Understanding Spectrum Analysis

Transcription

1 Understanding Spectrum Analysis Swept, FFT, and RTSA principles E & T Team September 2017

2 Operation principles of SPA architectures Agenda Part I Chasing dynamic and transient signals Probability of intercept which signal can I see Swept SPA principle FFT SPA principle NLTL Sampler Principle Stepped FFT Principle VSA SPA Principle RTSA SPA Principle 2 Copyright ANRITSU

3 Chasing dynamic and transient signals Analysis of elusive signals? Signal parameters When did it occurs? How long does it last? Where did it occur? What is the center frequeny? What is the bandwidth? What kind of modulation format? You may search in either frequency domain and / or time domain 3 Copyright ANRITSU

4 Chasing dynamic and transient signals Analysis of elusive signals? Frequency Domain is best for identifying what is happening in the spectrum Static and / or dynamic behaviour Continuous, bursty Modulated Time Domain is best for identifying when something occurs At a specific time Repetition rate In a relative sense versus other events SPA must have High frequency Adequate Dynamic Range Vector Signal Analysis (VSA) capability SPA must have a bandwidth wider than the signal of interest tigger capabilities signal capture memory 4 Copyright ANRITSU

5 Probability of Intercept What signal can I see? Probability of Intercept is the key specification in order to detect a bursty or transient signal Is the minimum duration of a signal that can be observed with a 100% probability and without amplitude errors Factors that determine the POI Sampling Rate Time-Record-Length (or FFT size) Windowing function Window size Overlap processing Noise Floor T Sweep = SPAN k RBW 2 RBW = Resolution Bandwidth TSweep = Sweep Time k = Filter Skirt Factor (2-3 for Gaussian filters) POI = R + T R + R T = Duration of the signal of interest R = Listening time at frequency R = Time not listening R+R = revisit time ~R = RBW + S T SPAN S BW = spectral width of the signal 5 Copyright ANRITSU

6 S w e p t S p e c t r u m A n a l y z e r p r i n c i p l e 7 Copyright ANRITSU

7 Swept Spectrum Analyzer principle Heterodyne Spectrum Analyzer Heterodyne principle is the basis for most spectrum analyzers Input signal converted to an intermediate frequency mixer and a local oscillator (LO) Signal is swept past a fixed-tuned filter to determine resolution bandwidth Signal is then logarithmically amplified and passed to the display This pure historical architecture is more or less out of the market 8 Copyright ANRITSU

8 Swept Spectrum Analyzer principle Basic operation of an analog Spectrum Analyzer 10 Copyright ANRITSU

9 Swept Spectrum Analyzer principle Basic operation of an analog Spectrum Analyzer - RBW influence 11 Copyright ANRITSU

10 Swept Spectrum Analyzer principle POI for Swept Spectrum Analysis Swept sweep of Swept SPA If the green line is crossing a signal you can see it on the display SPA is missing any signal during it s sweep re-visit time (blind time) 12 Copyright ANRITSU

11 Swept Spectrum Analyzer principle POI for Swept Spectrum Analysis Increasing the sweep speed is resulting in a higher number of signal crossings Nevertheless, there is always a re-visit time and therefore a SPA blind time 13 Copyright ANRITSU

12 Swept Spectrum Analyzer principle Catching up signals without gaps LO is at a fixed frequency Instantaneous Bandwidth (Analysis Bandwidth) allows capture and analysis of a technically limited portion of the spectrum MS28xxA up to 1 GHz MS2720T 15 MHz IQ Capture or 20 MHz demodulation bandwidth MS2710xA 20 MHz IQ data bock or 2,6 MHz streaming mode Gap free capture for wide bandwidths Best at measuring short duration signals that are infrequent or occur periodically MS2710xA has pre-trigger capability T min = [ window size + time record length 1 P] f Window Size Time Record Length P f s = FFT window length in #points = FFT bin size in #points = Overall FFT processing points = sample rate 14 Copyright ANRITSU

13 Swept Spectrum Analyzer principle Swept SPA Summary A swept LO with an assigned RBW Covers much wider span as RTSA Good for events that are stable in the frequency domain. Magnitude ONLY, no phase information (scalar info). Captures only events that occur at right time and right frequency point. Data (info) loss when LO is not there. 15 Copyright ANRITSU

14 F F T S P A p r i n c i p l e 17 Copyright ANRITSU

15 FFT Spectrum Analyzer principle FFT Spectrum Analyzer - Analog frontend with digital IF and processing Converts time domain signals into frequency domain signals using a Fourier transform Digitizes a sampled signal and applies the FFT Very fast and much more stable and accurate than analog design Digital RBW filter faster than analog filters FFT much faster Higher filter skirt factor Easy to have lots of RBW & VBW filters Just DSP coefficients Easy to do 1 Hz RBW Easy to do Linear or Log processing Log much better than analog o o o ~ 0.1 db/100 db for digital ~ 1 db/10 db, max 1.5 db for analog Digital ~ 15x better (1.5 db vs. 0.1 db) 18 Copyright ANRITSU

16 FFT Spectrum Analyzer principle FFT Spectrum Analyzer - Analog frontend with digital IF and processing 19 Copyright ANRITSU

17 FFT Spectrum Analyzer principle Basic FFT Relationships 21 Copyright ANRITSU

18 FFT Based Spectrum Analysis Some more real example Remember, there are half as much frequency points as there are time points Example: SPAN: 20EE+06 Hz N: # bins in spectrum (theorectical) Alialising cuttoff: 0, # bins in spectrum (real) separated each time 6250 Hz f s : T s : T Record : 51EE+06 Hz 19,53EE-09 sec 160,00EE-06 sec f min : T Record Hz f max : (2 * T s ) -1 25,60EE+06 Hz RBW = f bin : T Record -1 = f S * N Hz 20EE+06 Hz (without aliasing) 22 Copyright ANRITSU

19 FFT Based Spectrum Analysis Some more real example Remember there are half as much frequency points as there are time points Time Record Length N t = 160 μs N = 8192 RBW = 6,25 khz 23 Copyright ANRITSU

20 FFT Based Spectrum Analysis Some more real example The length of time record T Record determines frequency span and resolution RBW Exampel: T Record ': 2 * T Record T Record ': 320,00EE-06 sec N': N * f S N': RBW' = f bin ': T Record ' Hz increasing time record will reduce RBW (down to 1 Hz), but will require more and more memory points Noise Bandwidth = DANL -174 dbm/hz + NF + 10 log10(bin [Hz]) T record N 160EE EE EE ,28EE ,56EE ,12EE Copyright ANRITSU

21 FFT Based Spectrum Analysis Some more real example We know, that the length of T Record determines frequency span and resolution. How to make the SPAN smaller? bandwidth reduction from 25,6 MHz to 12,8 MHz (aliasing free 10 MHz) 12,8 MHz SPAN just requires 25,6 MSps sampling frequency doubling T Record and reducing sampling to f s /2 is resulting in 8192 FFT points with 3200 bins with a spacing of 3,125 khz process of doubling the time record and halving the span can be repeated by using multiple stages of digital filtering FFT processor operates then at constant number of points and the resulting FFT will N points from DC to f max How to start the SPAN somewhere other than DC? Just digital filtering of the input is resulting always in a SPAN from DC to f max Multiplying the incoming signal with a complex sine wave will frequency shift the signal Heterodyning allows the SPA to compute zoomed spectra starting at frequencies other than DC 25 Copyright ANRITSU

22 FFT Based Spectrum Analysis Spectral Leakage & Windowing Implied periodicity of the Fourier Transform assumes x(t) is periodic in T Sampled Time Record Sampled Time Record Implied Periodicity One spectral line no leakage Spectrum with leakage 27 Copyright ANRITSU

23 FFT Based Spectrum Analysis Minimize leakage by appropriate windowing In order to minimize the sidelobes a window other than a rectangular one is chosen. The input time samples are multiplied by an appropriate window function which brings the signal to zero at the edges of the window. Window selection is primarily a tradeoff between main lobe spreading and side-lobe roll off. 28 Copyright ANRITSU

24 FFT Based Spectrum Analysis Window Characteristics Comparison Gaussian window is used when trying to resolve closely spaced signals of similar amplitude. Power spectrum is also a Gaussian function! Flat Top window is used for resolving closely spaced signals with large amplitude differences. bin RBW is influenced by ENBw of window type Parameter Uniform Hanning Gaussian Flat Top Leakage Performance: Poor Rel. Good Best Good Requency Resolution: Poor Rel. Good Best Better Equivalent Noise Bandwidth: 1,0000Δf 1,5000Δf 2,2150Δf 3,8194Δf 3 db Bandwidth: 0,8844Δf 1,4380Δf 2,0910Δf 3,7670Δf Filter Skirt Factor: 716 : 1 9,1 : 1 4,0 : 1 2,45 :1 Max. Amplitude Error: 3,92 db 1, 42 db 0,68 db < 0,01 db Highest Sidelobe: -13 db -31 db -125 db -95 db Sidelobe fall-off: - 20 db/dec db/dec db/dec db/dec. 29 Copyright ANRITSU

25 FFT Based Spectrum Analysis Window Characteristics Comparison Comparision of Rectangular and Gaussian window in Time Domain Blue indicates strong leakage (Rectangular Window) with a large number of side lobes, red shows leakage greatly reduced by the Gaussian Window function. The window function selected in the time domain defines the filter shape of the measurement bandwidth in the frequency range. 30 Copyright ANRITSU

26 FFT Based Spectrum Analysis Instantaneous Bandwidth Real Time Bandwidth Instantaneous bandwidth (IBW) refers to the bandwidth in which all frequency components can be simultaneously captured and analyzed. IBW is also commonly called the analysis, modulation, or IF bandwidth T meas N FFT (T acq T FFT T ) LO N FFT T acq T FFT T LO number of FFTs required to form the desired span is the time required to collect the necessary amount of data per FFT is the computational time required for the FFT itself is the switching time of the LO 31 Copyright ANRITSU

27 FFT Based Spectrum Analysis FFT Overlapping The before presented way to gain IBW has several disadvantages that avoid Near Real-Time or Real-Time Signal Analysis. the before mentioned blind time the disadvantage of the windowing functions; that suppress greatly the original data at the window edges whereby these data samples do nearly not contribute to the FFT analysis result than the data at the center of the window the dependency between frequency and time resolution of the FFT. For good frequency resolution, the time record has to be long, resulting in a poor time resolution and vice versa. 32 Copyright ANRITSU

28 FFT Based Spectrum Analysis FFT Overlapping Signal bandwidth is less than the real-time bandwidth, this means we do have some spare time and can invest this to do something different while the DSP processor is waiting for the next time record data block. If instead of waiting for an entirely new time record we overlap the new time record with some of the old data and we will get a new spectrum as often as we computed the FFT. Each new displayed spectrum contains some information from the previous spectrum. Very short spectral event (particularly one that does not even last as long as one FFT frame) can be seen, even if at reduced amplitude The advantage this provides is visibility of the very-short time variations within a signal. Very good amplitude accuracy 33 Copyright ANRITSU

29 FFT Based Spectrum Analysis FFT Overlapping 34 Copyright ANRITSU

30 FFT Based Spectrum Analysis Another example gapless capturing Gapless capturing of a 5 MHz wide SPAN SPAN = 5 MHz, f s = 2 fmax 1,28 = 12,8 MSps; RBW = bin bandwidth = f s /N = 6,25 khz N = 2048, T Record = N t S = 160 μs (time for one FFT) 6250 FFTs for a time period of 1 sec (12,8 MSps) 35 Copyright ANRITSU

31 FFT Based Spectrum Analysis Another example gapless capturing To capture a signal with 100% POI and correctly measure its level, a minimum signal duration corresponding to two FFT frames is required T Record = μs = 320 μs Focus is to detect a short signal rather than measuring the level accuracy FFT frame overlapping of e.g. 50% is reducing T Record down to 240 μs Better amplitude accuracy requires higher overlapping 36 Copyright ANRITSU

32 FFT Based Spectrum Analysis Real overlapping example for MS2840A Selected parameters SPAN = 125 MHz RBW = 1 MHz FFT-length N = 2048 points ADC operating 14 bits Sampling Rate = 200 Msps 37 Copyright ANRITSU

33 FFT Based Spectrum Analysis Real overlapping example for MS2840A Example 1 1 Time for one FFT point 6 fs ns Time Record Length FFT window length 5ns ,24 s FFT calculations per second 1 FFT window length 1 10, / sec s 1 1 Overlap delay time 1 s 6 RBW 1 10 Overlap Factor 1- RBW 2 T RecordLength RBW 10 khz 30 khz 100 khz 300 khz 1 MHz #FFT points FFT windows length 1,31E-3 327,68E-6 163,84E-6 40,51E-6 10,24E-6 Waiting time (1/RBW) 100,00E-6 33,33E-6 10,00E-6 3,33E-6 1,00E-6 FFT Overlap 84,7% 79,7% 87,8% 83,5% 80,5% Theoretical shortest detectable event with 100% POI is equal to 2 T Record = ~20 μs With an overlapping of 50% its is ~15 μs, with 81% ~18 μs 38 Copyright ANRITSU

34 39 Copyright ANRITSU

35 FFT Spectrum Analyzer principle Advantages of FFT spectrum analyzer technology Fast capture of waveform: In view of the fact that the waveform is analyzed digitally, the waveform can be captured in a relatively short time, and then the subsequently analyzed. This short capture time can have many advantages - it can allow for the capture of transients or short lived waveforms. Able to capture non-repetitive events: The short capture time means that the FFT analyzer can capture non-repetitive waveforms, giving them a capability not possible with other spectrum analyzers. Able to analyze signal phase: As part of the signal capture process, data is gained which can be processed to reveal the phase of signals. Waveforms can be stored Using FFT technology, it is possible to capture the waveform and analyze it later should this be required. 40 Copyright ANRITSU

36 FFT Spectrum Analyzer principle Disadvantages of FFT spectrum analyzer technology Frequency limitations: Cost: The main limit of the frequency and bandwidth of FFT spectrum analyzers is the analogue to digital converter (ADC), that is used to convert the analogue signal into a digital format. While technology is improving this component still places a major limitation on the upper frequency limits or the bandwidth if a down-conversion stage is used. (There are forthcoming 12-bit ADCs with 6.5 Gbps sampling rate. Looking at IF bandwidth in an instrument such as a VSA, 1 GHz is probably the best that can be done today, and 2 GHz in the next 18 months or so) The high level of performance required by the ADC means that this item is a very high cost item. In addition to all the other processing and display circuitry required, this results in the costs rising for these items. 41 Copyright ANRITSU

37 FFT Based Spectrum Analysis Difference between FFT SPA and FFT on an Oscilloscope Oscilloscope also sample signals in Time Domain and do FFT processing afterwards. What are the differences compared to SPA? The ADC selection follows different target applications SPA requires a large ADC dynamic range Osc requires an ADC with a very high sampling rate in order to catch and display steep signal slopes The ADC quantization depth depends on max. sampling rate Original and Quantized Signal Higher sampling rate is resulting in lower quantization depth Quantization depth is the number of bits used to sampel a value Quantization depth drives also quatization noise and thus the resulting dynamic range For SPAs with a high dynamic range it is requested to use ADCs with a high quantization depth and a lower sampling rate 42 Copyright ANRITSU

38 FFT Based Spectrum Analysis Difference between FFT SPA and FFT on an Oscilloscope SPA / VSA bit ADC > 100 db Dynamic Range Heterodyne design Low noise floor Time Domain resolution typ. 20 ns (rendering of rise or fall time) SFDR typ. ~60 dbc Allows to zoom into the signal (Start, Stop, RB, VW) MSO Oscilloscope 8 bit (high bandwidth up to 100 GHz) bit (low bandwidth) db Dynamic Range Samples RF directly (>>f S ) Higher noise floor Time Domain resolution 3-5 ps (100 GHz) SFDR typ. ~45 dbc SPA always from 0 Hz to f Max ; no zoom-in; just change of sampling frequency; manual calculation of e.g. RBW Very high waveform update rates Multi channels with synchronous triggering 43 Copyright ANRITSU

39 FFT Based Spectrum Analysis Difference between FFT SPA and FFT on an Oscilloscope 44 Copyright ANRITSU

40 FFT Based Spectrum Analysis Performance differences between Signal Analyzer and Spectrum Analyzer mode Example: Pulse signal PRF 1ms and PD 500 ns Spectrum Analyzer mode Wrong signal representation PD PRF 45 Copyright ANRITSU

41 FFT Based Spectrum Analysis Performance differences between Signal Analyzer and Spectrum Analyzer mode Example: Pulse signal PRF 1ms and PD 500 ns Spectrum Analyzer mode Wrong signal representation Signal Analyzer mode Correct signal representation 46 Copyright ANRITSU

42 FFT Based Spectrum Analysis Overview Benchtop Product Portfolio 47 Copyright ANRITSU

43 FFT Based Spectrum Analysis Overview Product Portfolio MS2830/40/50A Full featured signal analysis in the lab up to 43 GHz (1 GHz) Up to 300 GHz with external mixers MS2720T Full featured spectrum analysis in the field Up to 43 GHz MS2710xA Monitoring, Multilateration, Demodulation Ext. SW Packages Up to 6 GHz MS2760A mmwave spectrum analysis 32, 44, 50, 70, 90, 110 GHz solutions for the lab, field or manufacturing line MS2760A Spectrum Master EMEA webinar Copyright ANRITSU

44 N o n L i n e a r T r a n s m i s s i o n L i n e ( N L T L ) W h a t i s t h i s? 49 Copyright ANRITSU

45 NLTL Technology Principle of Nonlinear Transmission-Line (NLTL) Zl, A uniform Non-Linear-Transmission-Line (NLTL) is a high-impedance line loaded periodically by reverse biased Schotty diodes serving as voltage-variable capacitors. Under reverse bias, a diode behaves as a non-linear capacitance Strong input signals will generate harmonics and mixing products of the applied input signal L Zl Cl Zl RD CD(U) d cpw fc Z 1 l Cl Cj The NLTL cell consists of a diode connected between the center conductor and ground at the junction between two sections of CPW. [2] Copyright ANRITSU

46 NLTL Technology Principle of Nonlinear Transmission-Line (NLTL) NLTL form a propagation medium whose phase velocity and thus, time delay, is a function of the instantaneous voltage Copyright ANRITSU

47 NLTL Technology Principle of Nonlinear Transmission-Line (NLTL) For a step-like waveform, the trough of the wave travels at a faster phase velocity than the peak This results in compression of the fall time and as a result, the formation of a steep wave front that approaches that of a shock wave Copyright ANRITSU

48 NLTL Technology Principle of Nonlinear Transmission-Line (NLTL) At high negativ voltages, the capacitance of the diodes is small and the velocity is fast. If the input voltage waveform has a negativ going function, the first part of the wave propagate slower as the following parts. So the fall time is becoming shorter and shorter as the wave propagates along the line Copyright ANRITSU

49 NLTL Technology Principle Nonlinear Transmission-Line (NLTL) based Sub-Harmonic Sampler Shocklines are Non-Linear Transmission Lines (NLTL) Shocklines efficiently generate narrow impulses at microwave and millimeter wave frequencies. Anritsu VNA/SPAs incorporate patented NLTL technology made at an in-house chip fab. Used in sampling receivers to measure amplitude and phase of the VNA stimulus. Generates power for VNA source and RX LO signals Copyright ANRITSU

50 NLTL Technology Principle Nonlinear Transmission-Line (NLTL) based Sub-Harmonic Sampler Differentiation of Shockline waveform is resulting in a train of very narrow pulses in the time domain Short pulses in the time domain are resulting in a highly harmonic rich spectrum up to the mm-wave ranges N th harmonic is used to downconvert signal under measurement Copyright ANRITSU

51 S t e p p e d F F T S P A p r i n c i p l e 56 Copyright ANRITSU

52 Stepped FFT principle Operation principle Multiple FFTs are concatenated to create a span significant higher as the IF bandwidth High speed LO and digitizer list mode enables fast stepping across span Software stitches together FFTs and displays single trace result up to several tens of GHz 57 Copyright ANRITSU

53 M S A S p e c t r u m A n a l y z e r p r i n c i p l e 58 Copyright ANRITSU

54 NLTL Technology NLTL in a spectrum analyzer Sampler FFT VBW DET Copyright ANRITSU

55 NLTL Technology MS2760A basic function principle Direct ADC sampling for 9 khz to 24.5 MHz Conventional mixer used for 24.5 MHz to 6.15 GHz without preselection filtering NLTL sampler-based conversion for GHz in a customized MMIC unique software algorithms to minimize image responses which may appear under certain use cases when wideband modulated and multi-tone signals are being analyzed Copyright ANRITSU

56 Swept Spectrum Analyzer principle Homodyne Spectrum Analyzer A single local oscillator is used to shift the incoming RF signal down to baseband. Baseband contains two paths, I-path and Q- path, corresponding to In-phase and Quadrature paths. Each path is then digitized separately. The direct conversion receiver has benefits over the super-heterodyne receiver in terms of bandwidth and compactness However, the super-heterodyne receiver, in general, is capable of more dynamic range than the direct conversion receiver. 61 Copyright ANRITSU

57 NLTL Technology Stepped FFT frequency representation Stitching together FFTs to cover span User Span 20 MHz 20 MHz 20 MHz 20 MHz FFT FFT FFT averaging for VBW 20 MHz Capture Low Side sweep FFT Capture Time Take Min Hold Level of both Display MHz Capture High Side sweep Copyright ANRITSU

58 NLTL Technology High side / low side spur rejection Low-side LO conversion: f LO < f RF High-side LO conversion: f LO > f RF Take a HIGH SIDE and a LOW SIDE measurement compare and take the min value at each point sample and display the results Copyright ANRITSU

59 NLTL Technology Impact on frequency hopping signals Every trace is a comparison of a HIGH SIDE and a LOW SIDE sample to remove unwanted images and spurs Copyright ANRITSU

60 NLTL Technology Stepped FFT frequency representation Stitching together FFTs to cover span User Span 20 MHz 20 MHz 20 MHz 20 MHz FFT FFT FFT averaging for VBW 20 MHz Capture Low Side sweep (manual disable possible) FFT Capture Time (additional manual setting) Take Min Hold Level of both Display MHz Capture High Side sweep (manual disable possible) Copyright ANRITSU

61 NLTL Technology A success story of unique advantages Parameter NLTL based advantage Customer benefit Simplified product architecture Stability Bandwidth Dynamic Range Size Cost Monolitic designs reduce number of dicrete parts and connectors Integrated chip design greatly reduces the temperature variations across constitents Extremely wide RF sampler bandwidth allows one sampler to cover broad frequency range Over 100 db across all frequency bands High performance in a very small form factor Improved capability-to-cost ratio enables new applications Lower maintenance cost, reduced down time and operating cost Longer intervals between calibrations, better measurement accuracy and repeatability. Lower cost for making high-performance measurements over broader frequency ranges Better characterization of RF devices Direct connection to DUT, smaller footprint, light weight Dramatic cost reduction for high frequency testing in engineering, manufacturing and field Copyright ANRITSU

62 MS2760A mmwave SPA On-Wafer spectrum mm-wave multiplier measurement Forget the cables and take the measurement up to 125 GHz right where you want it with the MS2760A

63 MS2760A mmwave SPA Simultaneous VNA and Spectrum Analyzer On-wafer Measurements to 110 GHz NLTL module direct connect to probe. SPA measurements thru GHz Anritsu coupler. Full band sent to Spa through V connector and db coupled port.

64 V e c t o r S i g n a l A n a l y z e r p r i n c i p l e 70 Copyright ANRITSU

65 VSA SPA principle Basic architecture Vector Signal Analyzer (VSA) 71 Copyright ANRITSU

66 VSA SPA principle The digital side of a VSA 72 Copyright ANRITSU

67 VSA SPA principle VSA Summary A parked LO with a given IF BW Collects IQ data over an interval of time. Performs FFT for time- frequency domain conversion Captures both magnitude and phase information (vector info) Data is collected in bursts with data loss between acquisitions. 73 Copyright ANRITSU

68 R T S A p r i n c i p l e 74 Copyright ANRITSU

69 RTSA SPA principle Outcome and conclusion RF tu Trig 75 Copyright ANRITSU

70 Real Time Spectrum Analysis Catching up signals without gaps The phrase real-time analysis mean different things to different people In a spectrum or signal analyzer with a digital IF, real-time operation is a state in which all signal samples are processed continuously and gap-free for some sort of measurement result or Spectrum (amplitude versus frequency) Time Domain (power versus time) triggering operation In most cases the measurement results are scalar power or magnitude Non real-time operation: CALC includes computation of an FFT or a power spectrum as well as averaging, display updates, and so on. 76 Copyright ANRITSU

71 Real Time Spectrum Analysis Catching up signals without gaps The phrase real-time analysis mean different things to different people In a spectrum or signal analyzer with a digital IF, real-time operation is a state in which all signal samples are processed continuously and gap-free for some sort of measurement result or triggering operation In most cases the measurement results are scalar power or magnitude Real-time operation: 77 Copyright ANRITSU

72 Real Time Spectrum Analysis Catching up signals without gaps T refers to the analyzer s time record for FFT processing ( n FFT points) The analyzer collects a block of contiguous samples from the ADC that is digitizing the IF signal and then performs an FFT to obtain a power or vector spectrum. It is important to understand that the time length of the block T will be shorter when bandwidths are higher and sampling is faster A loss of data refers to the frequent case where the samples in a time record are "windowed" this means attenuated at the beginning and end of the record, to avoid spectral leakage or to form the equivalent of different resolution bandwidth filters. This attenuation of data at the edges of the time record is often so significant that some samples are effectively lost. The solution to this problem is called Overlap Processing. 78 Copyright ANRITSU

73 Real Time Spectrum Analysis RTSA simplified block diagram ADC Real-time correction and decimation Time Domain Processor Overlap Memory FFT Processor Power vs Time trace memory Spectrum trace memory Density trace memory Frequency Mask trigger Display Processor 81 Copyright ANRITSU

74 Real Time Spectrum Analysis RTSA Summary A fixed LO w/ a given IF BW Collects IQ data over an interval of time. Data is corrected and FFT d in parallel Vector information is lost Advanced displays for large mounts of FFT s 82 Copyright ANRITSU

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