pyAPRiL is a python based DSP library which implements passive radar algorithms. The ultimate goal of the library is to make available the so far ellaborated passive radar algorithms to everyone including sceintific researchers, radar system designers and amateurs.
All the implemented methods are tested and verified through real-life systems, field measurements and simulations.
Project guidelines:
This project respects the authors of all the contributions, therfore references are always highlighted where applicable.
Understanding the operation of the implemented algorithms is always of primary importance. The efficient execution and concise coding do not belong to the principles of the project, but encoured
when it is not at the expense of comprehensibility and does not change the essential operation of the originally proposed algorithm.
Contributions:
Contributions are welcome from anyone who shares the passion of passive radars and wants to help make opensource passive radar community even better.
Whether you're a seasoned developer or just getting started, there are plenty of ways to get involved and contribute to pyAPRiL. By contributing to the project,
you'll be helping to make a positive impact on passive radar developers around the globe and advancing the deeper sceintific understanding of this novel technology.
Whether you can contribute code, documentation, or simply provide feedback, every contribution helps us move closer to our shared goal.
Restrictions:
This projects strictly focuses on the support of scientific researches. The source code of the library is
licensed under the GNU General Public License version 3 (GPLv3), which is a copyleft license. This means that anyone who uses, modifies, or distributes the project
must also license their work under the same terms and conditions.
Commercial use of this software is permitted, provided that any results obtained from such use are made available to the public under the same license.
By adhering to these restrictions, this project gains comliance with the REGULATION (EU) 2021/821 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL for european contributors.
Apply preprocessing on the reference channel to purify from signal corruptions
Clutter filtering
Apply various clutter filtering techniques to mittigate the masking effect of the direct path interference and
other high power clutter components
Beam-space processing
Synthesize and apply beampatterns for reference and surveillance channels to implement spatial filtering and imporve the detection capabilities
Target recognition
Automatic target recognition is used to identify potential targets on the range-Doppler matrices with the application of adaptive algorithms, such as the CFAR.
Hit processor
Filter and clusterize hits to extract target plots from hit matrices or hit list
Target parameter estimation
Extract and estimate accurate target range, Doppler and DoA information
Tracker
Fit target kinematic modells to plots information to filter false positives and to estimate target states
Display
Display intermediate and final radar processing results.
Analysis
Analyse the performance of the used algoirthms and tune them to obtain optimal operation
Simulations
Generate synthetic data to test the operaiton of the experimental processing techniques
Target Localization
Calculate the location of the target using bi or multistatic mesurement
