Kalman Filter Application to GBSAR Interferometry for Slope Monitoring

The use of Kalman filtering techniques for landslide monitoring has proved effective as a tool estimating and predicting land displacements. Ground-Based Synthetic Aperture Radars (GBSAR) are popular remote sensing instruments able to provide displacement maps the investigated area, with submillimeter precision. These outperform other sensors in several respects, such all-weather all-day monitoring. However, some cases, instance vegetated scenarios, is affected by significant uncertainty due decorrelation radar signal. In case, retrieve any reliable information, noise must be filtered out using appropriate methods. Given success kinematic modeling movements through filtering, this technique seems optimal candidate processing measured interferometric GBSAR data. This paper investigates idea, applying Filter measurements acquired two different campaigns: north Spain, sliding glacier Alpes, Italy. A proper initialization algorithm parameters fundamental correct application filter. work, we present strategy that exploits information from coherent pixels tuning optimizing filter performance on areas low coherence.