Software Tools: MATLAB
- M. Alaee-Kerahroodi, L. Wu, E. Raei and M. R. B. Shankar, “Joint Waveform and Receive Filter Design for Pulse Compression in Weather Radar Systems,” in IEEE Transactions on Radar Systems, vol. 1, pp. 212-229, 2023, doi: 10.1109/TRS.2023.3290846.
- L. Wu, M. Alaee-Kerahroodi and B. M. R. Shankar, “Improving Pulse-Compression Weather Radar via the Joint Design of Subpulses and Extended Mismatch Filter,” IGARSS 2022 – 2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 2022, pp. 469-472, doi: 10.1109/IGARSS46834.2022.9884519.
The meteorological industry is currently exploring Solid State Weather Radar (SSWR) systems, a new technology that emits minimal transmission power, to minimize harm to the environment and reduce system cost. However, accurately estimating reflectivity for each polarization and decreasing the blind range present significant challenges for SSWR systems that use pulse compression. In these systems, transmit waveforms and receive filters play a crucial role in enhancing estimation accuracy, while implementing partial correlation can reduce the blind range.
In the research highlighted above, we propose a novel joint design technique for transmit waveforms and receive filters in weather radar systems using the Alternating Direction Method of Multipliers (ADMM) and Coordinate Descent (CD) optimization approaches. We demonstrate the effectiveness of our technique by iteratively solving nonconvex design problems and showcasing the convergence of the objective function.
Real Data and the dataset (download here (2.14GB)!)
In this post, we evaluate the effectiveness of the enhanced waveform and subpulse-filter combinations by analyzing real-world weather data from ELDESradar. The data was collected during a period of heavy rainfall in Florence, Italy, on September 30, 2022.
The collected I and Q data are obtained from an uncoded pulse with a resolution 31.25m.
In order to replicate the effects of pulse compression on these observations, we convolve the optimized waveform (designed in the previous section) with the echoes captured by the radar when transmitting an uncoded pulse.
The acquired uncompressed samples are then convolved with the optimized filter coefficients. Therefore, we assess the effectiveness of the optimized waveform and subpluse-filter pair using real data from the aforementioned system. The results for reflectivity calculation is depicted in the figure below; (a) indicates reflectivity (dBZ) of real weather data as the benchmark that was recorded on September 30, 2022, in Florence, Italy, during a time of rain. The remaining images represent the impact of applying pulse compression to real data reflectivity (dBZ) using (b) CAN, (c) Gradient Descent, (d) ADMM , (e) CD (p=2), and (f) CD (p=6).
Thanks to ELDESradar, the dataset is now publicly accessible and can be obtained using the provided link. To simplify access to the raw data, an accompanying MATLAB script named “realData.m” has been included within the dataset. Running this script will enable you to recreate the reflectivity of the benchmark, as illustrated in the above figure.