Nostr notes by SDR-Radio (Unofficial)

SDR Television v1.1.7 SDR Television v1.1.7 October 26th, 2026, ...

2026-10-25T03:52:12Z

SDR Television v1.1.7

SDR Television v1.1.7
October 26th, 2026, Important bug fix and enhanced user interface. I've been using ChatGPT to help with both DSP and user interface design. Here we see the result of interface design.
Receiver
Finally found the cause of a bad memory leak. Every time
User Interface
Added an Octave Spectrum display, this is selected on the
Receive
and
Transmit
displays from the main dropdown, sub-menu "Audio Display..." as shown below.
Added a new Style, SDR Television - Modern. Select the style from the
Style
dropdown in the top right-hand corner of the main display.
Downloads are at the bottom of this page.
SDR Television v1.1.6
September 14th, 2026. To avoid confusion with the test versions of 1.1.5, this kit is now 1.1.6.
Receiver
Improved Gardner timing recovery.
Improved Sum-product decoding.
Redesigned soft decoder.
Redesigned RRC matched filter.
Note: this kit should be able to gain a dB or so sensitivity when detecting the frame header. v1.1.6 will have even better frame detection, hence better weak signal decoding.
LDPS Settings, important!
Open this page, select
Defaults
. There is no need to adjust the Sum-product optons, these options are only for development and may be hidden in future kits.
Transmit
Provider changed from SDR Television to SDR TV, some receivers only display a maximum of 18 characters in the provider field.
Downloads are at the bottom of this page.
SDR Television v1.1.4
User Interface
Rearranged ribbon bar, added Forward Error Correction (FEC) to the Home panel.
Receiver
Currently trying to improve LDPC decoding for low SNR. Part of this work involves reducing CPU load in the LDPC decoder. Planning to add advanced decoding in v1.1.5.
Changed the list of FEC / LDPC algorithms, removing min-sum and min-sum correction.
Optimised offset min-sum algorithm to reduce CPU load.
Optimised Bose-Chaudhuri-Hocquenghem (BCH) processing.
Added sum-product.
Corrected LLR soft scaling for QPSK.
Changed default RRC filter length to 12 symbols, previous value of 30 was too long.
Updated Feed-Forward AGC with a design from CoPilot.
Optimised Gardner TED RRC filter length, now 25 taps.
Increased the receive RRC filter rolloff factor by 25% to capture more energy with weak signals.
Reset LDPC after 1 second of inactivity.
FEC - LDPC
There are now two LDPC algorithms,
Offset min-sum, and
Sum-product.
Sum-product is more sensitive than offset min-sum but currently doesn't work at all for FEC rates lower than 1/2 (1/4, 1/3 and 2/5). When sum-product is selected and the rate is lower than 1/2 then offset min-sum is used. The default damping settings for sum-product are shown below.

The aim is to work on sum-product with the help of ChatGPT in 2027 to get the maximum possible sensitivity.
Downloads are at the bottom of this page.
SDR Television v1.1.3
User Interface
Fixed erroneous cursor flicker in Settings help text and Receive, Transmit panes.
Fixed error in the spectrum squelch logic.
Minor fix/improvement in QPSK LUT lookup for low values.
Receiver
Use highest possible quality decimation for symbol rates of 333ksps and lower.
Added optional Doppler correction for LEO/MEO satellites such as
MARMOTsat
(July 2026). This will most probably be tweaked when the DVB-S2 transmissions started.
Downloads are at the bottom of this page.
SDR Television v1.1.2
June 12th, 2026: Add option to disable audio, thus making more bits available for video, this is for use in DATV contests.
Note: 66 ksps sample rate is still experimental, will be improved.
Downloads are at the bottom of this page.

SDR Television v1.1.1
June 9th, 2026: Fixes a fatal bug in the Settings, Camera page.
SDR Television v1.1
May 18th, 2026:
This is the official v1.1 release
, code exactly the same as 1.0.16 which has survived testing for a few weeks. Lots of improvements since the last official kit. As with any software project, there's always room for improvements and new features, but for now here's a stable solution which works well with the QO-100 satellite.
Many thanks to Sigi and the DATV test team.
To Do
The items below may be completed for a future release.
Correct a possible logic error when a change of PCR is received; audio and video frames in the presentation queue are unnecessarily discarded.
Add 8PSK.
Update the manual, add a Quick Start chapter.
For noisy decoding, consider:
Applying adaptive algorithms to estimate and correct I-Q mismatch in real-time.
Techniques like blind source separation (BSS) or least mean square (LMS) filters can effectively compensate for mismatches.
https://rahsoft.com/2025/01/31/i-q-mismatch-in-communication-systems/
Release Notes
v1.016 (same as v1.1)
Transmitter
Better detection of OBS Studio running state; started or not started.
The selecting a video source the list is reloaded as the state of a source may have changed and become available.
Using Microsoft Video Resizer DSP (CLSID_CResizerDMO).
Optional: Extra resolutions added when using OBS-Camera (OBS Studio) and/or other video sources.
Tested 360 x 180 using 125 ksps, support for 66 ksps will be added in a future release.
Resolutions and frame rates are now sorted high to low in the Transmit, Video selection window.
v1.0.15
Receiver
Switching channels is now faster if the background threads have data to process.
Transmitter
Worked around a problem with the OBS VirtualCamera plugin which offers 'bogus' FPS values. The resolution and frame rate set in OBS Studio is the first value returned when enumerating OBS-Camera, all other values are now ignored.
Video input throttling is now disabled in callbacks to the Encoding window - this was an early attempt to limit video data rates.
User Interface
Added Always On Top button to Ribbon Bar, Home, Layout. When selected, SDR Television stays on top of other programs.
v1.0.14
User Interface
FFT Resolution added to Settings: Spectrum: Display, FFT
Matched filter roll-off and length moved from Receive: Configuration to Settings: Receive: Frame Search.
Gardner timing recovery using better dot product code.
v1.0.13
Receiver
Gardner timing recovery and IQ imbalance are now always enabled.
Gardner timing recovery filters now use fewer taps as the symbol rate increases.
Updated frequency tracking by adding a rotator (NCO) after the SDR data decimation (NCO, Downconverter). This help with signal locking and possibly decoding of weak signals.
The Decoding Overlay now shows
Tracking
instead of
Offset
. The first value is the offset applied to the IQ data from the SDR, the second the current offset from the first value. The first value is updated when the second value is greater than 1/10,000 of the symbol rate.
User Interface
Updated the ribbon bar.
Spectrum FFT resolution reduced from 100% to 25% as there's no need for a super-sharp display.
v1.0.13
Receiver
Frequency Tracking: Fixed error in the receiver matched filter; in the Dot Product processing the filter coefficients were not conjugated, so the frequency offset was roughly doubled instead of being corrected!
The dot product of two complex vectors is defined as the sum of the products of the corresponding entries of the two sequences of numbers. For complex vectors, the dot product is calculated using the complex conjugate of the second operand. This is important because the dot product is a bilinear form, which means it is symmetric and positive definite.
Use Interface
Start Stream cannot be deselected while sending.
In the Receiver panel, Bit Rates all meta data packets (PAT, SDT etc.) are shown as Other.
v1.0.11
Receiver
Dish Alignment features:
Optionally speak the MER value using Microsoft's text-to-speech.
Display either the average MER or (new) peak MER, peak being better for alignment.

v1.0.10
Receiver
SDR-Radio kits dated March 18th (build 4008) or later now have the Pluto/LibreSDR previous filtering logic restored:
Up to and including 2MHz bandwidth use a standard bandpass design with Blackman windowing. There is neither decimation nor interpolation in the Pluto.
There is more gain but the filtering quality is reduced.
Recommended: 2.5MHz and above use pre-defined generic filters from the Analog Devices AD9361 library libad9361-iio. These are the cleanest filters, however the output signal is ~1dB lower than firmware-based solutions from Evariste Courjaud, F5EOE.
User Interface
The current QO-100 channel selection is highlighted.
Added 175% and 200% to the Decoding Overlay, Font Sizes. Useful when using a laptop for dish adjustment, larger text size makes reading MER easier.
v1.0.9
Receive
Replaced Gardner timing error detection (TED) with a corrected design based on the book "Digital Communications: A Discrete-Time Approach" by Michael Rice. This uses a second-order continuous-time phase locked loop (figure C.2.2). This implementation is designed to be robust when unexpected IQ samples are processed.
AGC Target default set to 1.
Changed IQ Imbalance design, now using code from Airspy (Youssef Touil, Leif Asbrink).
Removed 'None' from the list of bandpass filter options as it is superfluous.
General
Fixed startup error "Encountered an improper argument" when no microphones are available.
User Interface
When transmitting, the resolution and frame rate are shown in the transmit timer. If either value is greater than the recommended default, a warning symbol is added.
v1.0.8
Receive
Fixed a bug with RMS calculation in IQ Imbalance.
Tuned IQ Imbalance for weak signals.
v1.0.7
User Interface
The audio and video windows now resize the selection dropdown to the width of the main windows.
Receive
Improved carrier tracking, used when there are no pilots. This benefits weak signals with low FEC values, for example FEC (LDPC) 1/4.
Release Notes
v1.0.6
Transmit
A FIR filter at the end of the IQ generation processing was too narrow and too sharp, must have been degrading reception of signals created with previous kits. This filter is now wider and uses fewer taps.
Pluto Support
(This is part of SDR Console.) The FIR filter support for the Pluto has been corrected:
Images reduced if not completely removed.
Transmit now works reliably when the Pluto bandwidth is greater than 2 MHz.
The digital filter uses a FIR design from Analog Devices, taken from the file ad9361_baseband_auto_rate.c in the libad9361-iio library.
The analog filter is unchanged. It will appear wider than expected to allow for roll-off.
With QO-100 and other DVB-S2 operation there really is little if any need for any filtering.
v1.0.5
Receive
Added optional inversion of the incoming signal inversion (swap I and Q).
Transmit
Now setting the Quantization Parameter (QP) to the default value of 26. I had come across this before but never really understood what it does. I was just querying the value, not trying to set it.
https://www.oupree.com/knowledge/Whats-Quantization-Parameter-QP-in-Video-Encoder.html
v1.0.4
Receive
Improved initial frequency detection when searching for a signal.
Optional advanced signal detection options.
Found and fixed a bug causing a lockup when a 188 byte packet checksum failed.
v1.0.3
Testing
Added optional Gaussian noise when in loopback mode, this is for development use only.
User Interface
Added display of elapsed time to the Receive Audio window. Gaps in reception of up to 10 seconds are allowed. Later use of AI may support a 'speech quality' metric, determined from quality and technical content.
Added tuning bars in the spectrum, these show the frequency and symbol rate for the current mouse position.
Added extra categories in the Log window, now Main, Radio, Transmit, Debug.
Audio and video windows optionally hide controls when the cursor leaves the corresponding window.
Receiver
Added initial Gardner timing error detection (TED).
Changed AGC algorithm, target output now ~0.7 using 32-bit IEEE floats.
Redesign #1 of the IQ to Transport Stream (TS) processing, still plenty of refactoring to do.
Changed default LDPC algorithm.
Fixed BCH implementation which was not correcting any bits, also not detecting errors.
Changed default matched filter (RRC) defaults.
Added Quadrature Imbalance correction.
Interpolation filter roll-off dynamically updated with the value in the base-band header byte MATYPE-1. This value is also shown in the Decoding overlay and Decode Status window.
Refined the list of receive filter bandwidths.
AGC
Added selectable reference level.
Added alternative algorithm, Feed forward (the default algorithm is Feedback). Feed forward is experimental.
Searching for a signal in the noise uses considerable CPU, especially at higher symbol rates. The spectrum data is now used to detect the possible presence of a signal, thus saving CPU while a signal is not present. When a signal is believed to be present:
The arrows at the top, bottom of the bandwidth display are filled in,
The receiver is enabled.
Transmit
The transmitted BB Header roll-off value in byte MATYPE-1 now matches the roll-off value used in the filter, rather than preset to 0.35.
Removed 12-bit camera formats, problem discovered using a new DELL laptop.
Added optional transmit signal inversion (swap I and Q).
Pluto
Pluto RX FIR Filter gain set to 6, example: "RX 3 GAIN 6 DEC 1 TX 3 GAIN 0 INT 1 ..."
Removed Pluto/Libre bandwidths below 1.5 MHz.
Changed filter taps for bandwidths above 2MHz, was 64 now 128. Signal is now cleaner, ~40dBm SNR.
From the AD9363 product sheet: "The AD9363 transmitters use a direct conversion architecture that achieves high modulation accuracy with ultralow noise. This transmitter design produces a best-in-class Tx EVM of −34 dB, allowing significant system margin for the external power amplifier (PA) selection."
v1.0.2
Improved receiver sensitivity, still not as good as it can be.
v1.0.1
Fixed fatal bug in receive audio AGC when the output device sample rate was 192kHz.
Download v1.1.7
The minimum version of SDR Radio is shown below. You must download it if SDR Television displays an error message when starting.
SDR Television
Download from either:
OneDrive:
SDR Television v1.1.7, 64-bit, 2026-10-24_0530.exe
Dropbox:
SDR Television v1.1.7, 64-bit, 2026-10-24_0530.exe
SDR Radio
Requires a SDR Radio 64-bit kit build 4008 March 18th, 2026 or any newer kit.
OneDrive:
SDR-Radio V3.4, 64-bit, 2026-03-18_1731.exe
Dropbox:
SDR-Radio V3.4, 64-bit, 2026-03-18_1731.exe

https://www.sdr-radio.com/sdr-television-v1-1-7

#rss #feeds #linksharing #qwibbit

Better SSB Demodulation Can We Build a Better SSB Demodulator? ...

2026-10-16T05:22:46Z

Better SSB Demodulation

Can We Build a Better SSB Demodulator?
Improving weak-signal SSB reception
Over the years, SDR Console has benefited from improvements in digital signal processing, noise reduction, filtering, and interference suppression. Recent work on wideband noise blanking has demonstrated that there are still opportunities to improve established SDR techniques.
Soon I'll start another interesting project:
investigating whether we can develop a better SSB demodulator.
The objective is simple: can I make weak SSB signals easier to understand, particularly when they are close to the noise floor?
This is an experimental project. There are no guarantees of improvement, but several techniques are worth investigating.
How SSB Demodulation Works Today
Most software-defined radio receivers use a relatively straightforward approach to SSB demodulation.
Once the incoming signal has been filtered and shifted to the correct frequency, the receiver extracts the real component of the complex IQ samples.
In simplified C++:
float audio = IQ.real();
That's essentially it!
Of course, a complete receiver includes frequency conversion, filtering, AGC, and other processing, but the actual coherent SSB demodulation is remarkably simple. Importantly, this method is mathematically correct. Under ideal conditions, there is no alternative demodulation formula that will magically recover additional information.
However, real-world signals are rarely ideal. They suffer from noise, fading, frequency errors, interference, and propagation-related distortion. The question is whether more sophisticated processing of the complex IQ signal, before converting it to audio, can improve the recovered speech.
1. Automatic Frequency Correction
One possibility is to improve the accuracy of SSB tuning. Unlike AM or FM, suppressed-carrier SSB has no continuously transmitted carrier that the receiver can simply lock onto. Even a small frequency error changes the pitch of the recovered speech.
For example, a station may be tuned 20 or 30 Hz away from its ideal frequency. The resulting audio is perfectly intelligible, but it is not quite correct. We could investigate an adaptive frequency estimator that analyses the received signal and automatically corrects small tuning errors.
The challenge is distinguishing genuine frequency errors from natural variations in human speech.
Potential benefit:
More accurate tuning and improved speech reproduction without manual adjustment.
2. Adaptive Phase Correction
Another interesting area is phase distortion. Radio signals can experience phase changes due to propagation, fading, and receiver imperfections. A conventional SSB demodulator simply takes the real component of the received complex signal.
But what if we could identify and compensate for unwanted phase variations before demodulation?
A simplified mathematical representation is:
z(t)=s(t)e^{j\phi(t)}+n(t)
Here, the received signal consists of the wanted SSB signal, phase variations, and noise. If we can reliably estimate the unwanted phase variations, we may be able to compensate for them before producing audio.
There is an important complication: SSB does not normally provide an absolute carrier-phase reference, and a constant phase rotation is not necessarily a distortion.
Any correction algorithm must therefore avoid introducing errors of its own.
Potential benefit:
Better audio under certain fading and phase-distortion conditions.
3. Complex-Domain Weak-Signal Processing
This is perhaps the most interesting part of the project. An SDR receiver processes complex IQ samples, containing both in-phase (I) and quadrature (Q) components. In a conventional SSB demodulator, the final audio is obtained from the real component. However, before this conversion, the complex signal contains relationships that may be useful for estimating the wanted transmission.
Could we exploit those relationships to distinguish weak speech from noise and interference?
Possible techniques include:
Complex-domain spectral estimation.
Adaptive interference suppression.
Statistical signal estimation.
Time-frequency analysis.
Processing that exploits the mathematical structure of SSB signals.
There is an important distinction here. The I and Q components are not two independent copies of the speech. In an ideal SSB signal, they are mathematically related through the Hilbert transform. We cannot simply combine them to obtain a free improvement in signal-to-noise ratio. Nevertheless, more sophisticated processing may exploit information about the signal, interference, or propagation channel that conventional demodulation does not explicitly use.
Potential benefit:
Improved recovery of weak SSB signals, especially in difficult interference or fading conditions.
4. Speech-Aware Processing
Human speech has a great deal of structure. Voiced speech contains harmonics, while the resonances of the vocal tract produce formants that change relatively smoothly over time. These characteristics provide opportunities for distinguishing speech from noise.
I have already investigated speech-processing techniques in SDR Console, and one lesson is particularly important: aggressive processing can easily damage the wanted audio. Different speakers have different voices, pitches, and spectral characteristics. An algorithm that works well with one voice may distort another.
For this project, I would prefer to concentrate initially on recovering the actual received signal rather than generating speech that an algorithm predicts should be present. Speech-aware processing may become useful later, but it should not be the starting point.
5. Carrier Estimation and Tracking
Another possibility is to estimate the missing SSB carrier frequency from the received speech. Because the carrier is suppressed, this is not straightforward. However, the statistical properties of speech may provide clues that help determine the correct tuning frequency.
I could also investigate tracking certain propagation-related phase variations. The challenge is obtaining a reliable estimate without confusing normal speech characteristics with carrier or channel errors.
This is another area worth investigating experimentally.
Proposed Signal Processing Architecture
The experimental demodulator would operate directly on the complex IQ samples, before conversion to real audio.
The proposed processing chain is:
Not every stage will necessarily prove useful. Some may provide little improvement, while others may introduce unacceptable distortion.
The purpose of the project is to find out through controlled experiments.
Development Plan
I'll develop the new demodulator in C++17 and integrate it experimentally into SDR Console.
The initial development stages will be:
V1 Conventional coherent SSB demodulator for reference
V2 Adaptive residual frequency correction
V3 Adaptive phase and channel correction
V4 Complex-domain weak-signal estimation
V5 Combined adaptive demodulator
Each version will be compared against the conventional SSB demodulator using the same recorded IQ signals.
This is important. Any claimed improvement must be repeatable and not simply the result of different signal levels, filtering, or subjective listening conditions.
Where suitable reference recordings are available, I can also make objective measurements.
What Am I Trying to Improve?
There are two main objectives.
1. Better speech quality
Improve the audio from signals that are already reasonably strong but suffer from fading, frequency errors, or distortion.
2. Better weak-signal sensitivity
Recover more intelligible speech from extremely weak signals, particularly those close to the noise floor. The second objective is the one I find most interesting. If a signal is already strong and intelligible, improvements in audio quality are welcome, but they are not necessarily revolutionary.
The real challenge is improving the intelligibility of signals that are currently difficult to copy. For example, could a new algorithm make a weak station easier to understand without the unnatural artefacts sometimes introduced by aggressive noise reduction?
That would be a worthwhile achievement.
A Realistic Assessment
There are fundamental mathematical limits to what can be recovered from a noisy signal. A conventional coherent SSB demodulator is already an excellent starting point, and under ideal assumptions it is optimal for recovering the transmitted waveform. I should not expect an enormous improvement simply by replacing the familiar
IQ.real()
operation with something more complicated.
Any genuine improvement will need to come from better estimation of the received signal, correction of impairments, exploitation of additional information, or carefully designed processing that improves intelligibility. It is entirely possible that some experiments will produce no measurable improvement. That's part of research.
The important thing is to test each idea carefully and retain only those techniques that provide a genuine benefit.
Starting Soon
Soon I'll begin work on the first experimental SSB demodulator. I'll establish a reference implementation and then investigate the most promising signal-processing techniques, concentrating particularly on weak-signal reception.
As with recent noise-blanker development, I'll use actual SDR recordings, compare the results, and refine the algorithms based on what I hear and measure.
The ultimate objective is not simply to create a more complicated demodulator.
It's to find out whether we can hear and understand weak SSB signals that are currently difficult to receive.
Whether we achieve a small improvement, a significant improvement, or discover that conventional SSB demodulation is already close to the practical limit, the results should be interesting.
I'll report our progress as the project develops.

https://www.sdr-radio.com/better-ssb-demodulation

New Noise Blanker Introduction During September and October 2026 ...

2026-10-16T03:46:03Z

New Noise Blanker

Introduction
During September and October 2026 new noise blankers have been implemented to improve what was a weak part of the SDR Console signal processing chain. Using the knowledge of ChatGPT, this exercise took only five days and the result is shown below. These noise blankers will be available sometime in October 2026.
Impulse Noise
A noise blanker should remove Impulsive interference such as electrical switching noise, electric-fence pulses, ignition interference and short static crashes
The screenshot below shows the new wideband noise blanker in action.
The top half of the main waterfall shows the noise created by an electric fence,
The bottom half shows the noise removed by the new wideband noise blanker.
Listen to a recording made using AM on 1,287kHz, a
signal is present
. In the recording the wideband noise blanker is engaged after 10 seconds then disengaged after 20 seconds. The result is good!
Listen to a recording made using AM on 709kHz, there is
no signal present, just noise
. In the recording the wideband noise blanker is engaged after 15 seconds.
Receiver Noise Blanker
Predictive Impulse Repair
Predictive Impulse Repair (PIR) is a new noise-blanking technique designed to remove short-duration impulsive interference such as electrical switching noise, electric-fence pulses, ignition interference and static crashes. Unlike a conventional noise blanker, PIR does not simply mute or zero the samples affected by an impulse. Instead, it identifies the damaged region and attempts to reconstruct the original complex IQ waveform using the undamaged signal immediately before and after the disturbance.
Impulse detection is performed in the complex IQ domain by looking for sudden changes that are inconsistent with the recent behaviour of the received signal. Adaptive background measurements allow the detector to distinguish normal signal variations from genuinely impulsive events over a wide range of signal levels. Additional amplitude-envelope analysis helps identify disturbances that might otherwise be difficult to detect, while rejecting normal modulation as far as possible.
Once an impulse has been identified, PIR determines the extent of the damaged region and replaces it using information from the surrounding clean IQ samples. Very short disturbances can be repaired by interpolation, while longer events use bidirectional predictive reconstruction. Predictions are made from both sides of the damaged region and smoothly combined, preserving both amplitude and phase continuity through the repair.
This is fundamentally different from traditional noise blanking. A conventional blanker deliberately creates a gap in the received waveform; PIR instead tries to repair the gap. This is particularly effective with SSB reception, where abrupt blanking can itself produce audible clicks and distortion. The result is that strong impulsive interference can often be removed with remarkably little indication that an impulse was ever present.
Predictive Impulse Repair operates on the complex IQ signal before demodulation, so the repair is performed while both amplitude and phase information are still available. It is modulation-independent and can therefore be used ahead of SSB, AM, SAM and other demodulators. In testing with SSB signals affected by severe repetitive impulse interference, the reconstructed audio can remain continuous even where conventional blanking would produce clearly audible artifacts.
Wideband Noise Blanker
The
Wideband Noise Blanker
in SDR Console is designed to suppress short-duration impulsive interference before it reaches individual receivers or the spectrum display.
Typical sources of impulsive interference include electric fences, ignition systems, switching equipment, electrical machinery, thermostats, power-line faults and atmospheric static. These disturbances can produce very short but extremely powerful bursts of RF energy which may cover a large part, or even all, of the received spectrum.
Unlike a conventional noise blanker which simply removes or zeros samples, the SDR Console Wideband Noise Blanker detects the damaged section of the received IQ data and attempts to replace it with a reconstructed signal.
This technique is referred to as
Predictive Impulse Repair (PIR)
.
Why Wideband?
The important difference between this noise blanker and a conventional receiver noise blanker is
where the processing takes place
.
The Wideband Noise Blanker operates directly on the incoming wideband complex IQ data, before the data is sent to the spectrum display and before individual receivers are extracted from the wideband data.
The processing chain is approximately:
SDR wideband IQ
|
v
Wideband Noise Blanker
|
+--------> Spectrum / Waterfall
|
+--------> Receiver 1
|
+--------> Receiver 2
|
+--------> Receiver 3
|
+--------> ...
This has an important advantage. An electrical impulse is normally broadband. If several receivers are operating within the same SDR bandwidth, the same impulse may be heard simultaneously in every receiver and may also appear as a vertical line on the waterfall. By detecting and repairing the impulse in the original wideband IQ stream, the interference is removed
before the individual receivers see it
.
One operation can therefore improve all receivers using that IQ stream as well as reducing impulsive interference visible on the spectrum and waterfall displays.
Processing
Impulsive Interference
An impulse can be extremely short in time while containing energy across a very wide range of frequencies. On a waterfall display this often appears as a vertical line because a single disturbance affects many frequencies simultaneously.
An electric fence is a good example. The fence produces a powerful electrical discharge at regular intervals. Although the disturbance may last only a fraction of a millisecond, its amplitude can be much greater than the wanted signals.
A traditional noise blanker attempts to detect this event and suppress it. The difficulty is deciding exactly which samples have been damaged. Remove too few samples and part of the impulse remains. Remove too many and useful received signal is unnecessarily destroyed.
The Wideband Noise Blanker therefore uses an adaptive detector which continuously examines both the received signal power and how well each new complex IQ sample agrees with the recent signal behaviour.
Adaptive Impulse Detection
The detector does not simply look for samples exceeding a fixed amplitude. A fixed threshold performs poorly with SDR signals because the received signal level and spectral occupancy can vary considerably. Instead, the Wideband Noise Blanker continuously estimates the normal background behaviour of the wideband IQ stream.
For each incoming complex sample it examines the difference between the actual sample and the value expected from immediately preceding samples. This difference is called the
prediction residual
.
During normal reception the residual follows the statistical behaviour of the received wideband signal.
When a sudden electrical impulse occurs, the received IQ can change far more rapidly than would normally be expected. The prediction residual can then increase dramatically.
The detector combines this residual measurement with signal-power information. This helps distinguish genuine impulsive interference from ordinary changes in the received signal.
The background estimates continuously adapt, so the detector automatically follows changes in normal received signal conditions.
Finding the Damaged Region
Detecting the peak of an impulse is only part of the problem. The samples immediately before and after the strongest part of the impulse may also have been corrupted. SDR Console therefore identifies a strongly detected
core
of the impulse and then examines a limited region around it to estimate where the damaged waveform actually begins and ends. This prevents a short impulse from being incorrectly expanded into a much longer event while still allowing the lower-energy edges of the disturbance to be included in the repair.
This distinction proved particularly important with real wideband recordings. The strongest portion of an electrical impulse may be very short, while the complete disturbed region can extend for considerably longer.
Predictive Impulse Repair
Once the damaged region has been identified, simply setting those IQ samples to zero is not ideal. A sudden transition from the received signal to zero, followed by another sudden transition back to the received signal, is itself a discontinuity. In the frequency domain this discontinuity can create additional broadband energy. Instead, the Wideband Noise Blanker uses the undamaged complex IQ surrounding the impulse to construct replacement samples.
SDR Console calls this technique
Predictive Impulse Repair
.
Clean IQ samples immediately before and after the damaged region are examined. The behaviour of the complex signal on both sides is used to predict into the missing region. A prediction is made forward in time from the clean signal preceding the impulse and backwards in time from the clean signal following the impulse. The two predictions are then smoothly combined across the damaged interval.
Conceptually:
Clean IQ Damaged IQ Clean IQ
--------- | X X X X X X X X | ------------
\ /
\ /
Forward Backward
prediction prediction
\ /
\ /
Reconstructed IQ
The objective is not to claim that the original RF waveform can always be recovered perfectly. Once interference has completely overwhelmed the receiver, the original information in those samples may no longer exist.
Instead, the objective is to replace a very large impulsive disturbance with a waveform which is consistent with the clean signal surrounding it and which does not itself introduce another sharp discontinuity. For impulsive interference this can be remarkably effective.
Look-Ahead Processing
Predictive repair requires samples from both sides of an impulse.
This means the Wideband Noise Blanker must wait briefly for clean samples following the disturbance before it can reconstruct the damaged region. SDR Console therefore uses a small amount of look-ahead buffering.
The additional latency is approximately
0.3 milliseconds
, which is insignificant for normal receiver operation but gives the repair algorithm access to clean IQ following the impulse.
The processing is fully streaming. The number of samples entering and leaving the noise blanker remains the same; the output is simply delayed slightly to provide the required look-ahead.
Maximum Impulse Duration
There is an important limitation to any impulse-repair algorithm. If interference destroys a sufficiently long section of the received signal, there is no mathematical method which can recreate arbitrary missing wideband RF information perfectly. For this reason SDR Console deliberately limits the duration of an event which will be automatically reconstructed.
The current Wideband Noise Blanker is designed to repair impulses up to approximately
200 microseconds
. This is long enough to handle many severe electrical impulses while avoiding increasingly speculative reconstruction of much longer disturbances. Events exceeding the permitted duration are left unchanged. This is particularly relevant to atmospheric noise.
A lightning discharge can produce anything from a relatively short impulse to a much longer static crash. The short impulsive components may respond well to Wideband Noise Blanking, but a long crash may contain hundreds of microseconds or several milliseconds of corrupted data.
There is simply not enough information available to reconstruct such a long section of arbitrary wideband RF accurately. The Wideband Noise Blanker is therefore intended primarily for
impulsive interference
, not continuous noise or long-duration interference.
Electric Fence Interference
Electric-fence interference has been particularly useful during development because it provides strong, repeatable real-world impulses. Testing was performed using recorded wideband IQ containing a powerful electric-fence pulse approximately once per second. The detector identifies the short electrical disturbances automatically. Some of the strongest events occupy considerably more than 100 microseconds of IQ data.
With Predictive Impulse Repair enabled, these pulses can be reconstructed at the wideband IQ level before the data reaches the receivers. The result is especially useful because the interference does not have to be independently detected by every active receiver. It is repaired once at the source.
Spectrum and Waterfall
Because processing occurs before spectrum generation, the Wideband Noise Blanker can also reduce the characteristic vertical streaks produced by broadband electrical impulses on the waterfall. This is an important difference from an audio noise blanker.
An audio noise blanker may make a particular receiver more pleasant to listen to, but the original impulse is still present in the SDR's wideband IQ and therefore remains visible on the spectrum and waterfall.
Wideband processing attacks the interference earlier in the signal chain.
Independent of Demodulation Mode
The Wideband Noise Blanker operates on complex IQ before demodulation, so it does not need to know whether a receiver is subsequently using:
SSB
CW
AM
Synchronous AM
FM
Digital modes
The impulse detection and repair take place before these modulation-specific operations. This makes Wideband Noise Blanking particularly useful when several receivers using different modes are active simultaneously.
Threshold
The
Threshold
control determines how sensitive the detector is to impulsive events.
A lower threshold makes the detector more sensitive and therefore more likely to classify unusual signal transitions as interference.
A higher threshold makes detection more conservative.
The default setting has been selected to provide good detection of strong electrical impulses without unnecessarily modifying normal wideband IQ.
As with any noise blanker, the optimum setting can depend on the type of interference and the signals being received.
Increasing sensitivity too far can cause legitimate signal transitions to be mistaken for interference, so the lowest possible threshold is not necessarily the best setting.
Maximum Width
The
Maximum Width
setting determines the longest detected event which the Wideband Noise Blanker is permitted to reconstruct.
Short impulses are ideal candidates for repair because clean IQ exists very close to both sides of the damaged region.
As the duration increases, predicting the missing wideband waveform becomes progressively less certain.
The maximum width therefore provides an important safeguard: events longer than the selected duration are detected but are not reconstructed.
A value around
200 microseconds
accommodates the strong electrical impulses encountered during development while remaining conservative about much longer disturbances.
What the Wideband Noise Blanker Cannot Do
The Wideband Noise Blanker should not be confused with general-purpose noise reduction. It cannot remove ordinary receiver noise, atmospheric background noise, adjacent-channel interference or continuous electrical interference.
It also cannot recover information which has been completely destroyed by a long-duration disturbance.
Its purpose is much more specific:
detect short, abnormal, broadband impulsive disturbances and replace the damaged IQ with a smoother estimate derived from the surrounding clean signal.
When the interference matches this description, processing the original wideband IQ has a major advantage over trying to remove the resulting clicks independently from every receiver.
Wideband and Receiver Noise Blanking
Wideband and receiver-level noise reduction solve related but different problems.
A receiver-level blanker operates after the SDR has selected and filtered a particular receiver bandwidth. It has much less bandwidth to analyse and may therefore be able to make use of characteristics specific to that receiver.
The Wideband Noise Blanker sees the complete SDR bandwidth instead. Its task is more difficult because the wideband IQ may contain many signals simultaneously, but successful repair benefits everything downstream.
The two approaches are therefore complementary rather than mutually exclusive.
Summary
The SDR Conso

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