Joint Self-Position and Yaw Angle Tracking Based on Signal Steering Vector Expansion

Self-position determination with array sensing multiple emitter signals is a feasible alternative when the signal of the global navigation satellite system (GNSS) becomes weak due to obstruction or interference. This paper proposes a joint self-position and yaw angle tracking based on signal steering vector expansion. To be more specific, the angles are updated with the orthogonal property of the noise subspace and the first-order Taylor expansion. In order to refine the yaw angle, the estimator with local search is proposed and the estimated error is suppressed by minimizing the derivative of the error variance. After the angle estimation, the steering vectors are reconstructed and the position iterative formula is derived from its first-order Taylor expansion. Both simulated and practical experiments indicate that the proposed algorithm has lower complexity and better tracking performance than multiple signal classification (MUSIC) with weighted moving average (WMA), signal subspace fitting (SSF) with WMA and weighted signal subspace fitting (WSSF) with WMA.