Subspace Based Adaptive Beamforming Algorithm with Interference Plus Noise Covariance Matrix Reconstruction

As we all know, the model mismatch, primarily when the desired signal exists in the training data, or when the sample data is used for training, will seriously affect algorithm performance. This paper combines the subspace algorithm based on direction of arrival (DOA) estimation with the adaptive beamforming. It proposes a reconstruction algorithm based on the interference plus noise covariance matrix (INCM). Firstly, the eigenvector of the desired signal is obtained according to the eigenvalue decomposition of the subspace algorithm, and the eigenvector is used as the estimated value of the desired signal steering vector (SV). Then the INCM is reconstructed according to the estimated parameters to remove the adverse effect of the desired signal component on the beamformer. Finally, the estimated desired signal SV and the reconstructed INCM are used to calculate the weight. Compared with the previous work, the proposed algorithm not only improves the performance of the adaptive beamformer but also dramatically reduces the complexity. Simulation experiment results show the effectiveness and robustness of the proposed beamforming algorithm.

Sunflower Optimization Algorithm Based Filtering Method for State of Charge Estimation of Batteries in Electric Vehicle

The main focus of the battery management system is the estimation of the battery’s State of Charge (SOC) which is an indicator to determine the driving range of an electric vehicle. The Extended Kalman Filter (EKF) algorithm is the most promising for SOC estimation when the system is running. The EKF state estimation algorithm is sensitive to the process noise covariance matrix Q and measurement noise covariance matrix R. Inappropriate noise covariance matrices reduce the accuracy and make divergence in state estimation. In this paper, the Sunflower Optimization algorithm (SFO) is used to optimize the noise covariance matrices before applying EKF for online SOC estimation. This simply indicates that the iterative SFO does not affect the instantaneous response of EKF in online estimation because the SFO is only performed once to determine the optimal values. The effectiveness of the proposed identification is examined through the constant discharge rate test and dynamic stress test. As observed, the performance indices such as maximum error, Mean Absolute Error, Mean Square Error and Root Mean Square Error of both SOC and voltage obtained by the proposed SFO-EKF are low compared to the other three methods. Besides accuracy, the proposed method quickly converges even when the initial SOC is inaccurate. The simulation results show that the proposed method has high accuracy and a better convergence rate in terms of estimating SOC under static and dynamic operating conditions.

Modified dual extended Kalman filters for SOC estimation and online parameter identification of lithium-ion battery via modified gray wolf optimizer

In order to achieve accurate state of charge (SOC) estimation of Lithium-Ion Battery, A method that dual Extended Kalman filters (DEKF) optimized by PSO-based Gray Wolf optimizer (MGWO) is proposed. A second-order equivalent circuit model with two resistor-capacitor branches is applied. The battery parameters are determined by battery test. Dual Extended Kalman filters are divided into state filter and parameter filter. Parameter filter is applied to adjust battery parameters online, state filter is applied to SOC estimation. Meanwhile, MGWO is applied to optimize the noise covariance matrix to improve the state estimation accuracy of SOC which reduces the linearization error from EKF. The results shows that the accuracy of algorithm is improved by adding online parameter identification and the optimization of the noise covariance matrix, meanwhile, the proposed method can adapt to the initial error well.

UAV Swarm Navigation Using Dynamic Adaptive Kalman Filter and Network Navigation

Aiming to improve the positioning accuracy of an unmanned aerial vehicle (UAV) swarm under different scenarios, a two-case navigation scheme is proposed and simulated. First, when the Global Navigation Satellite System (GNSS) is available, the inertial navigation system (INS)/GNSS-integrated system based on the Kalman Filter (KF) plays a key role for each UAV in accurate navigation. Considering that Kalman filter’s process noise covariance matrix Q and observation noise covariance matrix R affect the navigation accuracy, this paper proposes a dynamic adaptive Kalman filter (DAKF) which introduces ensemble empirical mode decomposition (EEMD) to determine R and adjust Q adaptively, avoiding the degradation and divergence caused by an unknown or inaccurate noise model. Second, a network navigation algorithm (NNA) is employed when GNSS outages happen and the INS/GNSS-integrated system is not available. Distance information among all UAVs in the swarm is adopted to compensate the INS position errors. Finally, simulations are conducted to validate the effectiveness of the proposed method, results showing that DAKF improves the positioning accuracy of a single UAV by 30–50%, and NNA increases the positioning accuracy of a swarm by 93%.

Performance and Evaluation of GNSS Receiver Vector Tracking Loop Based on Adaptive Cascade Filter

In the scenario of high dynamics and low C/N0, the discriminator output of a GNSS tracking loop is noisy and nonlinear. The traditional method uses a fixed-gain loop filter for error estimation, which is prone to lose lock and causes inaccurate navigation and positioning. This paper proposes a cascaded adaptive vector tracking method based on the KF+EKF architecture through the GNSS Software defined receiver in the signal tracking module and the navigation solution module. The linear relationships between the pseudo-range error and the code phase error, the pseudo-range rate error and the carrier frequency error are obtained as the measurement, and the navigation filter estimation is performed. The signal C/N0 ratio and innovation sequence are used to adjust the measurement noise covariance matrix and the process noise covariance matrix, respectively. Then, the estimated error value is used to correct the navigation parameters and fed back to the local code/carrier NCO. The field vehicle test results show that, in the case of sufficient satellite signals, the positioning error of the proposed method has a slight advantage compared with the traditional method. When there is signal occlusion or interference, the traditional method cannot achieve accurate positioning. However, the proposed method can maintain the same accuracy for the positioning results.