Autonomous detection and tracing of ion trails in the Martian ionosphere by exploiting spectral morphology and spatial geometry

Abstract:

Autonomous detection, tracking, and disambiguation of local ion trails in the Martian ionosphere against solar wind has been a difficult computational challenge. This is primarily due to highly non-linear spectral morphology of the ion trails as well as the potentially multi-dimensional overlap between the trails themselves and against the trail signature of the solar wind. We present a computational application of geometric feature extraction that autonomously detects and tracks charged particle trails in the Martian ionosphere using solar wind ion analyzer (SWIA) data from NASA’s MAVEN mission. Our technique involves algorithmic analysis of energy spectrograms to extract ion trails that exhibit high signal-to-noise ratio (SNR) levels as well as topologically connected spectral morphology that allow detection using popular morphological signal processing techniques. Specifically, we utilize various signal processing methods to unite connected particles and isolate individual trails from surroundings. We provide results of our algorithm’s extraction process over energy spectrograms as well as spatial angle maps that allow visualization of the evolution of individual ion trails.