# Tag Info

7

There is a range of possibilities, depending on what kind of signal you want to find. I'll start from easy and move up to hard. I'm assuming that you are using an FFT to get your spectra. RFI. An earlier poster referenced some papers on finding RFI. I don't know precisely how that is defined, but lets assume that it is unintentional RF from things like ...

6

Complexity of your question It's hard to make general statements here, because computational complexity is a property of the implementation of a receiver, which usually is a choice given the properties of the transmission. For example, assume you have 2-FSK. You can either demodulate that by having say, 2 bandpass filters applied to a passband signal, and ...

5

From https://link.springer.com/chapter/10.1007/978-3-322-92773-6_9 : Spectral subtraction is a method for restoration of the power or the magnitude spectrum of a signal observed in additive noise, through subtraction of an estimate of the average noise spectrum from the noisy signal spectrum. The noise spectrum is estimated, and updated, from the periods ...

4

Completely independent. It is dependent on frequency only. The amplitude of the signal is only relevant when looking at the number of bits used (and the dynamic range). See here for more details.

4

What does this noise reduction do? That should depend on the device. Generally, it's to be assumed that it applies (analog and/or digital) signal processing to improve the perceived Signal-to-Noise ratio. In simple cases, this might simply mean reducing the bandwidth of an analog receiver. Sure, voice will not sound as crisp, but if that buys one a ...

3

Well, this is a well-studied field (Radio Frequency Interference detection and mitigation). There are tons of literature about it. The noise you receive is theoretically Additive White Gaussian Noise (AWGN). That means that its Probability Density Function (PDF) is Gaussian, and the pdf of its samples' power is exponential. By setting a false alarm ...

3

As hotpaw2 said, the thing is that while it looks like random noise, the actual UWB signal is correlated, in some specific way known to the receiver. I'll explain this using an example (keeping it short, I'm at GRCon): Imagine your UWB transmitter using a scheme where it generates a wideband noise signal. The way it encodes a "0" bit is that it turns on ...

3

Even if all the transmitted UWB pulses are identical, and initially start at an unknown time, the pattern variations in the delta time between pulses could be used to code information. As with any spread (spectrum or otherwise) system, a longer sequence with more coding might allow more (orthogonality coded?) signals to occupy a channel at some given ...

3

I've heard that 2 days in the lab can save 2 hours of reading but in this case it turned out to be easy to test. It is the sample rate of the SDR which changes the bandwidth of the signal. Since I am using the PLUTO SDR I can't go below around 500ksps. So I tried with a symbol rate of 300 kbaud at a sample rate of 600k. Able to demod successfully. I asked ...

2

The data is AM modulated on the 2.4 kHz subcarrier, with 256 different levels representing a single value from 0 to 255. It's a scanline every 1/2 second from the cameras with sync and telemetry data added to the beginning and end. Each line is 2080 data points (words) long, so it broadcasts at 4160 baud. The sync lines at the beginning let you know when a ...

2

(This answer is from the perspective of GNU Radio programming; I'm not familiar with what SatNOGS is doing.) There are two problems to solve to use a given data file. First, you need to decode the file format into samples of a signal. The OGG File Source presumably solves this problem for you. Converting the file to a raw format is also an option, but not ...

2

Theoretically, the rate is independant. In reality, exceeding certain power levels will cause real world digitizers to increase in non-linearities (clipping, saturation, thermal damage, and etc.), which will introduce harmonics and other spectra above any Nyquist rate based on linear system assumptions, if not complete system failure.

2

Quantization noise is highly dependent on the signal source distribution and its amplitude, the number of ADC bits, and the use of dithering. For a high resolution ADC that is digitizing a full amplitude sine wave, the maximum noise contribution is: $$\text{SNQR}_\text{dB}=1.76+Q \cdot 6.02 \tag 1$$ where $Q$ is the number of bits. Here we can observe ...

2

The answer can be fond in The ARRL Handbook 2019, Vol 3, although it's spreaded across different chapters. In short, SNR is typically calculated for the noise floor of 2500 Hz SSB signal. Particularly this is how WSJT-X calculates negative SNRs for FT8 and other modes. Now the trick is that by deviding the bandwidth in half you decrease the noise floor by 3 ...

1

If your signal was at 500 MHz, and you successfully captured it with a 6 Msps sample rate, the signal is no longer "at" 500 MHz. Indeed it was transmitted there, but either: your receiver has an analog mixer, and mixed this signal down to baseband such that it could be sampled at 6 Msps, or the digital signal was originally sampled at a much higher rate, ...

1

I'm quite confused by your question. I suspect you are talking about a satellite which is using the FX.25 protocol (on Wikipedia), which is an extension of the original AX.25 protocol. FX.25 is a compatible extension, as such the AX.25 packet is encapsulated inside the extra information of FX.25. The AX.25 is still decodable by a normal TNC, so it's format ...

1

You might try taking a image capture of the spectrum waterfall for some duration, and feeding that image to a machine learning inference engine, perhaps a DNN. The inference engine could be trained on a large image database with lots of waterfalls of lots of known or suspected signal types, similar to these signal ID databases: https://www.sigidwiki.com/...

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