Get ready for software-defined RADAR

With a big RF transmitter and enough fast computing power, you have the ability to do a lot of different things, as evidenced by a General Electric presentation on "software-defined radar" at the GPU conference this year.

At GTC 13 last year, GE gave a standing-room-only presentation about how it's using RDMA (Remote Direct Memory Access) to drive multi-GPU process performance to new heights. The firm was back this year to talk about new and innovative applications of GPU tech it has cooked up over the past year.

How it works: Simultaneous transmit/receive for a whole load of functions

In its session, Dustin Franklin, GE GPU Applications Engineer guru, gives us an update on how it has been proceeding with RDMA and how it allows the electric company to build large scale, multi-node, products.

Multifunction Phased-Array Radar: Time Balance Scheduler for Adaptive Weather Sensing

Ricardo Reinoso-Rondinel, Tian-You Yu, and Sebastián Torres, 2010: Multifunction Phased-Array Radar: Time Balance Scheduler for Adaptive Weather Sensing. J. Atmos. Oceanic Technol., 27, 1854–1867. doi: http://dx.doi.org/10.1175/2010JTECHA1420.1

Abstract. Phased-array radars (PARs) have the capability of instantaneously and dynamically controlling beam position on a pulse-by-pulse basis, which allows a single radar to perform multiple functions, such as tracking multiple storms or weather and aviation surveillance. Moreover, these tasks can be carried out with different update times to achieve the goal of better characterizing and forecasting the storms of interest. However, these tasks usually compete for finite radar resources, and scheduling algorithms are often needed to address resource contention. To capitalize on the PAR capabilities, an algorithm based on the concept of time balance (TB) is developed for adaptive weather sensing. Two quality measures are introduced to quantify the gain of adaptive sensing relative to standard scanning patterns used by the Weather Surveillance Radar-1988 Doppler (WSR-88D). A simulation experiment is performed to demonstrate the advantages of adaptive sensing and to test and verify the performance of the TB scheduling algorithm. It is shown that the gain of adaptive sensing can be realized by the TB scheduler; that is, storms of interest can be revisited more frequently within a relatively short period time compared to conventional scanning.