Aerospace and Electronic Systems Magazine August 2016 - 43

Griffiths be made in geolocation and synchronization techniques (especially in GPS-denied environments), in communication between platforms, and in resource management techniques for radar networks. Bistatics and Networked Radar offer both challenges and opportunities. Networked radar - also known variously as distributed radar, netted radar, MIMO, and multiple bistatic radars - is a topic that has been around for some time but is still relatively immature, and definitions are imprecise, even where they exist at all. There are essentially two forms of MIMO radar: in statistical MIMO, spatially separated transmit and receive antenna elements form a distributed radar; in coherent MIMO, the antenna elements radar are constrained within a single manifold and the radar uses orthogonally-coded waveforms from each transmitter to provide multiple simultaneous transmit-receive paths. Separation of transmitters and receivers brings new design freedoms but also adds technical and logistical challenges. Compressive sensing has become something of a hot topic in recent years. It involves a digital signal processing technique that provides for efficiently acquiring and reconstructing a signal, by finding solutions to underdetermined linear systems. It stands to reason that signals or images that are 'sparse' over part of their domain do not need to be sampled uniformly, but there remain some rather important questions, particularly about its applicability to real-world problems. Compressive sensing is based on the principle that, through an optimization technique, the sparsity of a signal can be exploited to recover it from fewer samples than required by the Shannon-Nyquist sampling theorem. The presentation posed a number of questions which are listed here. Has compressive sensing delivered on its original promise to significantly reduce sampling rate ? Does this not lead to a drop in detection performance ? Was Nyquist a pessimist ? Surely the radar community has been dealing with sparse/non-uniform sampling for many years under the heading of Array Processing? What if the signal is off-grid or the dictionary is not correct ? Is greed really good or should we iterate to avoid indigestion? Does compressive sensing give us a fundamentally new way of addressing radar signal processing challenges that throws off the shackles of approximate orthogonality and allows us to forget sidelobes and even the ambiguity function? The presentation threw some more light on some of these questions through the illustration of a simple example that is indicative of both airborne pulsed-Doppler GMTI and SAR image formation. The conclusion is probably unsurprising - that the technique has much to offer provided that the signal or image is suitably sparse. Cognitive Radar represents an exciting and potentially farreaching set of techniques [4]. A general objective for cognitive radar research is to transition the cognitive processes that are currently performed by an operator into automated processes in a radar system. Consequently, cognitive radar can reduce the operator's cognitive responsibilities by acquiring and intelligently exploiting knowledge on the situations that the radar may encounter; however, to achieve this objective, it is necessary to overcome a number of technological challenges. Even if these technological challenges can be overcome, the development of cognitive processes may be limited by practical limitations, such as legal constraints or new types of drawbacks associated to cognition. Although it is relatively new, the ideas behind the concept can be identified in research and operational systems that pre-date the concept by at least a decade. As well AUGUST 2016 as the advantages, potential drawbacks should be considered in cognitive radar system design, and these may limit the potential applications. A key enabler of cognitive radar is the a priori information made available to the radar to allow this novel processing, and the DARPA KASSPER (Knowledge Aided Sensor Signal Processing and Expert Reasoning) program was a key trailblazer in this respect. Adoption of cognitive techniques will be easiest for capabilities that a human cannot do or does badly. Automating capabilities that humans are good at will be slower to adopt and potentially limited by legal or trust concerns. In addition, cognitive radar research must draw on other disciplines, such as robotics, convex optimisation, machine learning, information fusion, control, and operations research. Finally, a system view of a cognitive radar system should be taken, where modules may not be cognitive in isolation but contribute to cognitive behaviour of a complete system. Multifunction RF Systems depend critically on the evolution of the active electronically scanned array (AESA) and the associated enabling technologies, and on new operational needs of radars, and particularly air defence systems. These are made possible by new technologies such as photonics and Gallium Nitride (GaN) semiconductor devices. We may now consider multifunctional RF systems which integrate radar, communications, electronic support measures, electronic countermeasures and electronic attack in a single system and antenna aperture. The new systems can be installed on land, naval, and air platforms (including drones and remotely piloted vehicles). A study of the evolution of such technologies [5] shows that they often take the form of a series of S-curves. This indicates that research and development effort may need to be harnessed over several decades in order to deliver clear benefit, and this should be approached strategically. Bringing down the cost is another major factor; in the telecommunications market the investment to make such advances are justified by the huge production volumes, but in military radars this is much more difficult. Looking to the future, important issues are identified as: C C C Wide spectrum of applications for multifunction RF systems, Open architecture and standardization, Exploitation of commercial technology development (e.g.: COTS) without redoing the system architecture, C Management of legacy systems, C Dual-use, C C C New methodologies of conception, design, prototype, program management, production, tests, training, maintenance (virtual reality, ...), remote logistics (3D printing, ...), porting of functionalities (produced data and C^2) on Android devices, SWAP-C (Size, Weight, and Power - Cost) just one development for more system applications, DARPA program: Arrays at Commercial Timescales (ACT). "How do we reduce the NRE (Non-recurring engineering) cost of array development?" IEEE A&E SYSTEMS MAGAZINE 43

Table of Contents for the Digital Edition of Aerospace and Electronic Systems Magazine August 2016