Real Time Mid-course Maneuver and Guidance of a Generic Reentry Vehicle

The aim of any mission is to accomplish the final objective with desired accuracy and the same is valid for a generic launch vehicle. In many missions it is necessary to execute mid-course maneuvers with an intentional diversion trajectory to create a counter measure or to avoid certain specific known geographical locations. The current work elaborates a novel and practically implementable mid-course maneuver and an ascent phase guidance of a reentry vehicle executing an in-flight determined mid-course maneuver (trajectory reshaping) without compromising the accuracy of the final achieved target position. The robustness of the algorithm is validated with 6DoF simulation results by considering the dispersion of the burnout state vector conditions which arises due to variations in thrust profile, aerodynamics characteristics of the vehicle, atmosphere, etc.Defence Science Journal, 2013, 63(4), pp.346-354, DOI:http://dx.doi.org/10.14429/dsj.63.420

Atmospheric Reentry Dispersion Correction Ascent Phase Guidance for a Generic Reentry Vehicle

Launch vehicle explicit guidance mechanism depends on the estimation of the desired burnout conditions and driving the vehicle to achieve these conditions. The accuracy of the vehicle at the target point depends on how tightly these conditions are achieved and what is the strategy used to define the trajectory. It has been observed inthe literature that most of the guidance mechanisms during reentry use vacuum guidance equations that is durin greentry the atmospheric effects are not considered. In order to achieve minimum miss distance at the target point theat mospheric effects are to be considered during the guided phase and appropriate corrections should be executed,otherwise depending on the reentry flight path angle and ballistic coefficient the errors can be as high as tens of nautical miles. In this paper, the authors develop a novel approach to these vacuum guided launch vehicle problems.The paper elaborates how to calculate a prior the reentry dispersion during the ascent phase guidance and provide guidance corrections such that the terminal conditions are achieved with higher accuracy.Defence Science Journal, 2013, 63(3), pp.233-241, DOI:http://dx.doi.org/10.14429/dsj.63.373

Comparison of Accuracy Measures for RS Image Classification using SVM and ANN Classifiers

The accurate land use land cover (LULC) classifications from satellite imagery are prominent for land use planning, climatic change detection and eco-environment monitoring. This paper investigates the accuracy and reliability of Support Vector Machine (SVM) classifier for classifying multi-spectral image of Hyderabad and its surroundings area and also compare its performance with Artificial Neural Network (ANN) classifier. In this paper, a hybrid technique which we refer to as Fuzzy Incorporated Hierarchical clustering has been proposed for clustering the multispectral satellite images into LULC sectors. The experimental results show that overall accuracies of LULC classification of the Hyderabad and its surroundings area are approximately 93.159% for SVM and 89.925% for ANN. The corresponding kappa coefficient values are 0.893 and 0.843. The classified results show that the SVM yields a very promising performance than the ANN in LULC classification of high resolution Landsat-8 satellite images

Online Trajectory Reshaping for a Launch Vehicle to Minimize the Final Error Caused by Navigation and Guidance

Autonomous launch vehicles, once lifted off from the launch pad, equipped with an onboard intelligence which aids in achieving the mission objectives with high accuracy. The accuracy of the mission depends basically on navigation and guidance errors caused at burnout condition, after which the vehicle follows an elliptical path upto impact. The paper describes how to handle the final impact and injection error caused by these navigation and guidance errors. In the current work the initial burnout conditions are tuned and corrected such that the terminal impact point is achieved within the desired tolerance bounds. A two point boundary value problem is solved using the gradient method, for determining the impact errors. The algorithm is validated by simulation studies for various burnout conditions.Defence Science Journal, 2013, 63(3), pp.254-261, DOI:http://dx.doi.org/10.14429/dsj.63.241

Real-time Face Recognition Using SIMD and VLIW Architecture

There is a rapidly growing demand for using intelligent cameras for various applications in surveillance and identification. Most of these applications have real-time demands and require huge processing capacity. Face recognition is one of those applications highly in demand. In this paper we show that we can run face recognition in real-time by implementing the algorithm on an architecture which combines a massively parallel processor with a high performance Digital Signal Processor. In this paper we focus on the INCA+ intelligent camera. It contains a CMOS sensor, a Single Instruction Multiple Data (SIMD) processor [1] and a Very Long Instruction Word (VLIW) processor. The SIMD processor enables high-performance pixel processing and detects the interesting (face) regions from the video. It sends the regions of interest to the VLIW processor, which performs the actual face recognition using a neural network. With this architecture we perform face recognition from a 5-persons database at more than 200 faces per second. The performance is better than most recent high-end professional systems [2]