Digital signal processing algorithms are powerful tools that provide algorithmic solutions to common problems. For example, digital filters provide several benefits over their analog counterparts. These algorithms are traditionally implemented using dedicated digital signal processing (DSP) chips, FPGAs, or RISC processors. While these solutions are very efficient at their purpose, they only perform one function in the system and can be both expensive and large. This application note discusses an alternative solution using a Silicon Labs microcontroller to implement DSP algorithms in less space and still have plenty of CPU bandwidth available for other tasks.

TABLE OF CONTENTS

The flexibility of the FPGA means it can be used to run a dynamically configurable measurement application that measures the effect of the ‘uncontrollable’ parameters.

We’ve heard that FPGAs can do signal processing algorithms. But what about the rest of the problem: controlling analog-to-digital converters, compensating for effects of voltage and temperature, and more? Here, FPGAs get serious.

The growing use of FPGAs in SIGINT applications is enabling a significant increase in the capability of defense systems. With a wider variety of threats than ever before, the desire to monitor more of the frequency spectrum necessitates a high performance front-end processing engine. A SIGINT system that needs to monitor VHF (30-300 MHz), UHF (300-1000 MHz) communications and navigational aids, through to radar and satellite communications in the L band (1-2 GHz) frequencies, requires an analog bandwidth of 2 GHz for complete and instantaneous coverage.

Since there are a wide range of frequencies and consequently a wider range of signal modulation schemes, the processing system needs to have significant I/O bandwidth to receive the data as well as the computing performance to implement real time demodulation and analysis algorithms. Typical algorithms include Digital Down conversion, ffts, filtering, and I/Q demodulation.