Multi-sensor Information Fusion Technology Applied to the Development of Smart Aircraft

نویسنده

  • Allan Whittaker
چکیده

This paper explores the possibility of applying multi-sensor information fusion technology to the development of smart aircraft. This technology integrates information from multiple sensors and extracts tactical information to detect, track and identify time critical targets at any time, in any place and under all weather conditions. Such target information will help the war fighter avoid fratricide and deploy weapon systems for surgical strike. Finally, the smart aircraft helps the war fighter establish air superiority in the air, on the land and at sea. The architecture of a smart aircraft includes the pilot and flight crew, vehicle interface unit, multi-sensor correlation processor, multi-sensor image fusion model, multi-sensor track fusion model, multiple target identification fusion model and multiple sensors such as: Radar, CNI, EW, IFF Visual, Electro Optic and IR. The multi-sensor track fusion model computes a fused track from the sensor trackers. A Multiple Target Identification Fusion model creates integrated target identifications from identification of multiple targets. Similarly, a Multi-Sensor Image Fusion model creates an integrated target image from multiple sensor images. In traditional aircraft, the pilot and crew extract tactical information from a huge amount of multi-sensor information. This processing is time consuming and subject to human error. A future smart aircraft with Multi-Sensor Information Fusion technology will have access to accurately correlated fused information. Fused information assists the pilot and crew to detect, track and identify targets more rapidly and accurately. Such platforms are truly smart aircraft.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Multi-Focus Image Fusion in DCT Domain using Variance and Energy of Laplacian and Correlation Coefficient for Visual Sensor Networks

The purpose of multi-focus image fusion is gathering the essential information and the focused parts from the input multi-focus images into a single image. These multi-focus images are captured with different depths of focus of cameras. A lot of multi-focus image fusion techniques have been introduced using considering the focus measurement in the spatial domain. However, the multi-focus image ...

متن کامل

A New Approach to Self-Localization for Mobile Robots Using Sensor Data Fusion

This paper proposes a new approach for calibration of dead reckoning process. Using the well-known UMBmark (University of Michigan Benchmark) is not sufficient for a desirable calibration of dead reckoning. Besides, existing calibration methods usually require explicit measurement of actual motion of the robot. Some recent methods use the smart encoder trailer or long range finder sensors such ...

متن کامل

A New Fault Tolerant Nonlinear Model Predictive Controller Incorporating an UKF-Based Centralized Measurement Fusion Scheme

A new Fault Tolerant Controller (FTC) has been presented in this research by integrating a Fault Detection and Diagnosis (FDD) mechanism in a nonlinear model predictive controller framework. The proposed FDD utilizes a Multi-Sensor Data Fusion (MSDF) methodology to enhance its reliability and estimation accuracy. An augmented state-vector model is developed to incorporate the occurred senso...

متن کامل

Implementation of a Low- Cost Multi- IMU by Using Information Form of a Steady State Kalman Filter

In this paper, a homogenous multi-sensor fusion method is used to estimate the trueangular rate and acceleration with a combination of four low cost (< 10$) MEMS Inertial MeasurementUnits (IMU). An information form of steady state Kalman filter is designed to fuse the output of four lowaccuracy sensors to reduce the noise effect by the square root of the number of sensors. A hardware isimplemen...

متن کامل

Uncertainty Measurement for Ultrasonic Sensor Fusion Using Generalized Aggregated Uncertainty Measure 1

In this paper, target differentiation based on pattern of data which are obtained by a set of two ultrasonic sensors is considered. A neural network based target classifier is applied to these data to categorize the data of each sensor. Then the results are fused together by Dempster–Shafer theory (DST) and Dezert–Smarandache theory (DSmT) to make final decision. The Generalized Aggregated Unce...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2002