A Propagator Method for Bistatic Coprime EMVS-MIMO Radar

In this paper, a novel two-dimensional (2D) direction-of-departure (DOD) and 2D direction-of-arrival (DOA) estimate algorithm is proposed for bistatic multiple-input multiple-output (MIMO) radar system equipped with coprime electromagnetic vector sensors (EMVS) arrays. Firstly, we construct the propagator to obtain the signal subspace. Then, the ambiguous angles are estimated by using rotation invariant technique. Based on the characteristic of coprime array, the unambiguous angles estimation is achieved. Finally, all azimuth angles estimation is followed via vector cross product. Compared to the existing uniform linear array, coprime MIMO radar is occupying large array aperture, and the proposed algorithm does not need to obtain signal subspace by eigendecomposition. In contrast to the state-of-the-art algorithms, the proposed algorithm shows better estimation performance and simpler computation performance. The proposed algorithm’s effectiveness is proved by simulation results.

A Global Interconnected Observer for Attitude and Gyro Bias Estimation with Vector Measurements

This paper proposes a novel interconnected observer to get good estimates of attitude and gyro bias from high-noise vector measurements. The observer is derived based on the theory of nonlinear and linear cascade systems, and its error dynamics have the properties of global exponential stability and robustness to bounded noise. These properties ensure the convergence and boundedness of the attitude and gyro bias estimation errors. To obtain higher estimation accuracy, an approach to calculate time-varying gains for the proposed auxiliary observer is designed under the premise of considering noise terms in the rate gyro and vector sensors. The simulation results show that when the vector sensors’ outputs contain high-level noise, the proposed observer with time-varying gains yields better performance in both the transient and steady-state phases.