusion process.
This paper deals with the theoretical aspect of bat echolocation and bionics, and image processing-based target recognition and identification methods. The state estimation methods utilizing the linear rustic filters such as fixed gain and Kalman filters are studied and implemented for echolocation bionics for estimating the LOS distance. A complete mathematical modeling and simulation of bat dynamics and its prey are presented upon which the relative LOS distance is reconstructed with state estimators and less RMS errors. Also, target recognition and identification using Optical, IR Digital Night Vision and Thermal camera is studied and implemented at different environmental conditions to demonstrate the superiority of thermal camera.
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