This paper is concerned with nonlinear filtering under unknown dynamics and high-complexity observations. We propose a convex optimization-based filtering algorithm, and show that the algorithm yields bounded error if the disturbances are small and bounded, and if the observations are redundant. An experimental result is presented to demonstrate that the algorithm is capable of accurate real-time estimation of patterned wafer parameters in a plasma etching process with optical observation.