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Pulsed signals are widespread in radar and other electronic warfare (EW) applications, and they must be accurately measured for manufacturing, design of countermeasures, and threat assessment. Pulse measurements are an especially challenging area for signal analysis due to a combination of factors. Fortunately, many of the improving signal processing and analog-digital conversion technologies behind the generation of complex pulse environments also enable new techniques for effective pulse analysis.

A real-time spectrum (density) measurement of a multi-emitter signal environment.

In the past, basic pulse measurements generally were made with swept spectrum analyzers. The intermediate frequency (IF) bandwidth or resolution bandwidth (RBW) of the spectrum analyzer was generally narrower than the effective bandwidth of the pulse, so the spectrum analyzer was used to measure the resulting pulse spectrum. The pulse spectrum could then be used to measure basic signal characteristics such as pulse repetition rate or interval (PRI), duty cycle, power, etc. Spectrum analyzers were also used in more traditional ways to make out-of-band measurements such as spurious and harmonics of pulsed signals.

Though indirect and slightly clumsy, the pulse spectrum approach was adequate for simple pulses and signal environments containing only a single pulse train, and where frequency agility was low or could be inhibited. Modern systems use much more complex pulses, and many signals or signal environments include different pulses (along with other signals) from one or multiple emitters, as shown in the real-time spectrum measurement of Figure 1. The combination of complex signals and detailed measurement requirements means that pulse measurements must now be made using digital signal processing (DSP) techniques on digitally sampled signals.

Choosing RF/Microwave Hardware and Software

Measurement of time-varying signals is often described as a sequence of three steps. This is a convenient and useful descriptive summary, though the steps are not always as linear and independent as the diagram suggests.

A critical first step is to choose the main measurement hardware platform. Rapid increases in signal analyzer bandwidths and improved resolution in digital oscilloscopes are constantly changing the tradeoffs that affect pulse measurements. Two different RF/microwave hardware measurement platforms are generally used for this purpose: signal analyzers with a wideband digital IF, and oscilloscopes or digitizers with a sampling rate high enough to directly handle microwave RF/microwave signals at baseband.

The two hardware front end approaches are conceptually similar for most pulse measurements. In both cases, the output of the RF/microwave front end (including subsequent processing) is a stream or data file of I/Q samples of the signal or signal environment. The principal architectural difference is the location of the analog-to-digital conversion (ADC) operations and the type of processing used to focus analysis on the frequency band of interest. Signal analyzers use a fundamental or harmonic analog mixing process and analog filters to convert RF or microwave signals to an IF section where ADC operations are performed. Oscilloscopes (and other time domain samplers such as modular digitizers) sample the RF or microwave signals directly in a baseband fashion, and subsequent downconversion and band-limiting is performed by DSP.

While signal analyzers and oscilloscopes can make many of the same measurements, the best choice in a hardware front end is often dominated by two performance requirements: bandwidth and dynamic range. The high-speed ADCs in RF/microwave-capable oscilloscopes provide extremely wide bandwidth and good phase linearity. In contrast, the slower ADCs and bandwidth filters of the signal analyzers provide higher dynamic range. Where their bandwidth — now as wide as 1 GHz — is sufficient, they have a greater ability to detect and measure small signals, or to handle both large and small signals at the same time.

One practical advantage of the signal analyzer as a measurement platform is that it can support seamless switching among swept, vector, and real-time measurements in a single instrument. By using smart external mixers, this single instrument — via a single user interface — can provide these capabilities over wide bandwidths and up to 90-GHz operating frequencies.

Once a stream of wideband sampled signal data is available, a variety of software solutions are available to meet different analysis needs. Two major types of software are generally used. Built-in software and installable measurement applications have been available for oscilloscopes for some time, and their analysis is focused primarily on pulse timing parameters and time domain measurements. Built-in applications extend pulse analysis to the frequency and time domains in signal analyzers with wideband capability.

The functional blocks of pulse or signal environment measurements. Since the result of signal acquisition is a set of digital samples, the signal can be stored and post-processed (or reprocessed) in a flexible fashion.

Vector signal analysis (VSA) software is the second type of software applicable to pulse analysis. VSA software can be used with many RF/microwave front ends, including signal analyzers, oscilloscopes, and modular digitizers. VSA software performs time domain analysis, but is particularly useful when frequency domain analysis and demodulation (or modulation quality analysis) is needed. VSA software captures multiple pulses and extensive measurement of pulses one at a time.

Real-time spectrum analysis (RTSA) is also useful in pulsed signal environments. RTSA was originally implemented as a separate analyzer type, because the wide bandwidth of RF/microwave pulse analysis required dedicated RTSA hardware. Fortunately, recent improvements in processing power have made this a practical measurement application to add to general-purpose signal analyzers, at initial purchase or as an upgrade. RTSA involves gap-free processing of signal samples, or at least minimizing gaps so that analysis will not miss even very infrequent events. RTSA can be useful for finding elusive signals, and can also be important for triggering pulse analysis.

Combining these pulse measurement solutions can be especially powerful in meeting certain measurement challenges. For example, RTSA can be a uniquely effective tool for generating acquisition triggers for subsequent measurements made by VSA software or pulse measurement applications.

Pulse Analysis Measurement

The process of pulse analysis is often described in terms of three principal steps: triggering, signal acquisition, and measurement or analysis (Figure 2). Triggering can be understood as a general process of time alignment for acquisition of pulse data, since the signals under test are time-varying. The time alignment may involve an explicit trigger from an external source, or it may be generated in one of several ways by the acquisition hardware itself. For regularly repeating signals, the required time alignment may also be a simple matter of choosing a suitable measurement interval via a time gating function.

Acquisition can be as short as a single frame, or a lengthy recording that is intended for post-processing. The recording can be continuous or segmented, with some unnecessary data discarded to improve effective memory length. The bandwidth of the signal acquisition can be focused on the spectrum occupied by a single pulse or a wider signal environment or band, which includes many different ones, and may contain other signals as well.

Measurement can be single frame, or post-processing with analysis that can establish triggering or some form of time alignment or reference to the measurement. In the case of signal capture or recording using VSA software, the center frequency and span of measurement may be altered after time capture.

In understanding the pulse measurement process, the first step above may involve additional complexity: triggering may be derived from some later measurement/analysis processes such as an RTSA frequency mask trigger (FMT). This can make the complete measurement process somewhat recursive.

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