Abstract
An efficient position and attitude estimation method is proposed based on the compressive sensing and extended Kalman filter. A set of ground-based beacons is used as references, and the beacon signals are received by the compact uniform linear arrays equipped on the object. Compressive subspace predetection and compressive estimation approaches are then developed to distinguish and calculate the direction-of-arrival vectors from different beacons. The compressive estimation method reduces the signal sampling rate and the computational complexity of the algorithm. Subsequently, the extended Kalman filter is applied to estimate the position and attitude parameters based on the direction-of-arrival vectors and the translational and rotational kinematics of the object. Finally, the Cramer–Rao lower bounds of the estimation variances are derived. Simulations show that the proposed method can provide a full position and attitude solution, while reducing the sampling rates and computational complexity compared to the traditional uncompressed approach.
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