This paper introduces a fast implementation of the power iteration method for subspace tracking, based on an approximation that is less restrictive than the well-known projection approximation. This algorithm, referred to as the fast approximated power iteration (API) method, guarantees the orthonormality of the subspace weighting matrix at each iteration. Moreover, it outperforms many subspace trackers related to the power iteration method, such as PAST, NIC, NP3, and OPAST, while having the same computational complexity. The API method is designed for both exponential windows and sliding windows. Our numerical simulations show that sliding windows offer a faster tracking response to abrupt signal variations.
We propose a joint optical flow and principal component analysis (PCA) method for motion detection. PCA is used to analyze optical flows so that major optical flows corresponding to moving objects in a local window can be better extracted. This joint approach can efficiently detect moving objects and more successfully suppress small turbulence. It is particularly useful for motion detection from outdoor videos with low quality. It can also effectively delineate moving objects in both static and dynamic background. Experimental results demonstrate that this approach outperforms other existing methods by extracting the moving objects more completely with lower false alarms.
JSatTrak is a Satellite tracking program written in Java. It allows you to predict the position of any satellite in real time or in the past or future. It uses advanced SGP4/SDP4 algorithms developed by NASA/NORAD or customizable high precision solvers to propagate satellite orbits. The program also allows for easy updating of current satellite tracking data via CelesTrak.com. Because this application was written in Java, it should run on almost any operating system or directly off the web using java web start!