A class of pulsed radio frequency (RF), microwave, and mmWave applications present a measurement challenge to radar and electronic warfare (EW) designers due to the need for wide analysis bandwidth, as well as a desire to evaluate a significant time period of system activity. Segmented memory in wideband oscilloscopes may be used, including pulse analysis software, to address these challenges. Radar and EW applications can also be further explored in terms of pulse amplitude, frequency and phase measurements, and optimizing accuracy.

If direct digitization techniques are used to achieve amplitude and phase-flatness advantages in pulsed RF measurements – as is the case when using particular high-bandwidth oscilloscopes – the related high-speed sampling approach will consume acquisition memory very quickly. Thus arises the need for “segmented memory” in which signals of interest are placed into memory segments and the receiver ignores the time when signals of interest are not present, as shown in Figure 1.

Figure 1: Segmented memory approach, in which signals of interest are stored into memory segments.

Achieving long target time capture in pulsed RF applications

Consider the example of a pulsed RF signal with a 15 GHz carrier frequency and 2 GHz-wide modulation. The oscilloscope must sample fast enough to handle the modulated 15 GHz RF pulse signal. That would require a sample rate of at least ~ 2.5 x 16 GHz, or 40 Gsa/s (gigasamples per second). To have some margin beyond the 2 GHz modulation on the carrier, and to avoid the rolloff of the scope bandwidth, the next highest sample rate selectable is the full 80 Gsa/sec of the scope for 33 GHz bandwidth capture.

Using a standard capture approach – where all samples simply go into the available memory regardless of what signals are present – at the full 80 Gsa/sec sample rate of a 33 GHz bandwidth scope, and using the full 2 Gpts (billions of acquisition samples) memory depth available, that corresponds to 25 msec of capture time:

(2 Gsa) / (80 Gsa/sec) = 25 msec

Now consider a pulse train that has a pulse repetition interval of 100 usec (a pulse repetition rate [PRI] of 10 kHz) and 1 usec wide pulses. The related scope capture would include close to 250 pulses based on the following calculation:

(25 msec) / (100 usec / pulse) = 250 pulses

By using oscilloscope segmented memory, the number of pulses captured can be increased dramatically. With segmented memory mode, memory segments can be defined to be a little longer than the longest pulse captured. A 1.2 usec-wide segment size can be used to capture the 1 usec-wide pulses, for example.

The segmented memory capture can be set up to achieve 1.2 usec wide segments, where the memory depth is chosen to be 96 kpoints, and 32,768 segments as shown in Figure 2.

Figure 2: Segmented memory setup to choose 1.2 usec wide segments for 1 usec wide pulse capture.

The calculation for the required segment memory depth is very simple, knowing the sample rate is 80 Gsa/sec, and that a 1.2 usec segment length is desired:

(80 Gsa/sec) x (1.2 usec) = 96,000 samples

Now, by pressing the “Single” capture button, 32k pulses are captured and brought into 32k segments, which corresponds to 3.3 seconds of target activity time.

The segmented capture can be seen in Figure 3, taken on a pulsed RF signal with a 15 GHz carrier and 2 GHz-wide linear FM chirp modulation. Notice there is a “Play” button that allows a playback of the 32k segments. Notice also that statistics are calculated on the 32k pulses that were captured.

Figure 3: 33 GHz oscilloscope segmented memory capture of 32k pulses into 32k segments, 1.2 usec per segment.

Enhancing pulse/radar measurements

The segmented memory can be controlled by vector signal analysis software so that statistical pulse analysis can be conducted on many RF pulses captured into segmented memory. Such analysis can be performed on digitally downconverted oscilloscope samples, where the format is now baseband I/Q and the measurement has been tuned to the center frequency and a frequency analysis span chosen to be just a little wider than the signal spectral width. This allows processing gain to reduce noise in the measurement.

After noise reduction, many measurements can be taken on the in-phase/quadrature (I/Q) data, including how the amplitude, frequency, and phase change across an RF pulse. An example of such measurements can be seen in Figure 4, where memory segments 3, 4, and 5 and the pulses contained in those segments are being analyzed.

In this example, the linear FM chirp frequency shift across the RF pulse is measured and compared to a best-fit linear ramp (see center-right pane). The difference between the measured pulse and the best-fit straight line ramp is calculated and displayed (horizontal trace with noise). One can see that the measured ramp and reference ramp have very little difference between them. The error trace is displayed with a 1 MHz/div scale and around 500 kHz peak deviation, and the Freq Error RMS in the bottom-right table is showing around 300 kHz of frequency error.

In a similar fashion, the phase shift across a pulse is compared to a best-fit parabolic phase shift (see top-right pane), characteristic of linear FM chirp modulation on radar pulses. It is possible to zoom in on the difference between the measured and reference to see how much a target system is deviating from the ideal, and see around +8 and -5 degrees peak deviation and a Phase Error RMS of 2 degrees (as shown in the bottom-right table of Figure 4.)

Figure 4: Pulse-analysis software calculations based on measurements taken on oscilloscope segmented memory.

The spectral content of the RF pulse is seen in the left-center pane, a view of RF pulse envelope amplitude in the upper-left pane, and a view of the difference between the measured amplitude envelope and a best-fit straight line reference signal in the lower-left pane.

Finally, statistical analysis is possible on the measured parameters on the number of pulses captured into segments. In Figure 5, the statistical analysis can be seen in the pulse table based on 1,000 memory segments captured.

Figure 5: Statistical analysis on 1,000 memory segments.

When directly capturing wideband pulsed RF signals, the fast sampling rate required can make the capture of many pulses a challenge since the available acquisition memory is consumed quickly. However, segmented memory is one way to address this problem by acquiring RF pulses into memory segments, and then turning off the acquisition during “quiet” time until the next RF pulse occurs. Pulse analysis software can both control a segmented memory capture and digitally downconvert captured signals into baseband I/Q data. This method effectively tunes the measurement to a specific carrier frequency with a frequency measurement span slightly wider than the signal under test, thus reducing noise and increasing measurement accuracy. System validation time is decreased through the ability to compare actual, measured pulse characteristics against ideal, relative, best-fit reference signals for amplitude, frequency, and phase, thus enabling the user to identify issues in signal creation or system performance.

Brad Frieden is an applications engineer for the Aerospace/Defense Industry Solutions team of Keysight Technologies focusing on radar, EW, and satellite-related RF measurements using high-bandwidth, deep-memory oscilloscopes in conjunction with arbitrary waveform generators. He has been with HP/Agilent/Keysight since 1984. He received his BSEE from Texas Tech in 1981 and his MSEE from The University of Texas at Austin in 1991. Email the author at [email protected]

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