harrison-caudill / pylink Goto Github PK

https://www.mathworks.com/matlabcentral/fileexchange/34512-parabolic-reflector-radiation-pattern

#FAIL

Not sure how it should be incorporated...maybe just a PR node, and some indication about whether or not the PR is violated for a given EPFD/victim?

Not even sure where to begin -- never done RADAR, personally. Guessing there will be some phase nodes, a big-ole attenuator for the reflector, a few nodes for things like cross-section, and different "path-loss" calculations.

Currently 0 is one-side, 90 is boresight, and 180 is the opposite side. Recenter things from -180 to 180 with 0 at boresight.

and real pointing error

examples/latex.py doesn't produce dvi files.

"Do you like DAGs?"
"Sure, I like DAGs"
https://youtu.be/dQSnua3M2lo?t=56

https://pint.readthedocs.io/en/0.10.1/tutorial.html

In the report, it's showing the receiver noise bandwidth as being the bitrate. That value should be the required demod bw unless otherwise specified.

Will need to read more in Sklar before this one can even be scoped. If anyone has recommendations, let me know.

Haven't been necessary so far since it has been used for frequencies below 10GHz, but it should be added at some point.

https://github.com/harrison-caudill/pylink/blob/master/pylink/tributaries/budget.py#L181

It's a last-minute calculation -- do something more intelligent.

It would be cool if this system could do an ODT or XLSX export -- might make things easier for people who don't use python.

Calculate peak PFD for victim satellites other than GSO

What's the point of living on a sailboat and being a radio nerd if you aren't going to combine the two eccentric hobbies?

Besides, including support for ionospheric reflection/attenuation seems like a fun learning experience.

• xwatermark
• probably others

As defined by 47 CFR Part 25 Section 103, include appropriate computations for EPFD for compliance reports.

https://www.law.cornell.edu/cfr/text/47/25.103

This isn’t a real issue

Are there more detailed examples? There is a lot of extra features, and I'd really only like the ability to perform the things mentioned in the bullet points in the README

Need to find a reference, and do the math on this one first. Initial thoughts:

• An EVM distribution can be modeled as AWGN at the transmitter...I think...
• We are then transmitting amplified noise
• The amplified noise can probably be reduced by FSPL just like the signal (a la friis cascade)
• The size of the decision-region of the modulation is modulation-dependent, but if it can be modeled as AWGN, that won't matter...I think...
• Not sure whether or not realistic EVM's have non-trivial impacts upon system performance so it may not be worth inclusion in the overall budget
• Not sure if EVM distributions are even provided by VSA's
• If the EVM distribution is not very gaussian, would it be appropriate to integrate the distribution for a target BER/decision-region based upon the selected modulation?

https://github.com/harrison-caudill/pylink/tree/kungfoo-eg

Add self.override(var, best_val) to line 342 of model.py

Otherwise, the best_diff value can start at a more-or-less random value instead of the actual diff for the first value in the sweep.

Consider decoupling from room temperature.

https://www.microwaves101.com/encyclopedias/noise-temperature