JED - June 2012 - (Page 47)

TECHNOLOGY SURVEY A SAMPLING OF SPECTRUM ANALYZERS By Ollie Holt his month’s technology survey looks at spectrum analyzers for EW testing. Why are spectrum analyzers of interest to EW developers and users? Spectrum analyzers can provide insight into an RF signal’s characteristics. They can be used to characterize properties of a radar signal being transmitted to support development of radar signal identification parameters. They can also be used to monitor the output of a transmitter or jammer to ensure that it is creating the desired waveform. A spectrum analyzer can measure an RF signal’s dominant frequency, power, harmonics, bandwidth and other spectral components that cannot be easily measured in the time domain. They can also perform typical time domain measurements, like signal repetition rate and pulse rise time measurements. A typical spectrum analyzer display will show frequency on the horizontal axis and amplitude on the vertical axis. There are two basic types of spectrum analyzers; 1) swepttuned and 2) Fast Fourier Transform (FFT). Some spectrum analyzers use a combination (hybrid) of the two concepts by first down-converting the input RF signal and then analyzing using the FFT. A swept-tuned spectrum analyzer down-converts the signal to a center frequency of a swept tuned bandpass filter. The filter bandwidth defines the resolution bandwidth. A local oscillator is swept through the display frequency range, moving a band pass filter with it, and the results (energy that is passes through the filter) are shown on the display. The disadvantages of a swept-tuned spectrum analyzer are, if it scans too fast, it reduces amplitude and frequency measurement accuracy, and sweeping too slow may cause you to miss details in a time-variant signal. An FFT spectrum analyzer samples the input signal and Fourier transforms it to perform the signal measurements. The sampling frequency must be at least twice the bandwidth of the signal of interest following the Nyquist limits for correct signal measurements. After collecting a set of samples, different mathematical operations can be performed on the data to measure different signal parameters. The main disadvantage of the FFT spectrum analyzer is that for higher frequencies the sampling speeds start to exceed analog-to-digital converter technology, limiting the spectrum analyzer’s upper frequency. To compensate for the limited upper frequency capability of FFT spectrum analyzers, a hybrid of the two types was developed. First the signal is down-converted to an intermediate frequency and then sampled for FFT analysis. T ABOUT THE SURVEY JED sent out questionnaires and received responses from seven spectrum analyzer manufacturers. In the table, the “Operating Frequency Range” column defines the lower and upper operational range of the analyzer. The upper frequency limit is the more important value for determining if the analyzer will cover the frequency range of the system or component to be tested/evaluated. The next column defines the center frequency and span options. The display on a spectrum analyzer can be set with a start and stop frequency. The start frequency would be the start of the sweep on the left side of the display and the stop frequency is the top limit on the right side of the display. The frequency range between the start and stop frequency is called the span. The center frequency is the frequency point in the center of the display. The operator can select the center frequency and the frequency span to be displayed. There are usually multiple options the operator can select to control the amount of information on the display. The “Resolution Bandwidth” column indicates the bandwidth of the swept filter or the digital filter used in the signal processing of the FFT data. This bandwidth can be selected and is most important when trying to discriminate between closely spaced signals in frequency. The filter bandwidth also defines the analyzer noise floor (lower noise floor allows weaker signals to be detected). The wider the bandwidth the higher the noise floor will be, and the narrower the bandwidth the lower the noise floor – lower is better. The detector defines the technique used to determine the signal amplitude. Some typical types are simple detection, peak detection and average detection. Simple just uses the midpoint of the display. Peak uses the maximum measured point, and average uses all the data points. Average may use root mean squared averaging, voltage or log-power averaging. The SFDR column defines the spurious free dynamic range and is the same as for any receiver system. It defines the operational dynamic range from the lowest power signal detected to the highest power signal. This parameter can be a function of the bandwidth selected. The “Trigger” column defines the options for starting a measurement of a specific signal. This could be continuous, where the analyzer just keeps sweeping, or it can be set to trigger on specific artifacts on the analyzer input or some other external stimulus. The “Applications” column addresses the different uses the spectrum analyzer was designed to support. Some may be just for communication signals where others are general purpose for all RF types. The “Form Factor” column indicates whether the spectrum analyzer is more suited for laboratory use or is small enough to be taken in the field or to a flight line. Similarly, weight, power and size aid in determining where and how the spectrum analyzer can be used. In next month’s survey, JED will address radar jammers. The Journal of Electronic Defense | June 2012 47

Table of Contents for the Digital Edition of JED - June 2012

The View From Here
Conferences Calendar
Courses Calendar
From the President
The Monitor
Washington Report
World Report
Future EW: Next Gen Jammer
Technology Survey: Spectrum Analyzers
EW 101
AOC 2012 Election Guide
AOC News
Index of Advertisers
JED Quick Look

JED - June 2012