In the presence of steering vector (SV) mismatch, a novel iterative variable loading robust adaptive beamforming approach is presented in a new perspective. The proposed method searches for the desired array SV iteratively based on the relationship between the optimal weight vector and the assumed SV of standard Capon beamformer, which avoids any Lagrange multiplier methodology or the convex optimisation methods in each iteration. Simulation results demonstrate the superior performance of the proposed method.
The accuracy of gravity disturbance horizontal components is an obstacle in strapdown airborne vector gravimetry at present. In the strapdown inertial navigation system (SINS) field, the rotation modulation technique has been put into practice to improve the accuracy of navigation for decades, which could suppress the divergence of SINS errors. Since there is a close connection between SINS and strapdown vector gravimetry, we were inspired to research on introducing rotation modulation into strapdown airborne vector gravimetry in this study. In this paper, the second section introduced the connections and differences between SINS and vector gravimetry. Then, the third section analyzed three main obstacles in improving the horizontal components accuracy of strapdown airborne vector gravimetry. To solve these problems, the fourth section proposed the mechanism of rotation modulation vector gravimetry from system level which is inspired by rotation modulation inertial navigation system (RMINS). On the basis of the mechanism, the fifth section designed a yaw continous rotation modulation scheme for vector gravimetry. Simulation and real-world static experiment demonstrated that the designed scheme has better performance in horizontal components than the classical strapdown gravimetry. Moreover, the experiment with drift showed that rotation modulation could also suppress the effects of the accelerometer long-term drift on gravimetry results. In conclusion, the paper introduced the rotation modulation into strapdown airborne vector gravimetry for the first time. It will probably provide a reference for developing the next generation strapdown airborne vector gravimeter.
To reduce costs, an unmanned swarm usually consists of nodes with high-accuracy navigation sensors (HAN) and nodes with low-accuracy navigation sensors (LAN). Transmitting and fusing the navigation information obtained by HANs enables LANs to improve their positioning accuracy, which in general is called cooperative navigation (CN). In this method, the accuracy of relative observation between platforms in the swarm have dramatic effects on the positioning results. In the popular research, constructing constraints in three-dimensional (3D) frame could only optimize the position and velocity of LANs but neglected the attitude estimation so LANs cannot maintain a high attitude accuracy when utilizing navigation information obtained by sensors installed during maneuvers over long periods. Considering the performance of the inertial measurement unit (IMU) and other common sensors, this paper advances a new method to estimate the attitude of LANs in a swarm. Because the small unmanned nodes are strictly limited by relevant practical engineering problems such as size, weight and power, the method proposed could compensate for the attitude error caused by strapdown gyroscopic drift, which only use visual vectors built by the targets detected by cameras with the function of range finding. In our method, the coordinates of targets are mainly given by the You Only Look Once (YOLO) algorithm, then the visual vectors are built by connecting the targets in the covisibility graph of the nodes in the swarm. The attitude transformation matrices between each camera frame are calculated using the multivector attitude determination algorithm. Finally, we design an information filter (IF) to determine the attitude of LANs based on the observation of HANs. Considering the problem of positioning reference, the field test was conducted in the open air and we chose to use two-wheeled robots and one UAV to carry out the experiment. The results show that the relative attitude error between nodes is less than 4 degrees using the visual vector. After filtering, the attitude divergence of LANs’ installed low precision IMU can be effectively constrained, and the high-precision attitude estimation in an unmanned CN swarm can be realized.
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