A Multiple Radar Approach for Automatic Target Recognition of Aircraft using Inverse Synthetic Aperture Radar

Along with the improvement of radar technologies, Automatic Target Recognition (ATR) using Synthetic Aperture Radar (SAR) and Inverse SAR (ISAR) has come to be an active research area. SAR/ISAR are radar techniques to generate a two-dimensional high-resolution image of a target. Unlike other similar experiments using Convolutional Neural Networks (CNN) to solve this problem, we utilize an unusual approach that leads to better performance and faster training times. Our CNN uses complex values generated by a simulation to train the network; additionally, we utilize a multi-radar approach to increase the accuracy of the training and testing processes, thus resulting in higher accuracies than the other papers working on SAR/ISAR ATR. We generated our dataset with 7 different aircraft models with a radar simulator we developed called RadarPixel; it is a Windows GUI program implemented using Matlab and Java programming, the simulator is capable of accurately replicating a real SAR/ISAR configurations. Our objective is to utilize our multi-radar technique and determine the optimal number of radars needed to detect and classify targets.Comment: 8 pages, 9 figures, International Conference for Data Intelligence and Security (ICDIS

The art of chicken sexing

Expert chick sexers are able to quickly and reliably determine the sex of day-old chicks on the basis of very subtle perceptual cues. They claim that in many cases they have no idea how they make their decisions. They just look at the rear end of a chick, and ‘see’ that it is either male or female. This is somewhat reminiscent of those expert chess players, often cited in the psychological literature, who can just ‘see’ what the next move should be; similar claims have been made for expert wine tasters and experts at medical diagnosis. All of these skills are hard-earned and not accessible to introspection. But is there really anything unusual about the chicken sexer, the chess grand master, the wine buff or the medical expert? I argue that there is not. In fact, we are all constantly making categorizations of this sort: we are highly accurate at categorizing natural kinds, substances, artefacts, and so on. We do so quickly and subconsciously, and the process is completely inaccessible to introspection. The question is, why is it so difficult to acquire skills such as chicken sexing, when we automatically acquire the ability to categorize other objects. In this paper, I argue that we have mechanisms for learning the cues necessary for categorization, but that these mechanisms require selective attention to be given to the relevant features. We automatically acquire the ability to categorize certain objects because we have inbuilt attention directors causing us to attend to diagnostic cues. In cases such as chicken sexing, where we do not automatically develop categorization abilities, our inbuilt attention directors do not cause us to attend to diagnostic cues, and out attention therefore has to be drawn to these cues in another way, such as through training

Fault tolerant architectures for integrated aircraft electronics systems, task 2

The architectural basis for an advanced fault tolerant on-board computer to succeed the current generation of fault tolerant computers is examined. The network error tolerant system architecture is studied with particular attention to intercluster configurations and communication protocols, and to refined reliability estimates. The diagnosis of faults, so that appropriate choices for reconfiguration can be made is discussed. The analysis relates particularly to the recognition of transient faults in a system with tasks at many levels of priority. The demand driven data-flow architecture, which appears to have possible application in fault tolerant systems is described and work investigating the feasibility of automatic generation of aircraft flight control programs from abstract specifications is reported

Air Traffic Management Safety Challenges

The primary goal of the Air Traffic Management (ATM) system is to control accident risk. ATM safety has improved over the decades for many reasons, from better equipment to additional safety defences. But ATM safety targets, improving on current performance, are now extremely demanding. Safety analysts and aviation decision-makers have to make safety assessments based on statistically incomplete evidence. If future risks cannot be estimated with precision, then how is safety to be assured with traffic growth and operational/technical changes? What are the design implications for the USA’s ‘Next Generation Air Transportation System’ (NextGen) and Europe’s Single European Sky ATM Research Programme (SESAR)? ATM accident precursors arise from (eg) pilot/controller workload, miscommunication, and lack of upto- date information. Can these accident precursors confidently be ‘designed out’ by (eg) better system knowledge across ATM participants, automatic safety checks, and machine rather than voice communication? Future potentially hazardous situations could be as ‘messy’ in system terms as the Überlingen mid-air collision. Are ATM safety regulation policies fit for purpose: is it more and more difficult to innovate, to introduce new technologies and novel operational concepts? Must regulators be more active, eg more inspections and monitoring of real operational and organisational practices