Fusion of IRST and Radar Measurements for 3D Target Tracking

Two different types of measurement fusion methods for fusing IRST (infrared search and track) and radar measurements to track a target in 3D Cartesian coordinates are evaluated and discussed in this paper. Performance evaluation metrics were provided to evaluate the tracking algorithm. It was observed that both the fusion algorithms are performed alike. Proof was provided to show that both the methods are functionally similar

Detection of Airport Runway Edges using Line Detection Techniques

Airport runway detection is a vital aspect for both military and commercial applications. An algorithm to extract runway edges based on edge detection and line detection techniques is discussed. The runway images are initially enhanced by dilation, thresholding and edge detection. Based on some unique characteristics like the runway being gray with two white lines indicating the runway boundaries, long and continuous edges of the runway are considered to be straight lines. The straight lines are detected using Convolution operators pertaining to vertical, 45° or -45° lines. Hough Transform is then applied to fit only the pair of lines corresponding to the runway boundaries in certain orientations. The test results prove that combination of Convolution and Hough transform is very competent in detecting runway edges accurately

Implementation and Validation of Video Stabilization using Simulink

A fast video stabilization technique based on Gray-coded bit-plane (GCBP) matching for translational motion is implemented and tested using various image sequences. This technique performs motion estimation using GCBP of image sequences which greatly reduces the computational load. In order to further improve computational efficiency, the three-step search (TSS) is used along with GCBP matching to perform a competent search during correlation measure calculation. The entire technique has been implemented in Simulink to perform in real-time

Implementation of IMMPDAF Algorithm in LabVIEW for Multi Sensor Single Target Tracking

Real time IMMPDAF algorithm has been implemented and tested in LabVIEW. Single aircraft flight profiles have been simulated and the plot data from multiple radars observing the single aircraft are generated with noise as well as clutter. The performance of the algorithm is evaluated using standard procedures. Since it is implemented and tested in LabVIEW, this algorithm can be easily realized in hardware for real time tracking applications

Bearing Fault Diagnosis using DWT& SVM

Bearings are very critical components in all rotating machines used in the majority of the industries. Vibration analysis based condition monitoring is one of the best approaches for maintenance and diagnosing the faults in the rotating machinery. This paper deals with the vibration-based health condition-monitoring techniques used for bearing fault diagnosis. Discrete wavelet transform (DWT) and support vector machines (SVM) have been presented for the statistical feature extraction and fault classification of the bearings respectively. The useful features from normalized wavelets energy analysis and wavelets variance have been extracted. The results reveal that the vibration based health condition monitoring method is successful in fault diagnosis and clear classification of bearing faults using DWT and SVM

Gearbox Health Condition Monitoring: Vibration Analysis

Vibration analysis is a standout amongst the most effective strategies present for diagnosing the health condition of rotating machinery. This paper deals with the vibration based gearbox health condition-monitoring techniques for diagnosing and classifying the gearbox faults. The statistical feature extraction has been done using the discrete wavelet transform whereas the fault classification has been done using support vector machine. The results reveal that these vibration based diagnostic techniques are successful in gearbox health condition monitoring. National Renewable Energy Laboratory (NREL) provided the data used in this paper

Gearbox Health Condition Monitoring: A brief exposition

Gearbox is a mechanical power transmission device, most commonly used to get the mechanical benefits in terms of speed and torque. The gearbox is made up of different types of gears assembled in a cascading order to perform the intended task. Failure of any rotating component inside the gearbox will terminate the working condition of the mechanical system associated with it. This causes interrupted services to the industries, which lead to expensive compensation. Especially, in an aircraft engine, it is used as an accessory drive, which provides power for hydraulic,pneumatic and electrical systems. This motivated to monitor the gearbox health condition. This paper presents a brief review of GHCM (gearbox health condition monitoring), gearbox faults, overview of time-domain features, frequency-domain features, time-frequency domain; feature extraction techniques, and fault classification techniques.The outcome of this study is to provide brief information regarding gearbox health condition monitoring

Vibration Analysis of Heterogeneous Gearbox Faults using EMD Features and SVM Classifier

Gearbox is one of the important mechanical power transmission device most commonly used in automobiles and industries to get the desired change in speed and torque. The gearbox fault diagnosis has given utmost importance for its significance in preventing halts of a mechanical system and guaranteeing an advantage of sufficient maintenance. This paper presents the vibration analysis of heterogeneous gearbox faults using EMD features and SVM classifier. The vibration signal is converted into intrinsic mode functions (IMF) with decreasing order of frequencies using empirical mode decomposition (EMD) method. Feature vector consisting of information theoretic features have been computed for each IMF and concatenated to form a feature set. By using random permutations, the feature set has been divided into training and testing sets. The support vector machine (SVM) algorithm has been used as a classification technique to diagnose the gearbox faults, which consists of five-class classification. The accuracy of the developed algorithm has been validated using 100 Monte Carlo runs. A comparative study has been carried between computed features and varying IMF components. The observations made were - clear discrimination of the gearbox faults and improved classification accuracy, which contain - chipped tooth, missing tooth, root fault, surface fault and healthy working state of the gear

Implementation and Validation of Visual and Infrared Image Fusion Techniques in C# .NET Environment

This paper presents the implementation of image fusion techniques by means of an image fusion application “C#ImFuse”, developed in C#.NET. C# programming language is a simple, type-safe, object-oriented language that allows programmers to build a variety of applications. C#ImFuse application implements four fusion methods viz., Alpha Blending (AB), Principle Component Analysis (PCA), Laplacian Pyramid (LP), and Discrete Wavelet Transform (DWT) for a visual and a thermal image (still images) and for real-time images of the Enhanced Vision System (EVS). The performance of these fusion techniques is evaluated using fusion performance metrics. LP based image fusion technique proved to provide better fusion when compared to the other techniques. Source code is provided so that the reader can understand the techniques and use for his research work

A Brief Exposition on Brain-Computer Interface

Brain-Computer Interface is a technology that records brain signals and translates them into useful commands to operate a drone or a wheelchair. Drones are used in various applications such as aerial operations, where pilot’s presence is impossible. The BCI can also be used for patients suffering from brain diseases who lose their body control and are unable to move to satisfy their basic needs. By taking advantage of BCI and drone technology, algorithms for Mind-Controlled Unmanned Aerial System can be developed. This paper deals with the classification of BCI & UAV, methodologies of BCI, the framework of BCI, neuro-imaging methods, BCI headset options, BCI platforms, electrode types & their placement, and the result of feature extraction technique (FFT) with 72.5% accuracy