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Pulse Analysis: What Do I Need to Measure, and How Do I Do It?


by Jessica Patterson, Keysight Technologies, Inc.

Radar has become a more important discussion topic lately — and for good reason.  You may not know this, but radar is an acronym for Radio Detection and Ranging. At its most basic level, radar provides a method of detection to determine a moving object’s angle, location, and velocity. 

Radar enables scientists to collect data on planetary systems, capture natural phenomena, and predict hurricanes. Military radar engineers utilize the technology for threat identification and ground surveillance. In the air transport industry, radar provides safety measures for air and ground traffic control to guide planes, seen in Figure 1. However, it wasn’t always this advanced. 

Figure 1: An example of an air traffic control radar dish

Militaries widely used radar in World War II for aircraft detection.  Since then, it has evolved to support electronic warfare systems to identify targets and signals. Pulses now contain information with extremely complex modulation schemes. Modulation improves range resolution, but causes frequencies to hop rapidly, rising and falling with precise amplitude envelopes. If you’ve ever tried to analyze a signal that jumps from frequency to frequency, you know how difficult it can be.

In the past, you could use a swept spectrum analyzer to measure these modulated signals.  However, the methods used were somewhat basic and could never be used for today’s signals.  Today’s systems use much more complex pulses, and many signal environments include a variety of pulses. These advanced signals require digital signal processing (DSP) techniques in modern signal analyzers to achieve accurate measurements.

A combination of factors makes pulse measurements especially challenging. Signals may have a wide pulse bandwidth, resulting from a short pulse duration and fast transitions. They exist in complex signal environments, with pulses from many different sources, often with widely varying characteristics such as amplitude and width.  These pulses also have increasingly complex modulation, requiring demodulation and decoding. Not only are these pulses complicated, but they are also difficult to detect due to low duty cycles, intermixing with other signals, and low apparent power levels.

Radar systems teams need to efficiently collect radio frequency (RF) and microwave signal data across wide ranges of frequencies.  Figure 2 shows a measurement of many pulses in a short span. From this measurement, engineers must first identify, quantify, and validate individual pulses.  Once they do that, they can move on to answering critical questions such as, Did the transmitter work as expected? Why did the system drop a pulse?  How did pulse width change across thousands of pulses? 

Figure 2: Time and frequency domain representations of a chirp signal show varying pulses (left). A spectrogram view moves synchronously with the time and frequency representations (right).

To make these measurements and answer these questions efficiently, you need signal analysis measurement software running on a high-performance signal analyzer or oscilloscope. Your choice in hardware depends on which specifications are most important to you.  If you need a wide analysis bandwidth, use an oscilloscope. If you need excellent dynamic range and sensitivity, use a signal analyzer. From there, your software will likely provide you with advanced measurement options to analyze pulse-modulated radar signals. These options may include measurements of modulation characteristics and impairment efforts; performance indicators like power, droop, overshoot, and ripple; or statistical variance performance data for each pulse metric.

Software programs can capture fast-hopping, complex signals with angle of arrival (AoA) capability. You can use AoA to find out the direction of propagation of RF signals and establish location awareness in your radar system. AoA measurements not only help you determine where your signal comes from, but where the maximum signal strength lies during antenna rotation. There are many different methods involved in making AoA measurements, but one of the most efficient is segmented capture. 

Segmented capture allows you to quickly sample hundreds of pulses in short segments. This capability removes dead time from your measurement, letting you focus on more important elements of the pulse, such as overshoot or ringing. Figure 3 shows the amplitude profile on four channels, all changing over time. This change over time indicates an AoA change. Channel 4 (in red) displays a slight time domain effect that may cause issues later. The ability to identify and troubleshoot errors like this remains one of the most important components of pulse analysis.

Figure 3: Four channels showing acquisition time after the user has entered information on antenna spacing and location

When your signal analyzer includes AoA measurements, you can discover the positions of the antennas sending you signals.  An interactive graph depicts the X and Z positions, seen in Figure 4. This 3D plot shows antenna orientation with information on distance to the origin.  You can calculate actual AoA values by either noting phase differences or time difference of arrival (TDOA), also known as multilateration, between channels. TDOA is based on times of arrival (TOAs) of radio signals with known speeds. In most widespread uses, Global Positioning Systems (GPS) have replaced TDOA methods, but it is still quite useful for radar pulse analysis.

To find out more about AoA measurements and to perform them, you need to analyze the trend lines of our system. On one common graph, we can select both azimuth trend lines (on the X-Y plane) and elevation trend lines (on the Z plane). This view allows you to identify any phase discontinuities in your measurement. Also, unsurprisingly, we find a phase discontinuity due to the time domain effect from Figure 3.  The far right of Figure 5 depicts this phase effect. 

Figure 4: The X and Z positions showing the antenna configuration in the X-Z plane

Additionally, observe the four markers in Figure 5. These markers help you understand the start and stop angles of this measurement. Coupling the markers ensures they move at the same time. On the left, marker 1 is on both traces to provide the absolute phase of the signal.  On the right, there is a 2∆1 marker. This marker makes a delta measurement from its location with respect to marker 1, revealing the slope, or change over time of the angle. 

Figure 5: Elevation (purple) and azimuth (blue) trend lines showing the evolution across pulses on the Pulse Trend view

Placing markers on the traces allows you to determine antenna movement direction. The markers on the azimuth trend line (blue) determine the direction of movement of the antenna from left to right, with negative values indicating the antenna is moving to the left, and positive values indicating the antenna is moving to the right. In Figure 3, the platform changes in azimuth from negative degree values to positive degree values, meaning the antenna is moving to the right. In terms of elevation (purple), we can determine whether the antenna is moving down or moving up. In this example, the elevation moves from positive to negative degrees, meaning the antenna is moving down. These metrics together give us a two-dimensional analysis of how a signal in a multi-channel pulse scenario changes over time in amplitude and phase.

You now know how to measure and interpret AoA measurements. How do you know when to use this knowledge? If you need to make electronic warfare measurements or radar simulations, AoA measurements provide a useful method to accurately test your devices under test (DUTs) such as radar warning receivers. These tests give you information on whether your DUT can accurately locate the origin of the transmitter of a given radar threat. However, characterizing radar signal analysis doesn’t stop at AoA measurements; for most uses of radar, safety is on the line. 

To gain a comprehensive view of signals, you will need to analyze more information. This information may include:

Error – to best understand your signal, look for deviations from best fit analyses of your data to obtain error over time. Many methods exist to find error. You should analyze phase metrics, output level metrics, pulse compression metrics, and more.

Statistics – you can analyze any metric found in a pulse table in terms of statistics. Look at histograms of information such as envelope overshoot to find out how accurate and repeatable your results are. If you have a large population of pulses that need to have the same width or repetition interval, plot a histogram of the pulses. This plot will tell you variation.

Characterize – look at both individual pulses and pulse trains. Maintaining a tabulated catalog of pulse characteristics can help you identify outliers, like an adversarial signal trying to jam or confuse your receiver. Use features like pulse scoring and pulse train search to characterize.

By following these steps and taking your analysis to the next level, you can successfully understand and troubleshoot your radar signals. Using AoA, you can determine antenna location, but you don’t have to stop there.  With more measurements, you will be able to completely characterize your radar system. 

About the Author 

Jessica is a Product Marketing Engineer at Keysight Technologies in Sonoma County, CA. She specializes in signal analyzer hardware and software products and holds a BSEE from California Polytechnic State University, San Luis Obispo.