For decades, spectrum analyzers have been used in the development and characterization of radar and electronic warfare (EW) systems. However, traditional swept measurements are rapidly becoming insufficient for today’s agile and adaptive systems. Today’s best alternative is a fast, flexible, wideband signal analyzer equipped with real-time spectrum analysis (RTSA) and coupled with vector signal analysis (VSA) software. As an example, some of today’s most advanced signal analyzers support a variety of techniques that enhance the identification and analysis of pulsed or intermittent signals: fast sweep, RTSA and stepped density. Those that are also compatible with VSA software enable enhanced analysis through a comprehensive toolset that enables detailed examination of signals from multiple perspectives.
Leveraging the Advantages of a Signal Analyzer
Typical spectrum analyzers have difficulty capturing fleeting signals. For example, traditional swept analysis can fall short when measuring signals that carry pulse modulation, use wideband frequency hopping or have very short durations.
The best complement—or possible alternative—is a signal analyzer, which can measure amplitude and phase. As defined by the industry, a signal analyzer combines the capabilities of a spectrum analyzer and a vector signal analyzer in one instrument. Vector measurements are necessary for tasks such as viewing phase versus time, demodulating complex signals or measuring wideband power.
Signal analyzers that offer a very wide analysis bandwidth (e.g., 510 MHz) and also support RTSA and stepped density enable measurements of signals that occur infrequently or have long repetition intervals.
Using Fast Sweep
Figure 1 illustrates the operation of a traditional swept spectrum analyzer. The RBW filter and sweep trajectory are shown in green and are plotted against time; the dashed grey lines show the retrace time. Whenever the green line intersects with one of the signals (in black) it will appear on the analyzer trace.
Figure 1. In this example of a traditional sweep, each time the green line coincides with one of the signals (in black) it will appear on the analyzer trace.
In fast sweep, the analyzer is configured to sweep as fast as possible using a relatively narrow resolution bandwidth (RBW). Some signal analyzers implement this method using digital RBW filters and DSP-based error correction to produce dramatically faster speeds without compromising accuracy in terms of amplitude or frequency. By eliminating sweep errors, these filters also ensure greater repeatability at faster sweep rates.
As shown in Figure 2, sweeping faster (i.e., decreasing the sweep time) produces more intersections with the various signals in the environment. While this is an improvement over traditional methods, the analyzer may not provide a complete view of all in-band spectral content.
Figure 2. Using the fast sweep technique, the green line takes less time (Y-axis) to go from left to right. As a result, there are more intersections with the signals.
Actual measurement results are shown in Figure 3. These were performed using a signal analyzer that includes the fast-sweep capability.
Figure 3. The figure on the left shows a single measurement with fast sweep in the Keysight UXA X-Series signal analyzer. Notice that the analyzer caught some but not all of the signal content. On the right, the instrument is in max-hold mode and multiple sweeps are taken, making the envelope of the signal content more apparent.
Shifting to Real-Time Spectrum Analysis
Fast sweep is useful when looking for signals that have a relatively short repetition interval. In today’s signal environment, it’s more common to seek signals that occur infrequently, have long repetition intervals or are closely spaced and appear at virtually the same time.
RTSA relies on gap-free processing to acquire and display all in-band signals. In this mode, the local oscillator (LO) is stationary at a specific frequency and the analyzer digitizes the incoming spectrum. FPGAs process the acquired data in a gap-free manner by performing FFTs at a rate equal to or faster than the collection rate (Figure 4).
Figure 4. With the stationary LO, data is continously analyzed and displayed, and all signal content is captured independent of repetition rate. Notice that the bandwidth is narrower than that of the swept measurements shown in Figures 1 and 2.
With ongoing improvements in digitizers and DSP technology, this technique has evolved from narrow bandwidths, which are inadequate for today’s EW systems, to gap-free spans of 160 MHz (c. 2013) to 510 MHz (c. 2014) in standalone signal analyzers. In addition, the ability to maintain wide dynamic range at wide bandwidth helps to accurately identify smaller signals in the presence of larger ones.
Beyond the ability to monitor more of the spectrum without missing signals, real-time measurements can catch signals with very short durations. For example, at the higher sample rates needed for wider bandwidths, the analyzer can catch very brief signals. If at least 60 dB of signal margin is available, a real-time analyzer can detect virtually any signal that is as narrow as the reciprocal of the analyzer’s effective sampling rate (1/fs). For example, an analyzer that samples at 300 MHz is able to detect a signal of >3.33 ns, though not 100 percent of the time.
Although the analyzer can detect pulses in the nanosecond range, there is no assurance that it will always capture the pulse or display its amplitude with full accuracy. A specification called probability of intercept (POI) defines the minimum pulse width at which the analyzer will always detect an elusive or intermittent signal and do so with full amplitude accuracy. The minimum duration for 100-percent POI depends on several factors: sampling rate, time-record length (or FFT size), windowing function, window size, overlap processing, and noise floor. Because many of these have user-controllable parameters, the selected values will affect the minimum achievable POI value.
In post-processing, today’s ASICs and FPGAs are fast enough to not only process the FFTs but also do arbitrary resampling, decimation and corrections. These can be very helpful in maintaining dynamic range and ensuring accurate characterization of the signal.
When FFT processing is done at a very high rate, views such as the density display are used to enhance data interpretation. These displays provide a view that plots frequency, amplitude and signal duration in a single trace (Figure 5).
Figure 5. This density display is from a signal analyzer equipped with the optional RTSA mode. It clearly reveals coincident signals, including two pulses that occupy the same spectrum within the pulse density.
Extending RTSA with Stepped Density
Some instruments provide a hybrid approach called stepped density that stitches together a series of spectral density measurements across a wide frequency span. In this method, each real-time block of data is captured and analyzed for a period of time, and then the LO steps to an adjacent frequency band and takes another real-time acquisition (Figure 6).
Figure 6. In stepped density mode, the acquisition time determines how long (Y-axis) the signal analyzer dwells in each section of the spectrum. Here, this method captures at least one instance of each pulse train but does not capture all signal content.
One key advantage of this approach is a maximum measurement bandwidth that can be as wide as the full range of the analyzer. Another is the ability to find short or intermittent signals that occur over a specific time period. Contrast this to a single-band RTSA measurement: it is certainly possible to find a very small signal by making measurements over a very long time period; however, this is not realistic when test time is limited or the signal is not repetitive.
As an example, suppose a full test scenario for a transmitter takes 10 seconds but there is reason to be concerned about the presence of unwanted spurs within 1 GHz above and below the measurement. By setting the real-time acquisition period to 10 seconds, any spurs that occur during the test interval will appear. One possible technique is to configure the signal analyzer to cover the required 2-GHz span in real time by stepping through eight 250-MHz spans.
This approach does have two disadvantages worth mentioning. First, there is less flexibility in the setting of analyzer parameters such as RBW, as compared to the swept approach. Second, and perhaps more significant, is the time required to analyze the resulting spectrum if the task is to monitor a span of several gigahertz. This effect is compounded in scenarios that have very long repetition periods.
Pinpointing Signals of Interest
Either real-time method makes it possible to see multiple elusive signals within a frequency range of interest. However, when analyzing a dense EW environment, it may be necessary to pick out a single signal of interest, be it a carrier, spur or pulse.
The DSP that enables real-time measurements also supports advanced triggering capabilities that can detect signals of interest and then initiate measurements. Available trigger attributes include signal frequency, amplitude and duration (i.e., signal “on” time), or a combination. This can be a very useful way to find individual unwanted signals from an operating transmitter, something that is virtually impossible to do in the time domain.
Wrapping Up
A signal analyzer equipped with swept capability, wideband RTSA and stepped density provides the speed, flexibility and performance needed to characterize today’s complex systems and signal environments. Advanced real-time triggering can be used to isolate a signal of interest by initiating measurements based on specific frequency, amplitude and time criteria. The result is an ability to see the true behavior of dynamic signals and the real performance of leading-edge designs.
For More Information
Keysight offers a variety of application notes related to spectrum and signal analysis. Four are especially relevant to this topic:
- Using Wider, Deeper Views of Elusive Signals to Characterize Complex Systems and Environments
- Measuring Agile Signals in Dynamic Signal Environments
- Understanding and Applying Probability of Intercept in Real-Time Spectrum Analysis
- Using Fast Sweep Techniques to Accelerate Spur Searches
