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6

To convert a real signal sampled at rate $2B$ to its complex baseband representation (sampled at rate $B$), you want to map the frequency content in the range $[0, B)$ in the real signal to the range $[-\frac{B}{2}, \frac{B}{2})$ in the resulting complex signal. This can be done in a couple different ways: Design a linear filter to approximate a Hilbert ...


5

Wow, I'm honored by Matt L. doing what I'm often doing: Referring people to GNU Radio. The project actually has a list of recommended literature, but I don't know how well that'd fit you. It's probably still worth looking into. Then, regarding QPSK: Well, it's one of the basic constellations, and you'd probably be best off reading a textbook intro to ...


4

A quick scribble on paper shows that FMCW radar has a resolution of $$ \Delta d = \frac{c}{2b} $$ with $c$ being the speed of wave propagation (aka speed of light, essentially), $b$ being the bandwidth, and $2$ coming into play because the wave has to trave both ways. For your $\Delta d\overset != 1\mathrm m$, $$ 1\mathrm{m} = \frac{3 \cdot 10^8 \frac{\...


4

I also read this from a response to a USRP user's question about RSSI measurements: [The] Received Signal Strength [Indicator is] always relative to some signal model, incorporating considered bandwidth, assumptions on the modulation scheme, duration of transmission, generally: It's a estimation of received signal strength based on some property of ...


4

Sparsity concept is extensively being used in computer vision and image processing. The Idea is that natural image can be pretty sparse when it is transformed to different bases. this bases can be predefined, e.g. FFT,DCT or can be learned from the image, e.g. sparse coding. Here are a few well know example of algorithms that uses the sparsity assumption on ...


3

The AM or medium wave band extends from 526.5 kHz to 1606.5 kHz in Europe or 535 kHz to 1705 kHz in the US. Ultrasound (not supersound! :-) frequencies range from 20kHz and up: However, a bigger problem is that AM stations broadcast using electromagnetic radiation, whereas sound travels by acoustic waves in the air.


3

So, to give you something to read up on first, the channel you describe is a Rician or Rayleigh channel, depending on whether you have a dominant line-of-sight path or not. So, as a first approach, to delay something in time, you don't have to shift it by a whole sample – you can also do it in frequency domain, by DFT'ing your signal, multiplying it with a $...


3

Preface First of all: Arduino might really not be the tool of choice here; with a 1MS/s max, and some something-in-between-8-and-16-effective-bits ADC, there's hard limits on what you can detect. I will explain the theory of how to do better estimation than just sample-wise, but I won't address these limits here. Furtherore, I assume you do real-valued ...


3

If we take the complex baseband signal as $S(t) = I(t) + jQ(t)$ (which of course means that we have two separate wires on which the $I(t)$ and $Q(t)$ voltages appear), then we have a nice signal representation for the transmitted signal $$s(t) = I(t) \cos(2\pi f_c t) - Q(t) \sin(2\pi f_ct) = \operatorname{Re}\left(S(t)e^{j2\pi f_c t}\right).$$ If we ...


3

The idea is that you can transmit two signals, $I(t)$ and $Q(t)$, over the same bandwidth and at the same time, and still recover each independently of the other. The math is pretty simple. If the transmitted signal is $s(t)=I(t)\cos(2\pi f_ct)-Q(t)\sin(2\pi f_ct)$, then (ignorning noise, ignoring a factor of 1/2 and assuming coherent reception) the receiver ...


3

Your 2. is (as far as I know) the standard way to implement a FMCW radar. The major advantage of both the FMCW and the SFCW (which was mentioned in the comments), is the sample rate of the ADC is greatly reduced. (This is sometimes called down-conversion, or pulse-compression). After the mixer, you have a mixer sum and mixer difference, filtering out so ...


3

It's a simple trigonometric identity: $$\cos(\omega_mt)\cos(\omega_ct)=\frac12\left[\cos((\omega_m+\omega_c)t)+\cos((\omega_m-\omega_c))\right]\tag{1}$$ Multiplying two sinusoids of different frequencies results in the sum of two sinusoids with the sum and the difference of the two frequencies. So this type of AM (DSB-SC) really results in a suppressed ...


3

It is because the audio signals are real and already at baseband. In contrast radio frequency signals are often represented as complex numbers once they are brought back to baseband. Real signals can be represented as a single stream of real numbers, while for complex numbers two streams of real numbers are required to represent them (as in $I+jQ$). When ...


3

I don't believe there is much more to it than that, especially noting that you are aware not to track so fast that you track out amplitude information for modulated signals where the amplitude is used as part of the modulation (so more so for M-QAM than M-PSK). You can also design AGC's with different rise/fall times so can have "fast-attack" and "slow-decay"...


3

Does radio communication have to account for the doppler effect? Yes. Would be pretty terrible if that wasn't the case: RADAR wouldn't work! Do moving objects such as planes and rockets have to account for this, Yes. Phones in cars and trains, too. Your 5G NR phone of the future operating above 60 GHz, will have to do that at walking speeds, too. or is ...


2

If you know that it is a square wave, and the frequency is roughly constant, I would think about synthesizing an inverted copy of it and adding it to the signal. The remainder would be your speech, plus some residual harmonics of the square wave frequency, which might be dealt with by a 1-channel adaptive filter (see Sec. IIC of this link: http://www.cs.cmu....


2

Sparsity covers a wide range of concepts. It characterizes an object (a signal, a system, a function) for which their exists a representation (exact or approximate) whose dimension (number of parameters, degrees of freedom) is much lower than the inherent dimension of the object. For instance, let us first consider 1.000.000 points $(x_i,y_i)$, acquired to ...


2

A couple of example areas: Sonar beamforming - in many cases there are a small number of targets Radar processing - a radar image can be decomposed to a background and sparse set of point like targets, or a small number of moving targets. Radar tomography - This application uses multiple radar passes at slightly different elevations to extract elevation ...


2

It will work when you take the 2nd gradient of the signals: import numpy as np from scipy import signal s0 = np.gradient(np.gradient(s0)) s1 = np.gradient(np.gradient(s1)) np.argmax(signal.correlate(s0, s1)) -> 525358 That corresponds to a shift of 1071 which is close to your expected 1069 Interestingly the minimum (most negative correlation) is close ...


2

Periodic noise in an SDR spectrum is a common problem. One likely source is EMI or RFI from nearby electronics. The sources of EMI and RFI can be from computers, USB cables, networks cables and routers, power cords, wall plug or internal power supplies for various electronic devices, wireless chargers, LED lighting controls and voltage converters, and ...


2

What's labeled $(1)$ in your question is a special case, the flat channel. It can be represented as a single coefficient. In general, channels aren't flat, and we then need to apply $(2)$ instead. That's no different from acoustics. So, your claim that $(1)$ generally applies to SISO channels is plain wrong. However, when naming something "SISO", one ...


2

To complement @DanBoschen's answer: a real baseband signal is a purely in-phase signal. Its quadrature component is zero, so there is no need to sample it or represent it in any way. An interesting approach, though, would be to represent a stereo signal as quadrature. You could define the right channel as the in-phase signal, the left channel as quadrature, ...


2

Yes indeed it will be (although we could argue that it may not be necessarily for all distances since the phase is cyclical!). In free space the signal propagates at the speed of light, therefore this sets the wavelength in distance based on the frequency transmitted according to: $$\lambda = c/f$$ Where $c$ is the speed of light in meters/second (or ...


2

Yes and no, it can't be intermodded onto another SIGNAL, but it can be intermodded onto another CHANNEL. For example, if you are monitoring 144 MHz, an FM signal on 145 MHz and on 146 MHz CAN, due to non-linearity, end up being heard on 144 MHz. But it can't be modulated onto an existing signal on 144 MHz. Mark


2

But what does detector in the frequency domain? Something really complicated. We get trained to think about signal processing almost exclusively in the frequency domain, and we forget that the frequency domain stuff was only invented as a mathematical trick to make it easier to understand what's going on in the time domain. If it's easier to do in the ...


1

this is John BG 1.- GFSK is not same as single tone According to the CC1101 specs you refer to, the C1101 uses GFSK. GFSK varies the varrier frequency, therefore the spectrum cannot be a delta. Call it a 'shifting delta' but it cannot be same spectrum as just a tone. 2.- you want the carrier frequency to vary ISM are crowded bands, the carrier has ...


1

If the above is a good representation you should just try to infer when there is energy in the signal to align them. As it seems they start with nothing (Zero value). Then all needed is just to find where "Something" happens. This could be easily done with high resolution (Few samples). Regarding Cross Correlation, try to normalize both signal to have the ...


1

As far as the big picture goes, I assume you want to do both optimal detection of pulses and then tracking of the transmitters based on the detections you receive. This answer will only deal with taking steps to optimize detection using a low cost RTL-SDR, and then I'll address some of your specific questions in your post. In AWGN, the optimal detection ...


1

Have a model for the interference, if you can hopefully get the idea of amplitudes of the interference (you could get the idea using the following): Since you mention that interference occurs at random times, get a spectrogram running on the received signal and watch for abrupt transitions (would occur when ur random interference occurs), then from the FFT ...


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