Proc. SPIE. 8003, MIPPR 2011: Automatic Target Recognition and Image Analysis
KEYWORDS: Filtering (signal processing), Electronic filtering, Navigation systems, Monte Carlo methods, Error analysis, Algorithm development, Reliability, Detection and tracking algorithms, Information technology, Data processing
A matching-unscented Kalman filtering for gravity aided navigation is presented in this paper. With this method
submerged position fixes for autonomous underwater vehicle can be obtained from comparing gravity fields'
measurements with gravity maps, meanwhile the drawback of traditional matching or filtering algorithms can be
avoided. A synthetic gravity map was taken for the simulation, and the results showed that navigation errors can be
reduced more efficiently and reliably by the presented method.
Using star tracker to perform space surveillance is a focal point of research in aerospace engineering. However, autonomous attitude determination with star trackers in missions is a challenging task, because of spacecraft attitude dynamics and false stars. We present a novel star pattern recognition algorithm to resolve these problems. The algorithm defines a star pattern, called a flower code, composed of angular distances and circular angles. Then, a three-step strategy is adopted to find the correspondence of the sensor pattern and the catalog pattern, including initial lookup table match, cyclic dynamic match, and validation. A number of experiments are carried out on simulated and real star images. The simulation results show that the proposed method provides improved performance, especially on robustness against false stars. Also, the results for real star images demonstrate the reliability of the method for ground-based measurements.
A new method of underwater passive navigation based on gravity gradient is proposed in this paper. In comparison with
some other geophysical characteristics such as gravity or gravity anomaly, gravity gradient which is the second
derivative of gravitational potential has better spatial resolution and more sensitive to terrain changes. Through it, the
digitally stored gravity gradient maps and real-time gravity gradient measurements can be taken as input information,
with gravity gradient linearization techniques and extended Kalman filter, the navigation errors of INS are estimated by
using gravity gradient error, therefore the output in the inertial navigation system are corrected. Simulation test has been
done and the results show that, the method is effective and efficient for the positioning precision improvement.