Aerospace Avionics and Allied Technologies

Avionics is a very crucial and important technology, not only for civil/military aircraft but also for missiles, spacecraft, micro air vehicles (MAVs) and unmanned aerial vehicles (UAVs). Even for ground-based vehicles and underwater vehicles (UWVs), avionics is a very important segment of their successful operation and mission accomplishment. The advances in many related and supporting technologies, especially digital electronics, embedded systems, embedded algorithms/software, mobile technology, sensors and instrumentation, computer (network)-communication, and realtime operations and simulation, have given a great impetus to the field of avionics. Here, for the sake of encompassing many other applications as mentioned above, the term is used in an expanded sense: Aerospace Avionics (AA), although it is popularly known as Aviation Electronics (or Avionics). However, use of this technology is not limited to aircraft, and hence, we  can incorporate all the three types-ground, land, and underwater vehicles-under the term avionics.Defence Science Journal, 2011, 61(4), pp.287-288, DOI:http://dx.doi.org/10.14429/dsj.61.112

Mobile Intelligent Autonomous Systems

Mobile intelligent autonomous systems (MIAS) is a fast emerging research area. Although it can be regarded as a general R&D area, it is mainly directed towards robotics. Several important subtopics within MIAS research are:(i) perception and reasoning, (ii) mobility and navigation,(iii) haptics and teleoperation, (iv) image fusion/computervision, (v) modelling of manipulators, (vi) hardware/software architectures for planning and behaviour learning leadingto robotic architecture, (vii) vehicle-robot path and motionplanning/control, (viii) human-machine interfaces for interaction between humans and robots, and (ix) application of artificial neural networks (ANNs), fuzzy logic/systems (FLS),probabilistic/approximate reasoning (PAR), Bayesian networks(BN) and genetic algorithms (GA) to the above-mentioned problems. Also, multi-sensor data fusion (MSDF) playsvery crucial role at many levels of the data fusion process:(i) kinematic fusion (position/bearing tracking), (ii) imagefusion (for scene recognition), (iii) information fusion (forbuilding world models), and (iv) decision fusion (for tracking,control actions). The MIAS as a technology is useful for automation of complex tasks, surveillance in a hazardousand hostile environment, human-assistance in very difficultmanual works, medical robotics, hospital systems, autodiagnosticsystems, and many other related civil and military systems. Also, other important research areas for MIAScomprise sensor/actuator modelling, failure management/reconfiguration, scene understanding, knowledge representation, learning and decision-making. Examples ofdynamic systems considered within the MIAS would be:autonomous systems (unmanned ground vehicles, unmannedaerial vehicles, micro/mini air vehicles, and autonomousunder water vehicles), mobile/fixed robotic systems, dexterousmanipulator robots, mining robots, surveillance systems,and networked/multi-robot systems, to name a few.Defence Science Journal, 2010, 60(1), pp.3-4, DOI:http://dx.doi.org/10.14429/dsj.60.9

Aircraft Parameter Estimation using Feedforward Neural Networks With Lyapunov Stability Analysis

Aerodynamic parameter estimation is critical in the aviation sector, especially in design and development programs of defense-military aircraft. In this paper, new results of the application of Artificial Neural Networks (ANN) to the field of aircraft parameter estimation are presented. The performances of Feedforward Neural Network (FFNN) with Backpropagation and FFNN with Backpropagation using Recursive Least Square (RLS) are investigated for aerodynamic parameter estimation. The methods are validated on flight data simulated using MATLAB implementations. The normalized Lyapunov energy functional has been used to derive the convergence conditions for both the ANN-based estimation algorithms. The estimation results are compared on the basis of performance metrics and computation time. The performance of FFNN-RLS has been observed to be approximately 10% better than FFNN-BPN. Simulation results from both algorithms have been found to be highly satisfactory and pave the way for further applications to real flight test data

Continuous TIME H-infinity Filter with Asymptotic Convergence

In this paper H-infinity, a posteriori filter (HIPF) is converted to a continuous time H-infinity (HI) filter. Then, an observer is presented which uses the gain and the state error Gramian from the continuous time HI filter (CTHF). Asymptotic stability result of the observer\u27s error dynamics is derived using the Lyapunov energy functional. The performance of the CTHF is evaluated using numerical simulations carried out in MATLAB. These results establish the local stability of the underlying CTHF. This type of result is novel in the literature on HI theory of filters and the observers