Spectrum

Spectrum Analyzer Fundamentals

2009-10-15T00:53:00.002-07:00

Spectrum Analyzer
Fundamentals This chapter will focus on the fundamental theory of how a spectrum analyzer
works. While today s technology makes it possible to replace many analog
circuits with modern digital implementations, it is very useful to understand
classic spectrum analyzer architecture as a starting point in our discussion.
In later chapters, we will look at the capabilities and advantages that
digital circuitry brings to spectrum analysis. Chapter 3 will discuss digital
architectures used in modern spectrum analyzers.

Figure 2-1. Block diagram of a classic superheterodyne spectrum analyzer

Figure 2-1 is a simplified block diagram of a superheterodyne spectrum
analyzer. Heterodyne means to mix; that is, to translate frequency. And
super refers to super-audio frequencies, or frequencies abov the audio
range. Referring to the block diagram in Figure 2-1, we see that an input
signal passes through an attenuator, then through a low-pass filter ( later w
shall see why the filter is here) to a mixer, where it mixes with a signal from
the local oscillator ( LO) . Because the mixer is a non-linear d vice, its output
includes not only the two original signals, but also their harmonics and th
sums and differences of the original frequencies and their harmonics. If any
of the mixed signals falls within the passband of the int rmediate-frequency
( IF) filter, it is further processed ( amplified and perhaps compressed on a
logarithmic scale) . It is essentially rectified by the envelope detector, digitized,
and displayed. A ramp generator creates the horizontal movement across th
display from left to right. The ramp also tunes the LO so that its frequency
change is in proportion to the ramp voltage.

If you are familiar with superheterodyne AM radios, the type that receiv
ordinary AM broadcast signals, you will note a strong similarity between them
and the block diagram of Figure 2-1. The differences are that the output of a
spectrum analyzer is a display instead of a speaker, and the local oscillator is
tuned electronically rather than by a front-panel knob.

Spectrum Analyzer Tutorials

2009-10-15T00:53:00.001-07:00

This site is intended to explain the fundamentals of swept-tuned, superheterodyne spectrum analyzers and discuss the latest advances in spectrum analyzer capabilities.

At the most basic level, the spectrum analyzer can be described as a frequency-selective, peak-responding voltmeter calibrated to display the rms value of a sine wave. It is important to understand that the spectrum analyzer is not a power meter, even though it can be used to display power directly. As long as w know some value of a sine wav ( for example, peak or average) and know the resistance across which w measure this value, we can calibrate our voltmeter to indicate power. With the advent of digital technology, modern spectrum analyzers have been given many more capabilities. In this note, w shall describe the basic spectrum analyzer as well as the many additional capabilities made possible using digital technology and digital signal processing.

Frequency domain versus time domain Before w get into the details of describing a spectrum analyzer, w might first ask ourselves: Just what is a spectrum and why would w want to analyze it? Our normal frame of reference is time. We note when certain events occur. This includes electrical vents. We can use an oscilloscope to view the instantaneous value of a particular electrical vent ( or some other event converted to volts through an appropriate transducer) as a function of time. In other words, w use the oscilloscope to view the wav form of a signal in the time domain.

Fourier 1 theory tells us any time-domain electrical phenomenon is made up of one or more sine waves of appropriate frequency, amplitude, and phase. In other words, we can transform a time-domain signal into its frequency- domain equivalent. Measurements in the frequency domain t ll us how much energy is present at each particular frequency. With proper filtering, a wav form such as in Figure 1-1 can be decomposed into separate sinusoidal waves, or spectral components, which w can then valuate independently. Each sine wav is characterized by its amplitude and phase. If the signal that we wish to analyze is periodic, as in our case here, Fourier says that the constituent sine wav s are separated in the frequency domain by 1/ T, where T is the period of the signal 2

Figure 1-1. Complex time-domain signal

1. Jean Baptiste Joseph Fourier, 1768-1830.
A French mathematician and physicist who
discovered that periodic functions can be expanded
into a series of sines and cosines.
2. If the time signal occurs only once, then T is infinite,
and the frequency representation is a continuum of
sine waves.

Spectrum Analyzer

2009-10-15T00:47:00.000-07:00

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