The Crab pulsar seen with AquEYE at Asiago Cima Ekar observatory

We are developing fast photon-counter instruments to study the rapid variability of astrophysical sources by time tagging photon arrival times with unprecedented accuracy, making use of a Rubidium clock and GPS receiver. The first realization of such optical photon-counters, dubbed AquEYE (the Asiago Quantum Eye), was mounted in 2008 at the 182 cm Copernicus Observatory in Asiago. AquEYE observed the Crab pulsar several times and collected data of extraordinary quality that allowed us to perform accurate optical timing of the Crab pulsar and to study the pulse shape stability on a timescale from days to years with an excellent definition. Our results reinforce the evidence for decadal stability of the inclination angle between the spin and magnetic axis of the Crab pulsar. Future realizations of our instrument will make use of the Galileo Global Navigation Satellite System (GNSS) time signal.     

Implementing digital terrain data in knowledge-aided space-time adaptive processing

Many practical problems arise when implementing digital terrain data in airborne knowledge-aided (KA) space-time adaptive processing (STAP). This paper addresses these issues and presents solutions with numerical implementations. In particular, using digital land classification data and digital elevation data, techniques are developed for registering these data with radar return signals, correcting for Doppler and spatial misalignments, adjusting for antenna gain, characterizing clutter patches for secondary data selection, and ensuring independent secondary data samples. These techniques are applied to select secondary data for a single-bin post-Doppler STAP algorithm using multi-channel airborne radar measurement (MCARM) program data. Results with the KA approach are compared with those obtained using the standard sliding window method for choosing secondary data. These results illustrate the benefits of using terrain information, a priori data about the radar, and the importance of statistical independence when selecting secondary data for improving STAP performance