Spectral-Domain Local Statistics with Missing-Data Support for Cartesian and Polar Grids
Abstract
This paper presents a method for computing local mean, variance, standard deviation, and effective sample count on incomplete gridded data using boundary-aware spectral operators. The framework combines normalized convolution with explicit boundary-condition modeling: reflective Discrete Cosine Transform (DCT) for non-periodic Cartesian axes and periodic Real Fast Fourier Transform (RFFT) for circular azimuth processing in polar geometry. Stability safeguards (denominator floor, prefill fallback, and variance clamp) are specified for under-supported regions. We evaluate the framework across three targeted scenarios: a Cartesian boundary-condition check demonstrating the mitigation of wrap-around artifacts, a synthetic 3D outlier-identification test, and a real-radar polar application. Results establish bounded, support-aware interpretation of local statistics while preserving a concise reproducibility path through the open-source 'dct\_toolkit' implementation.
Keywords
Cite
@article{arxiv.2604.22721,
title = {Spectral-Domain Local Statistics with Missing-Data Support for Cartesian and Polar Grids},
author = {Jairo M. Valdivia-Prado and William E. Chapman and Katja Friedrich},
journal= {arXiv preprint arXiv:2604.22721},
year = {2026}
}
Comments
Accompanies the open-source dct_toolkit package