Functional Blocks of Pulse Measurement

The steps in the process previously described may be performed individually by separate devices, or multiple steps — including the entire pulse measurement — may be performed by a single analyzer. A general overview of the process is shown in Figure 3.

Acquisition hardware can take several forms, including both baseband and IF sampling, and can be performed by standalone instruments and modular systems. The most important performance characteristics of the hardware are frequency bandwidth and dynamic range, though memory depth, the number of channels, and other factors are also important.

Analysis algorithms turn the digitized signals into measurement data in the form of displays and result tables as needed. The algorithms may be part of general spectrum or VSA functions, or they may be embedded in dedicated pulse analysis applications. The applications are especially powerful when more comprehensive pulse analysis is needed, such as pulse parameter statistics or signal environment characterization.

Deep data storage is critical for some applications — generally where a large number of contiguous pulses must be analyzed from gap-free capture, or where access to the signal under test is limited and analysis must be performed later. Sampled data storage is combined with post-processing to generate the analysis results needed, and may also be used for signal playback.

Triggering operations can initiate or synchronize pulse acquisition, or can be used to time-align existing samples for pulse analysis. Since triggers can be taken directly from the input signal or can be the result of signal processing, such as real-time analysis or post-processing from data storage, they can be part of any of the main measurement blocks.

Challenges of Complex Pulse Analysis

Finding the signal of interest and aligning measurements with the desired timing are the first steps in pulse analysis, and can be some of the most challenging. This is particularly true of complex pulse environments that can include frequency- and amplitude-agile emitters, and multiple signal sources with widely varying amplitudes. Though it provides no frequency selectivity narrower than the analyzer span, the time and level parameters' IF magnitude trigger provide enough flexibility and specificity for many pulse measurements. By combining the selectable positive and negative (pre-trigger) delays with appropriate holdoff values and types, a single pulse can be selected from among many. If there is a repeating pattern, the largest signal can be used for triggering, and positive or negative time delays used to select any other single pulse or time interval. The holdoff function can also be used to avoid false triggers from pulse amplitude variations due to modulation.

The time-qualified trigger occurs when a combination of one or two timing criteria is met. Though the criteria may only be met after the signal of interest, a negative trigger delay can allow the signal of interest to be measured.

FMT is valuable in finding and measuring transient or interfering signals, or in capturing specific signal behavior that can be best identified in the frequency domain. FMT processing is real-time or gap-free, providing confidence that any signal or behavior that matches the criteria will result in a trigger for measurement or time capture operations. Frequency masks can be generated manually or constructed from example spectrum measurements with offsets, or subsequent editing of amplitude/frequency breakpoints. Selectable trigger criteria make FMTs very powerful for identifying particular signal or environment behavior. Triggers can be generated when a signal enters or leaves the mask, and even for more complicated behavior such as leaving the mask after an entry event. These logical trigger criteria can be useful for capturing signals that are switching channels or are using frequency-hopping techniques.

The triggers and signal processing described so far will meet most pulse measurement needs; however, pulse durations and duty cycles are important variations in some signals, and a time-qualified trigger (TQT) can help isolate them for measurement. The TQT is a supplement to the FMT and IF magnitude triggers, continuously tracking the duration of events in the acquisition bandwidth. Thus, the TQT establishes a time qualification parameter in addition to the amplitude or spectrum parameters already available (Figure 4). Three properties are associated with TQT: Time 1, Time 2, and Time Criteria. These properties allow both open-ended and fully-bounded time durations to be set. In TQT operation, samples are acquired for analysis after an event has lasted for the specified time duration. If analysis of prior events is needed, a negative (pretrigger) delay can be used.

As baseband samplers, oscilloscopes typically lack the sophisticated combination of time and frequency domain triggers described so far; however, they do offer trigger capabilities that can be useful with RF pulses. One example is basic edge triggering, when combined with trigger holdoff. A trigger occurs when the input signal crosses a voltage threshold, as is the case with the beginning of an RF pulse as it grows in magnitude. By selecting a holdoff time longer than the longest expected pulse, the holdoff ensures that triggers will happen only at the beginning of RF pulses. This technique works most predictably for signals with consistent pulse duration.

Dynamic Range and Bandwidth Tradeoffs

Wide and ultra-wide bandwidths are increasingly important in pulse applications for several reasons. Ultra-wideband radars provide fine range resolution, plus increased resistance to detection and jamming. Frequency-hopping transmitters operate over wide ranges, requiring wideband capture to fully characterize the signal and avoid missing hops. Signal intelligence applications require acquisition of wide, contiguous bandwidth to identify targets.

Though the specifics of the tradeoffs improve over time, sampling with wider bandwidths inherently imposes performance limits. These limits arise primarily from the increased noise inherent in wider bandwidths and the decrease in ADC effective bits as sampling rates increase. These limits must be weighed against performance needs such as dynamic range, sensitivity, distortion, amplitude accuracy, and phase noise.

Capture length, or the amount of data to be acquired for a measurement, is a critical issue in many pulse analysis applications. Capture length — in terms of time — is especially important in analyzing dynamic environments, where there is a need to capture a time segment long enough to represent the dynamics in question. Every hardware platform has a limit to its memory size, and efficient memory use will provide the longest possible capture and the best signal measurement.

For a sampled data system, the maximum capture length for a given memory size is a linear function of the acquisition bandwidth. This favors the signal analyzer over the oscilloscope, since the signal analyzer samples only the IF bandwidth. The oscilloscope must perform baseband sampling of the entire signal spectrum — with later data reduction to convert to a band-limited IF — and the result is a much shorter gap-free signal capture. As noted, the transfer and processing of this baseband data can also result in slower measurement throughput. For baseband sampling of signals with wide bandwidth and low duty cycle, the memory issues can be a problem.

For many applications, the solution to this problem is in the segmented memory feature of some oscilloscopes. When this feature is enabled, the acquisition memory of the oscilloscope is broken up into many smaller equal-length segments. The segment length is chosen to be slightly longer than the widest pulse to be captured. Segmented memory also makes it much easier to replay or review the captured pulses in the time domain in the oscilloscope. The user can step through the segments manually or automatically to understand pulse sequences before processing the memory segments.

The combination of oscilloscope-based time domain analysis, signal analyzer-based real-time spectrum analysis, and VSA software that can make comprehensive measurements from both platforms meets many needs for pulse analysis. However, some applications require more macro-scale measurement capability that gathers information from hundreds or thousands of pulses and organizes analysis results in tabular or graphical form. Some typical applications include transmitter and component testing, characterizing pulse modulation stability, characterizing threats (SIGINT), verifying threat simulations, and verifying responses to EW jamming.

Understanding and quantifying the stability and repeatability of multiple characteristics of pulsed signals are critical tasks in making effective use of them. The collective analysis of large numbers of pulses can reveal behavior that is otherwise difficult to spot or to quantify. Time-aligned pulse modulation measurements are especially useful for diagnosing problems, and the software can use best-fit algorithms to provide tabular summaries of parameters such as FM slope and peak-to-peak deviation.

These time-aligned pulse modulation measurements are especially useful for diagnosing problems, and the software can use best-fit algorithms to provide tabular summaries of parameters such as FM slope and peak-to-peak deviation. With large numbers of measured pulses, the application can use statistics derived from them to produce histograms and trend lines. The statistics can be gathered from single or multiple acquisitions and, in comparison to measurements of individual pulses, provide much greater sensitivity to defects and a more comprehensive view of transmitter performance.

This article was contributed by Keysight Technologies, Santa Rosa, CA. For more information, Click Here.

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