New Pulse Analysis Techniques for Radar and EW
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; however, pulse measurements are an especially challenging area for signal analysis due to these factors:
Wide pulse bandwidth — the result of short pulse duration and fast transitions.
Complex signal environments containing pulses from a number of different sources, often with dramatically different characteristics such as bandwidth, repetition rate, and modulation type.
Pulse environments with wide dynamic range in the pulses to be analyzed or created.
Pulses with complex modulation that must be demodulated and decoded or measured.
Pulses that are difficult to detect due to very low duty cycle, intermixing with other signals, and low apparent power level at the analysis point.
Fortunately, many of the signal processing and analog-digital conversion technologies behind the generation of complex pulse environments also enable new techniques for effective pulse analysis.
Pulsed Signals and the Challenge of Signal Acquisition
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 a number of different pulses (along with other signals) from one or multiple emitters. 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.
A critical first step is to choose the main measurement hardware platform. Rapid increases in signal analyzer band-widths and improved resolution in digital oscilloscopes are constantly changing the tradeoffs that affect pulse measurements. Two different RF/microwave hardware measurement platforms — shown in Figure 1 — 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 are now available to extend pulse analysis to the frequency and time domains in signal analyzers with wideband capability.
Vector signal analysis (VSA) software can be used with many RF/microwave front ends, including signal analyzers, oscilloscopes, and modular digitizers. It performs time domain analysis, but is particularly useful when frequency domain analysis and demodulation (or modulation quality analysis) is needed.
Real-time spectrum analysis (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. 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, by definition, 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 understanding the pulse measurement process, the first step may involve additional complexity: triggering may actually be derived from some later measurement/ analysis processes such as an RTSA frequency mask trigger (FMT). The steps in the previous process may be performed individually by separate devices, or multiple steps — including the entire pulse measurement — may be performed by a single analyzer (Figure 3).
Acquisition hardware can take several 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.
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. Its principal drawback is the measurement of small signals in the same frequency span as larger ones, where the small signals do not have a known or stable time relationship with larger ones that are used for the IF magnitude trigger.
DSP speed has advanced to the point that spectrum calculations (albeit of limited flexibility) can be performed in real time with bandwidths up to 510 MHz. The power spectrum results can then be tested against a set of limit lines or a mask, providing a powerful frequency selective trigger.
Frequency mask trigger (FMT) is particularly 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. While FMT operates only in the frequency domain, signal recordings can be processed in the time, frequency, and modulation domains. The recordings include data acquisition timing information, allowing complete characterization of the elements of a signal environment and any cross-domain relationships.
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.
As baseband samplers, oscilloscopes typically lack the sophisticated combination of time and frequency domain triggers; 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 ultrawide bandwidths are increasingly important in pulse applications. 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. 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.
The principal choices for RF/microwave front ends in pulse analysis are signal analyzers and oscilloscopes. An alternative solution is to use an RF/microwave signal analyzer as a downconverter, along with a suitable oscilloscope crowave signal analyzer as a downconverter, along with a suitable oscilloscope to digitize the IF output of the signal analyzer. Performance will be similar to that of the oscilloscope, with the frequency coverage of the selected signal analyzer. Analysis bandwidths of over 1 GHz can be obtained from this configuration, providing an economical solution for applications with bandwidths over 500 MHz and/or when microwave and millimeter frequencies are involved.
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. 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 previously, the transfer and processing of this baseband data can also result in slower measurement throughput.
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. Some applications require more macroscale measurement capability that gathers information from hundreds or thousands of pulses, and organizes analysis results in tabular or graphical form.
The collective analysis of large numbers of pulses can reveal behavior that is otherwise difficult to spot or to quantify. An understanding of time domain parameters such as pulse width, duty cycle, and rise/fall time is essential for working with pulse modulation. Once the user defines the range for a valid pulse width and rise/fall time, pulse analysis can sync the pulses automatically and report the time domain parameters in a pulse table.
The increasing performance and complexity of radar and EW signals are matched by increasing DSP capabilities and measurement application solutions, paired with new RF/microwave front end hardware. This new hardware offers a substantial performance improvement in the combination of bandwidth and dynamic range, an improvement that is evident in both signal analyzers and oscilloscopes.
This article was contributed by Keysight Technoloiges, Inc., Santa Rosa, CA. For more information, visit here .
University of Rochester Lab Creates New 'Reddmatter' Superconductivity Material...
MIT Report Finds US Lead in Advanced Computing is Almost Gone - Mobility...
INSIDERElectronics & Computers
Airbus Starts Testing Autonomous Landing, Taxi Assistance on A350 DragonFly...
Boeing to Develop Two New E-7 Variants for US Air Force - Mobility Engineering...
PAC-3 Missile Successfully Intercepts Cruise Missile Target - Mobility...
Air Force Pioneers the Future of Synthetic Jet Fuel - Mobility Engineering...
Specifying Laser Modules for Optimized System Performance
The Power of Optical & Quantum Technology, Networking, &...
How to Achieve Seamless Deployment of Level 3 Virtual ECUs for Automotive...
Manufacturing & Prototyping
Tailoring Additive Manufacturing to Your Needs: Strategies for Performance and...
Driver-Monitoring: A New Era for Advancements in Sensor Technology
Electronics & Computers
Leveraging Machine Learning in CAE to Reduce Prototype Simulation and Testing
Real Time Physiological Status Monitoring
ArticlesMechanical & Fluid Systems
Reducing the High Cost Of Titanium
Solving Military Satellite, Radar and 5G Communications Challenges with...