understanding available measurement techniques for ... · understanding available measurement...

© Keysight Technologies 2015

Understanding Available Measurement Techniques for Spurious or Unknown Signals

Richard Overdorf

Keysight Technologies

Page Agenda

– A quick look at the spectrum analyzer timeline

– Overview of spur and “unknown” signal requirements

– The block diagram and what’s changed?

– Understanding the available analysis tools and trade-offs

• Important architectures in an analyzers?

• How can we manipulate the test equipment for our measurement

– Types of signals and measurement concerns

– Mapping the tool to the application

© Keysight

Technologies 2015 2

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Spectrum/Signal Analyzer - Timeline A high-level look at the ~7 decades of analyzers

1960’s through 1990’s 2000 2015 2010

Analog

swept

analysis

FFT analyzers

Hybrid swept/FFT

Real-time

analysis

First all-digital IF

Fast sweep

Real-time in a

commercial

swept analyzer

Real-time

stitching

Time domain scan

© Keysight

Technologies 2015 3

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Signal Search

– Monitoring

• SIGINT, surveillance, verifying transmission…

- General swept monitoring, capture everything, identification…

– System performance (Spurs. and Harms.)

• Radar/EW transmissions

- Pulsed and dynamic

• Satellite

- Large range spurious requirements

• Electro-mechanical: Antenna (moving), vehicles…

- Longer amount of time for different issues

– Standards

• Internal (product specs), EMI Standards (MIL-STD-461)…

- Correlation, uncertainty, traceability…

Why people use analyzers to look for “unknown” signals?

© Keysight

Technologies 2015 4

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Spectrum Analyzer Block Diagram Find the DUT’s small signals (and not the analyzers)

Swept analysis dynamic range (DR) is limited by three factors: 1. Distortion performance of the input mixer 2nd & 3rd order products

2. Broadband noise floor of the system. Sensitivity/Displayed Average Noise level (DANL)

3. Phase noise of the local oscillator Narrow band measurements.

Be careful for:

1. Residuals

2. Spurs

5

Block diagram of a classic superheterodyne spectrum analyzer

© Keysight

Technologies 2015

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Spectrum Analyzer Dynamic Range Distortion and noise

• Increasing input attenuation reduces harmonic

distortion from spectrum analyzer. However this

also adds more IF gain which degrades noise

floor.

• To compensate for this you can reduce the RBW

and reduce the noise floor.

6

For more information on the Keysight Web site see:

App Note 150: Spectrum Analyzer Basics (pub no. 5952-0292.pdf) © Keysight

Technologies 2015

http://literature.cdn.keysight.com/litweb/pdf/5952-0292.pdf

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The “Front-End” (Part 1) At the input

Level Control – attenuators

• Help maximize dynamic range at any

frequency

• Can help match (VSWR)

Low Noise Path (LNP)

• Not a default state but can be very

helpful at improving sensitivity

• Can improve dynamic range (2nd order)

Preamplifier

• Helps with sensitivity but not dynamic

range

7

0-3.6 GHz low band

3 Hz-26.5 GHz

Input

Cal input

2 dB-step mech atten

μW converters

RF converter

2nd converter

3.5-26.5 GHz high band

2 2 6 10 20 30

RF preamp

Linearity

Corrections

μW preamp

YIG filter

with bypass

relay

1 dB-step electronic atten

Low Noise Path

© Keysight

Technologies 2015

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The “Front-End” (Part 2) The Preselector, Friend or Foe?!?

• At low frequencies preselection is

done with a low pass filter

• At higher frequencies it is typically a

YIG tuned filter

The preselector is a FOE because:

1. Limits bandwidth (predominately for

WB FFTs)

2. Degrades amplitude accuracy

3. “Sweeps” slow

The preselector is a FRIEND because:

1. Image free spectrum

2. Helps block out of band power

8

0-3.6 GHz low band

3 Hz-26.5 GHz

Input

Cal input

2 dB-step mech atten

μW converters

RF converter

2nd converter

3.5-26.5 GHz high band

2 2 6 10 20 30

RF preamp

Linearity

Corrections

μW preamp

YIG filter

with bypass

relay

1 dB-step electronic atten

© Keysight

Technologies 2015

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1960’s through 1990’s 2000 2015 2010

Analog

Swept

Analysis

FFT Analyzers

Hybrid Swept/FFT

Real-time

analysis


9

© Keysight

Technologies 2015

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The IF (Analog and Digital) What to be concerned about

Pre-filters 1. Great at making sure there is minimal

impact when multiple signals are in the IF

2. Has to be bypassed in wideband cases

The ADC

1. Resolution counts

2. Determines FFT limits

The “all-digital” IF

1. The log amplifier

2. Selectivity of the RBW

3. Frequency counter

4. Allows for a very wide range of RBW’s

5. Data manipulation without resampling

6. FFT processing (we will cover later)

7. Detectors (and multiple detectors)

Step

Gain

ADC

ASIC &

FPGAs CPU

Anti-Alias

Filter

Selectable

Pre-Filters

10

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Technologies 2015

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Display Detectors

– Input is the IF envelope detector output (log or linear scaled)

– Each output represents only one IF envelope value

– Peak and neg peak detectors bias the output value, perform

some data reduction

– Depending on choice the value can change >3 dB

Example: Peak, Negative Peak, and Sample

Time

Volts

Peak

Neg Peak

Sample

Display points or buckets

Peak, Avg,

Sample…

ASIC &

FPGAs

11

© Keysight

Technologies 2015

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Log-pwr (video) No VBW3XRBW

Log Average Output RF spectrum

Log-pwr (video) Yes Log Peak Spurious,

harmonics

Pwr (RMS)

Voltage

Pwr (RMS)

Pwr (RMS)

Trace Avg

No VBW3XRBW

Pwr Peak/sample Carr/Ph. Noise

No VBW3XRBW

Lin Sample RF envelope, rise/fall

No VBW3XRBW

Pwr Average ACPR

No VBW3XRBW

Pwr RMS Channel power, non-

sine mod.

Video BW Average Scale

Log/Lin/Pwr Detector Measurement

Summarizing the Detectors

Getting the right measurement

13

© Keysight

Technologies 2015

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Understanding Uncertainty Uncertainties Budget for Noise + Distortion

Spectrum Analyzer Dynamic range must be optimized in combination with

your DUT requirements and measurement uncertainties you can tolerate.

Uncertainty versus difference in

amplitude between two sinusoids

at the same frequency Error in displayed signal

amplitude due to noise

Example of uncertainty budget: Distortion Error budget: +/- 1 dB error = -18 dBc margin relative to DUT input

Noise Error budget: +/- 0.3 dB error = 5 dB margin relative to noise floor

Maximum total error: (+/- 1 dB) + (+/- 0.3 dB) = +/- 1.3 dB (excludes instrument)

Distortion Error Noise Error

For more information on Keysight.com see: App Note 150: Spectrum

analyzer basics and http://mwrf.com/author/bob-nelson 14

© Keysight

Technologies 2015



http://mwrf.com/author/bob-nelson




Page


1960’s through 1990’s 2000 2015 2010

Analog

swept

analysis

FFT analyzers

Hybrid swept/FFT

Real-time

analysis

Fast sweep

Real-time

stitching


15

© Keysight

Technologies 2015

Up to 50x Faster vs. Traditional Sweep

Fast Sweep Traditional Sweep

For more information on the Keysight .com see:

Using Fast-Sweep Techniques to Accelerate Spur Searches (pub no. 5991-3739EN)

Full 26.5 GHz span Full 26.5 GHz span

© Keysight

Technologies 2015 16

http://literature.cdn.keysight.com/litweb/pdf/5991-3739EN.pdf





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The Sweep Time Equation Balancing sensitivity & test time

RBW = Resolution Bandwidth Filter

ST = Sweep Time

k = the constant of proportionality ST = k (Span)

RBW2

The rise time of a filter (RBW) is inversely

proportional to its bandwidth, and if we

include a constant of proportionality, k,

then: Rise time = k/RBW

RBW has a squared

relationship with time.

Noise floor change* =

10 log (BW2/BW1)

Where

BW1 = starting resolution bandwidth

BW2 = ending resolution bandwidth * Peak-detectors do not accurately represent the noise floor.

17

42 ms (300kHz RBW)

4.2 sec (30 kHz RBW)

100x delta in sweep time

with a 10x delta in RBW

-10 dB

Note: The k value varies based on a number of conditions including filter shape for

RBW, VBW and detector types. Generally a value of 2 or 3 for Gaussian filters. © Keysight

Technologies 2015

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What is “Fast-Sweep”

Method to “correct” for over-

sweeping

– Algorithm in proprietary ASIC

• Understands filter response and corrects for

RBW size, frequency, and amplitude errors

– Improvement can be limited in some

areas

• Averaging for noise like signals

• FFT based sweeps

– Very effective in speeding up swept

measurements with RBWs ~10 kHz or

more

© Keysight


Improvements over traditional methods Standard Sweep

Fast Sweep

For more information on Keysight.com see:

Using Fast-Sweep Techniques to Accelerate Spur Searches (pub no 5991-3739EN)






Compensated RBW rise time with improved accuracy

ST = k (Span)

RBW2

Modified

k value

The Sweep Time Equation - Fast Sweep

Resolution Bandwidth Accuracy

Red: FS1

Green: Accuracy (no FS1)

RBW size

RB

W r

atio

Time

© Keysight

Technologies 2015

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Benefits of Fast Sweep Repeatability

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Comparing fast sweep to traditional sweep, the lower values and shallower slope of the blue data points

(fast sweep) show that repeatability is improved and varies less with sweep time.

Holding sweep time constant while

using a narrower RBW to measure CW

signals reduces measurement variance

because the narrower filter blocks more

of the broadband noise.

For more information on the Keysight.com see:

Using Fast-Sweep Techniques to Accelerate Spur Searches (Pub no. 5991-3739EN)






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Sweeping with FFTs

– FFT stitching is not new to signal

analyzers

• Analyzer swaps modes

automatically

• Dynamic range and speed choices

• Some analyzers give control of FFT

size

– What’s changing?

• FFT algorithms continue to improve

• Larger more capable ADCs

• Moore’s law presents opportunity for

speed upgrades

For smaller RBWs in wider spans

© Keysight


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“Sweeping” with FFTs For smaller RBWs in wider spans

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Stepped Spectrum Analysis

– Modern signal analyzers

• Wide FFTs in excess of 200 MHz

with similar dynamic range

• Faster processors

• Better analog approaches

– Modular instruments

• Wide FFTs in excess of 100 MHz

• Improved digital rejection

algorithms

• Processing power improvements

• High speed LOs

• More analysis in similar footprint

Some new technologies and approaches

© Keysight


Single FFT up to max

analysis BW (160 MHz)

Span up to frequency range of analyzer (27 GHz)

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Real-time Analysis

– LO is stationary and the

instrument’s ASIC and

FPGAs are processing data

at a very rapid speed

– Some differences from

swept

• Banded, not analyzing

full frequency range

• Dynamic range

- Narrow-band filtering

- Preselection

• No time gaps in band

Significant digital signal processing

Freq

Time

© Keysight


The FFT At first glance

Samples

Window Window

27

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Technologies 2015

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Real-time Analysis

– Signal amplitude is not accurate if the signal does not last for the

entire FFT

– The size of the window and the rate at which the samples are

processed determine the duration of signal that can be measured

accurately

What happens when the signal comes in?

Signal On Instrument FFTs

Samples

28

© Keysight

Technologies 2015

The Probability of Intercept Specification • FFT clock running faster than the sample rate

• The larger the difference the more the overlap

• Overlap points; P = (1024*(Fclk – Fs)/Fclk)

• POI specification: Tmin = (Window + 1024 – P – 1)/Fs

Samp

Rate

(MHz)

Span

(MHz)

Overlap

(points)

Duration

for 1024

Window

(usec)

Duration

for 512

Window

(usec)

Duration

for 256

Window

(usec)

Duration

for 128

Window

(usec)

Duration

for 64

Window

(usec)

Duration

for 32

Window

(usec)

200 160 341 8.53 * 5.97 4.69 4.05 3.73 3.57

150 120 512 10.23 * 6.82 * 5.11 4.26 3.83 3.62

100 80 682 13.65 * 8.53 * 5.97 4.69 4.05 3.73

50 40 853 23.88 * 13.6 * 8.52 * 5.96 4.68 4.04

25 20 938 44.40 * 23.9 * 13.6 * 8.52 * 5.96 4.68

29

© Keysight

Technologies 2015

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Real-time RBW/POI Curve

31

RBW 6

RBW 5 RBW 4

RBW 3

RBW 2

RBW 1

2

3

4

5

6

7

8

9

10

-110 -105 -100 -95 -90 -85 -80 -75

Tim

e (

µs)

Noise Floor (dBm)

100% POI versus Noise Floor 1 GHz, Preamp Off

PXA

© Keysight

Technologies 2015

Page


1960’s through 1990’s 2000 2015 2010

Analog

swept

analysis

FFT analyzers

Hybrid swept/FFT

Real-time

analysis


Fast sweep

Real-time in a

commercial

swept analyzer

Real-time

stitching

32

© Keysight

Technologies 2015

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Real-time Stitching

– Uses a combination of the

real-time engine and the

stepped FFT

– Not real-time across the

full frequency range of the

instrument

– Enables the full frequency

range of the instrument

– Helpful with low duty cycle

repetitive signals

What’s the difference?

Freq

Time

Period of interest

33

© Keysight

Technologies 2015

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Summarizing the Techniques We’ve Covered

Analog sweep

– Improved sweep speeds ideal for

measurements with wider RBWs

and large spans

– Great dynamic range

– Not so good at finding intermittent

signals

© Keysight


Analog Sweep, FFT Sweep, Real-time, Real-time Stitching

FFT “sweep”

– Significant improvements in DSP

have helped decrease sweep times

while holding dynamic range

– Very large FFTs have trade-offs

(preselection and spurs)

Real-time

– Unparalled capability at finding

small duration signals

– Trade-off of finding low level

signals is the signals have to last

longer

– Limited in DR, BW, flexibility

Real-time stitching

– Very useful in finding short duration

signals in a given period of time

– Limited in dynamic and

measurement range

– Sweep time

Page


1960’s through 1990’s 2000 2015 2010

Analog

swept

analysis

FFT analyzers

Hybrid swept/FFT

Real-time

analysis


Fast sweep

Real-time in a

commercial

swept analyzer

Real-time

stitching

Time domain scan

36

© Keysight

Technologies 2015

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Time Domain Scan (TDS)

– TDS processes a large chunk of FFTs with a high amount of overlap:

• Allows for multiple RBWs and also very good amplitude accuracy

• Allows for processing more difficult detector types (simultaneously)

Another signal processing technique

freq

am

plit

ud

e

freq

am

plit

ud

e

Receiver

FFT BW Receiver

Resolution BW

Swept or Stepped Scan Time Domain Scan

37

© Keysight

Technologies 2015

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Understanding the End Requirements

– Monitoring

• Heavily concerned with catching everything and less concerned

with accuracy


• Early stages: design and design integration

- Wants to know where the problem(s) are coming from

• Middle stages: design verification and pre-production

- Broad and “deep” characterization, wants to decrease test

times

- Measurement accuracy

• Final stages: production

- Traceability, repeatability, uncertainty and test time

– Standards testing

• Heavily concerned with traceability, accuracy, repeatability

Mapping a test goal to the instrument capabilities

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Where the Capabilities Match

– Monitoring

• Best: Streaming; Good: RTSA; OK: Swept (WB FFT, Fast)


• Early stages - Best: RTSA, Swept (WB FFT, Fast); Good: RTSA

Stitching

• Middle stages - Best: Swept (Narrow FFT, Fast); Good: Swept

(WB FFT)

• Final stages - Best: Swept (Narrow, Fast); Good: TDS (EMI)

– Standards testing

• Pre-scan - Best: TDS, Swept (All); Good: RTSA Stitching

• Compliance - Best: Dictated; Good: Swept (Narrow),

My suggestions but certainly not the law…

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Summary

– Understanding the test goals can allow you to take advantage of “non-

standard” equipment settings which can be apart of your current test

equipment

– A general knowledge of the test equipment will help you map the trade-offs

and select the right method for the test goal

– Depending on the analyzer, there are multiple advancements that can be

valuable for significantly reducing test times, improve repeatability and make

a more efficient measurement

• Digital IF

• Fast Sweep

• FFT Sweep

• RTSA

• Stitched RTSA

• TDS

What have we learned?

© Keysight


Page


1960’s through 1990’s 2000 2015 2010

Analog

swept

analysis

FFT analyzers

Hybrid swept/FFT

Real-time

analysis


Fast sweep

Real-time in a

commercial

swept analyzer

Real-time

stitching

Time domain scan

© Keysight


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