tdoa | Time Difference of Arrival ) is estimated using GCC

 by   xiongyihui Python Version: Current License: Apache-2.0

kandi X-RAY | tdoa Summary

kandi X-RAY | tdoa Summary

tdoa is a Python library. tdoa has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However tdoa build file is not available. You can download it from GitHub.

TDOA (Time Difference of Arrival) is estimated using GCC-PHAT. A realtime DOA (Direction Of Arrival) is also elstimated using 2 microphones.

            kandi-support Support

              tdoa has a low active ecosystem.
              It has 83 star(s) with 39 fork(s). There are 6 watchers for this library.
              It had no major release in the last 6 months.
              There are 5 open issues and 2 have been closed. On average issues are closed in 518 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of tdoa is current.

            kandi-Quality Quality

              tdoa has 0 bugs and 0 code smells.

            kandi-Security Security

              tdoa has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              tdoa code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              tdoa is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              tdoa releases are not available. You will need to build from source code and install.
              tdoa has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions are not available. Examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed tdoa and discovered the below as its top functions. This is intended to give you an instant insight into tdoa implemented functionality, and help decide if they suit your requirements.
            • Start the stream
            • Generalized Cross Correlation Phase Transform
            • Close the queue
            • Overrides paint event
            • Draws the bar chart
            • Return the color of a given color
            • Generalized Cross Correlation Phase Transform
            • Read chunks from the queue
            Get all kandi verified functions for this library.

            tdoa Key Features

            No Key Features are available at this moment for tdoa.

            tdoa Examples and Code Snippets

            No Code Snippets are available at this moment for tdoa.

            Community Discussions


            How to localize a signal given the location of three receivers and the times at which when they receive the signal (Time Delay of Arrival)?
            Asked 2020-Oct-13 at 19:22

            I have 3 receivers (A, B and C), and some signal producing source (let's say sound or light) with an unknown location. Given the locations of A,B and C, and the time at which each receiver "heard" the signal, I'd like to determine the direction of the source.

            I understand there are ways to do so with TDoA multilateration/trilateration, however I'm having trouble implementing the calculation. There isn't a lot of clear, detailed information on this out there for those entirely new to the subject. What is out there is vague, more theoretical, or a bit too esoteric for me.

            Some similar posts on SO (but not quite what I'm after): TDOA multilateration to locate a sound source Trilateration of a signal using Time Difference(TDOA)

            This is also interesting, but assumes we have some boundaries: Multiliteration implementation with inaccurate distance data

            @Dave also commented an excellent and fairly accessible resource, but it falls short of going into enough depth that one might be able to actually implement this in code (at least, for someone without deep knowledge of regression, finding the intersection of the resulting hyperbolas, etc).

            [EDIT]: I should add that I can assume the 3 sensors and the source are on the surface of the Earth, and the effects of the curvature of the Earth are negligible (i.e. we can work in 2-dimensions).



            Answered 2020-Sep-09 at 15:25

            The simplest (but not fastest) approach would be to solve the equations with gradient descent.

            I'm assuming that we know

            • the positions of the receivers, A, B, and C, which do not lie on the same line;
            • the pseudorange of the unknown source X to each of A, B, and C.

            Intuitively, we simulate a physical system with three ideal springs configured like so, where the equilibrium length of each spring is the corresponding pseudorange.



            TDOA calculation, scipy minimize overshoot sometime
            Asked 2020-Oct-08 at 18:26

            I am trying to use TDOA to find the sound source location in a coordinate system.

            We have a costfunction looks like this:



            Answered 2020-Oct-08 at 18:26

            BFGS will not guarantee anything but a local minimum. If you know that your sensors will always be between -50 and 50 and -2 and 2, then I would suggest:

            • As a first step, try a bounded local minimization - using L-BFGS-B, SLSQP, etc... and give your variables those bounds
            • If the above is not satisfactory enough, bring out the big artillery and try global optimization algorithms, such as SHGO, DifferentialEvolution, DualAnnealing. They are all available in SciPy, might be slower but you’ll be much more confident in your results.



            "Probability Based" Time Delay of Arrival Algorithm Producing Weird Results?
            Asked 2020-Sep-11 at 21:14

            I've written the following algorithm designed to solve the Time Delay of Arrival problem through a "brute force" method. The problem is as follows: given the known locations of three receivers in a plane, and the propagating speed of some signal, determine the location of the signal source knowing only the times at which each receiver "saw" the signal.

            The algorithm works by assuming the source to be within a 1000 x 1000 kilometre square area, and then iterating (with 1 km "resolution") over every possible location, calculating the time to travel to each receiver and determining which location matches most closely with the known delay of arrival between each receiver (so, for each location [x,y], I calculate the time to arrive to receivers 1, 2, and 3, then determine how close time to arrive at 1 - time to arrive at 2 is to the live data, and similarly for the combinations 1 - 3 and 2 - 3 (ignoring other possible combinations, for simplicity).

            Here's the problem: it's highly unlikely each signal event is coming from the same direction. However, my code seems to suggest every event is at [0,0]. While it is technically possible that this is the case, it is far more likely that there is something wrong with my code, so for the purposes of this question let's assume that to be the case. Perhaps I've made some obvious mistake here?



            Answered 2020-Sep-11 at 21:14

            alphaEst, betaEst and gammaEst are assigned the same values.


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install tdoa

            You can download it from GitHub.
            You can use tdoa like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.


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