Analyze Agile or Elusive Signals Using Real-Time Measurement and Triggering Ben Zarlingo, Agilent Technologies Inc.

Analyze Agile or Elusive Signals Using Real-Time Measurement and Triggering Ben Zarlingo, Agilent Technologies Inc.

This Webcast Agile & Elusive Signals Discovering Signals vs. Troubleshooting, Optimizing Two Case Studies Dynamic signal environment-ism band Radar signal Choosing & Using Techniques & Tools Make the most of what you already know & own Swept spectrum analyzers Vector signal analyzers/vsa software Oscilloscopes Real time analyzers Using Triggering to Enhance Measurement Effectiveness 2

Case Study #1: 2.45 GHz ISM Band 100 MHz Wide Spread Spectrum Techniques Lightly Regulated, Signals Not Explicitly Coordinated WLAN (typically dominant occupant), Bluetooth, Cordless Phones And 1 kw+ microwave ovens! And almost anything else! Very Dynamic Content not known at any specific point in time May be very low occupancy Lots of Collision Tolerance May Be Needed Potential Cliff Effect at Some Degree of Crowding 3

ISM Band View: Swept Spectrum Analysis Single Sweep (fast but retrace not as fast) Peak Hold (short) Band: 100 MHz Span: 160 MHz Peak Hold (long) Dynamics X2 (swept RBW & changing signals) Confuse the View Peak Hold Detects Signals but Frequency Overlap Obscures Signals Typically Short Duty Cycle, Total Low Occupancy POI is Approx. Low (useful concept even if not strictly applicable) 4

ISM Band View: Real Time Analyzer Density or Histogram Display Gap-Free Analysis No Signals Missed Density Display Shows Signals Inside Signals Fast, Full Display Update Rate Shows Signal Dynamics See Unexpected Behavior, Even If Infrequent Individual Display Updates Combine Thousands of Spectra 5

ISM Band View: Real Time Analyzer Spectrogram Display Top Trace is Latest or Selected Spectrum Same Spectral Data as Density/Histogram Individual Spectrogram Line Represents Many Spectra (typically thousands) View into Dynamics, Timing Acquisition Time Setting Affects Way Spectra are Combined to Form Display Update WLAN Signals Active Continuously? What are Vertical Bars? A Multitone Signal? 6

Spectrogram Display Adjusting Acquisition Time Shorter Acquisition Time Determines Amount of Data Processed for Spectrum or Spectrogram Trace Update Each Trace Update & Spectrogram Line Represents Fewer Spectra More Trace Updates/Second, Finer Time Resolution Spectrogram Covers Shorter Interval Determines Time Scale of Spectrogram Use Spectrogram Trace Buffer to Examine Signal Dynamics 7

Characteristics of the Real Time Analyzer Density View Fast Display Updates, High Data Density Easily Understand Signal or Event Frequency Find Very Infrequent Events Find Unexpected Events, Behavior Limitations Combining traces combines behavior from different times Limited flexibility (RBW, span combinations) Limited frequency span Power spectrum only Markers only on combined spectrum or density at a point 8

What is Real Time Analysis? Understanding Real Time Analyzers and Real Time Analysis General Definition of Real Time Measurement operations where all signal samples are used in calculating measurement results of some kind (usually spectrum) Real Time Bandwidth (RTBW) The widest analysis bandwidth where an analyzer can maintain real time operation Duration of maintaining real time operation is not specified; it may assumed to be short term or long term or unlimited Current Usage for Signal Analyzers A spectrum or FFT analyzer having a signal processing path where most or all samples, even at wide bandwidths, are used to create a spectral display or to trigger signal measurement or acquisition (sometimes both) 9

Real Time Operation In Real Time Operation the Analyzer s Processing (CALC) is Fast Enough to Keep Up with All Data Samples However some data may still be lost CALC Time Includes FFT or Power Spectrum, Averaging, Display Updates, etc. 10

Overlap Processing If Processing is Faster than Sampling, Perform Additional FFTs With Partially-New Time Records as Samples Come In Overlap 0% Overlap 50% Processor Idle Window Function Avoid Loss of Data Due to Windowing Accurate Amplitude Measurements of Short Duration Signals 11

Probability of Intercept (POI) Treated Informally Here Finding signals important to you, expected or unexpected Reasonable effort and time to discovery Signals found can be measured as necessary Knowledge of signals may be perfect or simply adequate A More Rigorous Treatment in MicroApp From Richard Overdorf Understanding Probability of Intercept for Intermittent Signals IMS 2013 MicroApp presentation Agilent Aerospace/Defense Symposium presentation (2013) Evaluate These Techniques Against Your Requirements 12

Frequency Mask Trigger (FMT) Trigger Based on In-Band Power (scalar) Spectrum Spectral Mask Eval, High/Low Limits, Logic Trigger Generated from High Speed FFT Engine Trigger Not Time-Aligned Trigger Timing Resolution: May Be 1/FFT Period Trigger a Single Spectrum Measurement or Time Capture (VSA), etc. 13

Vector Signal Analysis: The Logical Extension and Complement to a Real Time Analyzer Superheterodyne Downconversion to All-Digital IF Adaptable to swept spectrum analysis and real time analyzer Full vector data preserved Measurements Short/Long Data Block Vector, Demodulation Capture Memory 14

VSA Spectrum of ISM Band Signals: 1 kpoint vs. 100 kpoints Much More Informative Higher POI 15

Improving POI with FFT Analyzers: Several Steps for Dramatic Improvement Use Much Longer Time Records (25,000 points or more) Time record length goes up faster than FFT speed goes down Narrow Frequency Span to the Minimum Needed Time record is longer (in time) for given number of points Use IF Magnitude Trigger, Pre/Post Delays, Holdoff Avoid measuring when no signal present in span Use Persistence or Density/History Displays Typically persistence, to make peaks last long enough to be noticed Peak Hold Result: POI Dramatically Improved, Though may not be Near 100% 16

Triggering in VSAs and Real Time Analyzers IF Magnitude & Frequency Mask Triggers Triggered Individual Measurements or Signal Captures Sqrt (I 2 + Q 2 ) of each sample, is easier & faster (VSAs) FFT power Spectrum is slower but more selective (RTSAs) VSA architecture can do both IF Mag or Freq Mask Trig Measurements Short/Long Data Block Vector, Demodulation Capture Memory 17

Flexible Triggering to Initiate Capture IF Magnitude Trigger Frequency Mask Trigger Pre/Post-Trigger Delay Adjustable Power Level Delay Holdoff time, type Other Types Channel External/TTL Periodic Magnitude 18 Freq. Mask

VSA Attributes for Detailed, Flexible Analysis The Next Steps After Finding a Signal Detailed Signal Analysis, Including Vector, Demodulation Time Capture, Playback Connection to Many Signal Analzyer Front-Ends Signal analyzers Oscilloscopes Digitizers Multi-Channel Analysis, Including Channel Time Alignment Including multi-channel capture & analysis Beam-forming, MIMO, space-time coding Multi-Band Analysis, Multi-Measurements Excellent Complement to Single-Channel RTSA 19

Agilent 89600B VSA Signal Capture: Just Press the Red Button Use Existing Center, Span (Wider is ok) Press Record Then Press Play Adjust Capture if Desired Length (seconds, samples, time records) or use default Input Range Frequency Center, Span Better, Faster Insights from Measuring Same Signal with Different Settings 20

Control Playback Speed with Overlap How Time Records Step Through Capture Memory Higher Overlap: Slower Playback Overlap Adjustable 0% to 99.99% (full speed to 1/10,000 speed) 50% 90% 98% 21

Gap-Free, Overlapped Spectrogram: Analysis of Amplitude, Frequency, Time, See Everything At Once See Entire Event at a Glance Find Unexpected Behavior Select Any Trace From Deep Buffer and Measure 22

Example ISM Band Capture WLAN, Bluetooth, Microwave Ovens 16.7 ms 23

Focus on a Time and Frequency Region Marker CF, reduce span Play back until signal appears Click/drag analysis region, position

Span, Center Freq Change to Analyze, Demodulate 2.5 MHz span, Bluetooth demod preset, short result length

Use Start/Stop Times to Train Adaptive EQ Simple Gated Spectrum Measurement or Train Equalizer on Selected Time Segment Read Equalizer Coefficients to Understand Channel Frequency Response Time (impulse response) Using Gated Spectrum Using Demodulation Result 26

Save a Time/Frequency Portion of Capture Use Capture, Triggering to find What You Want Select the Signal or Time Region Desired Change Center Frequency and/or Span Save Segment for Later Analysis, Playback, Re-Use 27

Pass Captured Data to Other Tools, Processes Captured Data can be Fully Time and Frequency Corrected Data can be Fully Alias Protected Save as MATLAB, CSV, Text Send as ARB Register Data 28

Case Study #2: S-Band Acquisition Radar Raster Scanning Pulse Width 6 μs PRI 600 μs 7 Pulses, 10 MHz Spacing, Stepping Low - High Frequency Pulses -30 MHz to +30 MHz 29

Case Study #2: S-Band Acquisition Radar Fast, Clear Signal View Blue Indicates Very Low Duty Cycle Amplitude Varies over Seconds Set Persistence Long Peaks Consistent

S-Band Acquisition Radar Spectrum Analyzer View Peak Hold Many Measurements Required Signal Still Not Clear Dynamics Not Shown 31

S-Band Acquisition Radar Real-Time Spectrogram View Long Acquisition Time, Long Persistence Excellent for Long- Term Signal View (seconds) Spot a Pattern Spot Big Pulse Set Many Spectra (default 10,000) Combined, Pulses Still Shown Together 32

S-Band Acquisition Radar Spectrogram with Short Acquisition Time Short-Term Signal View (ms to 1+ seconds per screen) A more Rapid Pattern is Revealed Fewer Spectra Combined, ( 33+), Pulses May Now be Separated 33

Vector Signal Analysis Same Setup, Then Capture & Post-Processing Overlap Frequency Mask Trigger? IF Magnitude Trigger? Positive/Negative Trigger Delay? Capture Without Trigger? Spectrogram Shows Timing Adjust Overlap and FFT Length (# points) 34

Vector Signal Analysis Gap-Free Post-Processing Reveals an Anomaly Defects Hidden in one Mode or View are Obvious in Another Triggering Not Necessary for Repeating Signal Use Magnitude Trigger To Capture Highest Signal Levels 35

VSA Post-Capture Tune/Zoom Digital Resampling, Digital Local Oscillator Focus on Signal of Interest, Filter Out Other Signals Select Analysis Time/Interval Use Gating, Windows (uniform/rect. here, pulse is self-windowing) Any Analysis Type, Including Vector, Demodulation 36

Start With Problem/Need, Choose Best Tool Swept Spectrum Analyzer (especially with digital IF) Familiar meas, user interface; compatible with established std s, practices Frequency flexibility including span, RBW, VBW Maximum accuracy, dynamic range, sensitivity Measurement flexibility: phase noise, noise figure, ACPR, EMI, apps, etc. Real Time Analyzer Find elusive signals, some ability to characterize (spectrogram, PVT) Spot unknown signals, signal behavior Monitor spectrum, trigger on spectral behavior Vector Signal Analyzer (VSA software on signal analyzer platform) Detailed vector analysis and demodulation Signal capture, flexible playback Post-processing for signal selection, re-use Single user interface for multiple hardware platforms, multi-channel analysis Modern Signal Analyzer Hardware May Support All Three 37

Triggering: Find & Capture Signal of Interest Powerful Measurement Leverage Easy, Effective Ways to Monitor Environment for Signal Look for Expected and Unexpected Signals Avoid Measuring When No Signal Present Monitor Other Frequency Bands? Trigger on Other Activities External trigger from your circuit Oscilloscope or logic analyzer Consider all you know about signals, systems, transitions Take advantage of repeating signals, inter-signal timing, pos/neg delays Triggering Can Enhance Measurement Performance Time or synchronous averaging Periodic trigger Trigger a Time Capture 38

Compare Triggers: IF Mag. & Frequency Mask IF Magnitude Real time calculation of magnitude in selected span Precise, repeatable time alignment Negative & positive trigger delays Selectable level & polarity Selectable holdoff, holdoff type Playback IF magnitude trigger Frequency Mask Real time calculation of spectrum & test against spectral mask Upper, lower limits Build from trace & adjust or manual parameter entry Trigger timing ambiguity 1 FFT Logic: Enter/leave, in/out, enter leave, leave enter Negative & positive trigger delays 2012 Agilent Technologies

More Information 2013 IMS MicroAppp presentation: Understanding Probability of Intercept for Intermittent Signals by Richard Overdorf Agilent application note: Measuring Agile Signals and Dynamic Signal Environments April 2013 Agilent PXA Real Time Signal Analyzer Technical Overview, literature number 5991-1748EN Vector Signal Analysis Basics, Agilent literature number 5989-1121EN 40