Publications

The U.S. Federal Aviation Administration is deploying Automatic Dependent Surveillance-Broadcast (ADS-B) to provide next-generation surveillance derived through down- and cross-link of global positioning satellite (GPS) navigation data. While ADS-B will be the primary future surveillance system, FAA recognizes that backup surveillance capabilities must be provided to assure that air traffic control (ATC) services can continue to be provided when individual aircraft transponders fail and during localized, short-duration GPS outages. This report describes a potential ADS-B backup capability, Secondary Surveillance Phased Array Radar or SSPAR. SSPAR will interrogate aircraft transponders and receive replies using a sparse, non-rotating array of approximately 17 omnidirectional (in azimuth) antennae. Each array element will transmit and receive independently so as to form directional transmit beams for transponder interrogation, and support high-resolution direction finding for received signals. Because each SSPAR element is independently digitized, transponder returns from all azimuths can be equipped with Traffic Alert and Collision Avoidance System (TCAS) and ADS-B avionics to reduce spectrum usage and maintain the high surveillance update rate (~1 per second) achieved by ADS-B. Recurring costs for SSPAR will be low since it involves no moving parts and the number of array channels is small. This report describes an SSPAR configuration supporting terminal operations. We consider interrogation and receive approaches, antenna array configuration, signal processing and preliminary performance analysis. An analysis of SSPAR's impact on spectrum congestion in the beacon radar band is presented, as are concepts for integrating SSPAR and next generation primary radar to improve the efficiency and accuracy of aircraft and weather surveillance.

The Defense Advanced Research Projects Agency (DARPA) High Productivity Computing System (HPCS) program is developing systems that deliver increased value to users at a rate commensurate with the rate of improvement in the underlying technologies. For example, if the relevant technology was silicon, the goal of such a system would be to double in productivity (or value) every 18 months, following Moore's law. The key questions are how we define and measure productivity, and what the underlying technologies that affect productivity are. The goal of this paper is to synthesize from several different productivity models a single model that captures the main features of all the models. In addition we will start the process of putting the model on an empirical foundation by incorporating selected results from the software engineering and high performance computing (HPC) communities. An asymptotic analysis of the model is conducted to check that it makes sense in certain special cases. The model is extrapolated to a HPC context and several examples are explored, including HPC centers, HPC users, and interactive grid computing. Finally, the model hints at a profoundly different way of viewing HPC systems, where the user must be included in the equation, and innovative hardware is a key aspect to lowering the very high costs of HPC software.