From a different perspective: principles, practice and potential of bistatic radar 

Bistatic radar systems have been studied and built since the earliest days of radar. They have the advantages that the receivers are passive, and hence undetectable. The receiving systems are also potentially simple and cheap. Bistatic radar may have a counter-stealth capability, since target shaping to reduce monostatic RCS will in general not reduce the bistatic RCS. In spite of those advantages, rather few bistatic radar systems have got past the 'technology demonstrator' phase. It has also been remarked that activity in bistatic radar tends to vary on a period of approximately fifteen years, and that currently we are at a peak of that cycle; there is particular current interest in passive coherent location (PCL) techniques, using broadcast and communications signals as 'illuminators of opportunity'. The presentation will review some of the history and current developments in the subject, and conjecture whether or not the presesent interest is just another peak in the cycle.

Hugh Griffiths is Head of the Department of Electronic and Electrical Engineering at University College London. He received the MA degree in Physics from Oxford University in 1978, then worked with Plessey Electronic Systems Research, Roke Manor. He joined UCL in 1982, where he received his PhD in Electronic Engineering in 1986, and the DSc(Eng) degree in 2000. His research interests include radar and sonar systems and signal processing, radar remote sensing, and antenna measurement techniques, and he has published over 200 papers on these subjects. In 1996 he received the IEEE AESS Nathanson Award. He is a Fellow of the IEE and of the IEEE, and in 1997 he was elected to Fellowship of the Royal Academy of Engineering. 

Michael C Wicks, PhD, AFRL Sensors 

Directorate Radar the Next Generation - Sensors as Robots  

The objective of our research into Sensors as Robots is to develop a safe, cost effective, and extendable approach for providing surveillance for a variety of applications in dynamically changing civilian and military environments. Radar sensors and systems in general are not automatically integrated within the platform for which they reside. They do not share nor receive information from other systems on the same platform, or between platforms. Sensor platforms, for the most part, are a heterogeneous collection of disjointed sensors. However, the intelligent integration of multiple sensor data provides information. The user community is data rich and information poor when it comes to raw data from sensor systems. We need to have more sensor systems automatically share data so that their composite result provides maximally useful information to the user.

Historically, radar systems have been built given a fixed set of requirements. As such, bounds on operational flexibility have been set. Embedded processing algorithms were derived under assumptions that may no longer apply. As a result, these algorithms may not be well-matched to contemporary needs. Current fielded radar system software is difficult and costly to change or port to new processors since it is often computer platform dependent. Most radar sensor systems operate stand alone and do not communicate with other systems except for handoff; and generally perform single mode operations. In the future, simultaneous multi-mission multi-mode operation will be required. 

In Sensors as Robots, we envision a new sensor archetype. In this paradigm, sensors and algorithms will be autonomously changed depending on the environment. Radars will use the same returns to perform detection and discrimination, to alter the platform flight path and change mission priorities. The sensors will dynamically and automatically change waveform parameters to accomplish these goals. Dissimilar sensors will communicate and share data and instructions in real-time. Intelligent sensor systems will exist within and between sensor platforms such that the integration of multiple sensor data provides information needed to achieve dynamic goals and avoid electromagnetic fratricide. Intelligent sensor platforms working together will increase information flow, minimize ambiguities, and dynamically change multiple sensors' operations based upon a changing environment. Concomitant with the current emphasis on a more flexible defense structure, Sensors as Robots allows the incremental application of remote sensing assets by matching resources to the application. 

Our targeted application for Sensors as Robots is the Unmanned Airborne Vehicle, or UAV. In the regime of UAV-based remote sensing, we envision the ability to detect and exploit observable phenomena in the "Long, mid and near range" by mimicking the integrated use of the five senses plus memory and exploiting our understanding of human, animal and insect behavior for deployment and operation. Improved sensor signal and data processing will be gained from knowledge base and "a priori" information, multiple processing paradigms, and sensor fusion. Through the use of all available sensor and control data, autonomous maneuvering (motion, displacement and "right" placement) of UAV sensors can be achieved. UAV based sensors will have to accomplish difficult tasks in dynamic environments. However, existing robotic systems accomplish simple tasks which are not scenario dependent functions and they generally do not operate in concert with other systems. While some multi-function robotic systems exist, (factory "system of systems"), they are operating in carefully controlled environments. This research will meet future UAV sensor requirements to perform multi-level autonomous functions dynamically, in the real world. 

Future military and civilian requirements will be stressing and will demand innovative sensors and sensor configurations. In addition, many on-going research and development investigations increase our confidence that maturing technologies will foster success. In addition to Sensors as Robots and advances in UAVs, examples include the development of knowledge based space time adaptive processing (KBSTAP), a dynamic software architecture for the filtering, detection, and tracking stages of radar processing, using the "non-homogeneity detector" USGS "map" data, archival radar data, as well as off board sensor data to select the most appropriate STAP training data for improvements in filtering, detection, track, identification and handoff; as well as waveform selection and flight planning. As a result of near recent and current research efforts, well-grounded and validated signal processing algorithms abound. To deal with the enormous quantity of data expected in the future system architectures, increased processor speeds will be needed. On-going industrial activities are resulting in processor speeds which are doubling every 18 months. At the same time, the object oriented software paradigm is helping to reduce the cost of computing via software reuse. The next generation internet and the AI (Artificial Intelligence) communities are providing new software tools and models as well. 

Soon, technology will permit the deployment of multiple UAV platforms in performing dynamic scenarios. Each UAV could have a suite of heterogeneous sensors which can operate autonomously or in concert with other platforms. In this manner we can deploy the correct number and type(s) of UAVs to meet the requirements and goals of the deployment, reduce risk, and minimize the use of expensive or manned platforms. Sensors as Robots technology will help to accelerate this process for both military and civilian applications.

Michael Wicks received undergraduate degrees from Mohawk Valley Community College and Rensselaer Polytechnic Institute, and graduate degrees from Syracuse University, all in Electrical Engineering. He is a Fellow of the IEEE and a member of the Association of Old Crows. Dr. Wicks is a Principal Research Engineer in the U.S. Air Force Research Laboratory in the Sensor Directorate, Radar Signal Processing Branch. He has authored over 125 papers, reports and patents. His interests include adaptive radar signal processing, wide band radar technology, ground penetrating radar, radar clutter characterization, knowledge base applications to advanced signal processing algorithms, detection and estimation theory, and applied statistics. He serves on the Board of the SUNY Institute of Technology Foundation, the Mohawk Valley Community College Engineering Science Advisory Council, the IEEE Aerospace and Electronic Systems Board of Governors and is Chairman of the IEEE Radar System! s Panel.

Professor Yakov Davidovich Shirman

Advantages and Problems of Wideband Radar Y.D. Shirman, S.P. Leshchenko, V.M. Orlenko Kharkov Military University (KMU), Kharkov, Ukraine 

This paper addresses the definition, properties and modeling requirements of wideband and ultra-wideband radars. It begins by establishing the criteria by which the designation 'wideband' might be specified, since the common definition based purely on relative bandwidth fails to invoke some important associations. Next, the role of simulation is discussed and a representative simulation environment is described. The advantages and disadvantages of increasing the signal bandwidth are then evaluated quantitatively in the context of signal detection, parameter measurement, and the recognition of aerial target classes and types, with reference to specific classes of interest. In practice, certain other issues arise, including the degree of immunity to interference and electromagnetic capability. These topics are discussed in terms of their dependence on signal bandwidth and the associated signal acquisition and processing. In addition, an emerging problem is the need to take into account the possibility of signal exploitation and LPI. The essence of the theory of unauthorized signal detection that limits the LPI possibilities is briefly considered. 

Professor Yakov Davidovich Shirman has been a member of the academic staff at Kharkov Military University (former the Military Engineering Academy of Antiaircraft Defense) since 1949 where he holds the position of Professor of the Radio Engineering and Radar Theory Foundations' Chair. Professor Shirman received the Candidate of Science (PhD equivalent) degree from Leningrad Airforce Engineering Academy in 1948, and a D.Sc. from the Radiotechnical Institute of Academy of Sciences of USSR, Moscow in 1960. His early work addressed the statistical theory of optimal resolution in the presence of an interference background, spanning the time-frequency-space-polarization domain. Later, in 1956, he independently proposed the compression of wideband pulses, unknown then in the USSR. Subsequently, Prof. Shirman directed pioneering experimental work on Ultra Wideband Radar. In the late 1980's, he and his students started elaborating the method of computer simulation of real target bac! kscattering, accounting for different illumination signal and target parameters, both deterministic and stochastic. He has recently published a book Computer Simulation of Aerial Target Radar Scattering, Recognition, Detection and Tracking (Artech House) which covers the theory of such simulation and has developed software for recognition, detection and tracking of targets in multiple simulated flights. He has developed many educational courses, authored numerous text books and course manuals on the theoretical foundations of radar, and published around 400 papers. Professor Shirman has been awarded State Premiums of the USSR in 1979 (technique section) for his cycle of works on Radar and in 1988 (science section) for his cycle of works on Statistical Theory of Electronic Systems. He has been awarded many honours, including the status of Honorary Scientist and Engineer of Ukraine (1967). Professor Shirman is a Senior Member of the IEEE.

Secretariat

Plevin and Associates Pty Ltd PO Box 54 BURNSIDE 5066 South Australia
Tel +61 8 8379 8222 Fax +61 8 8379 8177 events@plevin.com.au

Last updated 26/06/2003