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Gini F., Rangaswamy M. (eds.) Knowledge-Based Radar Detection, Tracking, and Classification

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Gini F., Rangaswamy M. (eds.) Knowledge-Based Radar Detection, Tracking, and Classification
Издательство John Wiley, 2008, -288 pp.
The use of surveillance for a variety of applications in the dynamically changing civilian and military environments has led to a great demand for innovative sensors and sensing configurations based on cutting-edge technologies, such as knowledge-based (KB) signal and data processing, waveform diversity, wireless networking, robotics, advanced computer architectures, and supporting software languages [1]. Improved sensor signal and data processing performance will be gained from KB and a priori information, multiple processing paradigms, and sensor fusion. A knowledge-based system (KBS) uses a priori information to improve the performance of deterministic and adaptive systems. Although the exact form of this prior knowledge is problem-dependent, a KBS consists of a knowledge base containing information specific to a problem domain and an inference engine that employs reasoning to yield decisions.
With maturing electronics and radar hardware, advanced radar systems will use KB techniques to perform signal and data processing cooperatively within and between platforms of sensors and communication systems while exercising waveform diversity, as well as reconnaissance, surveillance, imaging and communications within the same sensor system. In addition, these sensors will cooperate with other users and sensors, sharing information and data. Sensor system performance can be enhanced by changing a sensor’s algorithms as the environment changes. This is the fundamental concept underlying KB or cognitive radar, known to the radar community since the pioneering papers of Vannicola and colleagues [2, 3], Haykin [4], and Baldygo et al. [5]. The operational radar environment is subject to rapid spatio-temporal variation. Hence, the key to efficient adaptation is real-time exploitation of a priori knowledge pertaining to the operational environment. For example, if an airborne radar system is aware of certain features of the Earth and its surroundings, then it can significantly improve performance by exploiting degrees of freedom such as the transmit waveform, polarization, frequency, phase, power, modulation, and coding. The adaptive and optimal use of all available degrees of freedom is broadly termed waveform diversity. Waveform diversity is the technology that will allow one or more sensors onboard a platform to automatically change operating parameters [e.g. frequency, gain pattern, pulse repetition frequency (PRF)] to meet the varying environments. Also, the system of sensors should operate with multiple goals managed by an intelligent platform network that can control the dynamics of each sensor to meet the common goals of the platform, rather than each sensor operate as an independent system. Intelligent software processing is required at all stages of signal, data, and systemprocessing fromthe filtering, detection, tracking, imaging, and identification stages to the communications, command, and control (C3) stages. Examples of a priori knowledge are archival radar data, Geographic Information Systems (GISs), Digital Terrain Elevation Data (DTED), Land Cover Land Use (LCLU) data, information on the radar kinematical parameters, off-board sensor data, roadway maps, and background of air/surface traffic. Recent advances in environmental measurements, DTED, future information quality and accessibility, digital processing, mass and random-access memory technologies, have opened up many possibilities, unrealizable in the past, for radar systems to improve their on-line performance. New real-time processing techniques are required for [e.g. for the constant false alarm rate (CFAR) behavior of the radar system [6]] to take advantages of these advances to bring radar performance back to optimum under difficult operation conditions such as littorals that include mixed sea and variable terrain.
The great interest in the application of KB techniques to adaptive radar signal and data processing is evident from the following examples:
The Defence Advanced Research Projects Agency (DARPA) has been pioneering the development of the first ever real-time knowledge-aided adaptive radar architecture. In particular, the Knowledge Aided Sensor Signal Processing and Expert Reasoning (KASSPER) program has as its aim the development and application of a revolutionary new approach to demanding multidimensional adaptive sensor systems, with a near-term focus on military applications of Ground Moving Target Indicator (GMTI) radar and Synthetic Aperture Radar (SAR). Annual KASSPER workshops started in 2002 to allow the exchange of ideas across the spectrum of R&D activities, including knowledge-based space–time adaptive processing (KB-STAP), environmental knowledge-base generation and maintenance, and real-time KB embedded computing [7].
The US Air Force Research Laboratory’s Sensors Directorate has been pursuing some of the most progressive work in employing KB techniques in the radar signal processing chain, specifically in the CFAR portion of the chain [5, 8].
The US Air Force (USAF) has an ongoing project called Autonomous Intelligent Radar System (AIRS) that is performing research in applying KB techniques to radar signal processing. The AIRS architecture design leverages advanced technologies developed by the World Wide Web Consortium (W3C) and the DARPA Agent Markup Language (DAML) program to define the next-generation Internet, also called the Semantic Web [9].
A series of lectures has been devoted to Knowledge-Based Radar Signal and Data Processing [10]. They were sponsored by the NATO Research and Technology Organization (RTO) with the following scope: promoting cooperative research and information exchange to support the development and effective use of national defense research and technology to meet the military needs of the alliance; maintaining a technological lead; and providing advice to NATO decision makers. This Lecture Series was held in Sweden, Hungary, and Italy in 2003; Poland and Spain in 2004; and in the Czech Republic, Belgium, and the UK in 2006.
A special section of the IEEE Signal Processing Magazine devoted to Knowledge-Based Systems for Adaptive Radar: Detection, Tracking, and Classification, published in January 2006, edited by Fulvio Gini [11].
A special section of IEEE Transactions on Aerospace and Electronic Systems devoted to Knowledge-Aided Sensor Signal and Data Processing, published in July 2006, co-edited by William Melvin and Joseph Guerci [12].
The aim of this book is to highlight recent advances in both knowledge-based systems and radar signal and data processing, in a common forum, in order to present a range of perspectives and innovative results with potential to enable practical adaptive radar systems design. The chapters of this book describe the current developments in the area and present examples of improved radar performance for augmented and upgraded systems, and project the impact of KB technology on future systems.
Introduction.
Cognitive Radar.
Knowledge-Based Radar Signal and Data Processing: A Tutorial Overview.
An Overview of Knowledge-Aided Adaptive Radar at DARPA and Beyond.
Space–Time Adaptive Processing for Airborne Radar: A Knowledge–Based Perspective.
CFAR Knowledge-Aided Radar Detection and its Demonstration Using Measured Airborne Data.
STAP via Knowledge-Aided Covariance Estimation and the FRACTA Meta-Algorithm.
Knowledge-Based Radar Tracking.
Knowledge-Based Radar Target Classification.
Multifunction Radar Resource Management.
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