In target tracking, the tracking process needs to constantly update the data information. However, during data acquisition and transmission of sensors, outliers may occur frequently, and the model is disturbed by non-Gaussian noise, that affects the performance of system state estimation. In this paper, a new filtering algorithm is proposed based on QR decomposition and singular value decomposition (SVD) method, namely adaptive robust unscented Kalman filter (QS-ARUKF) to suppress the interference of outliers, non-Gaussian noise as well as a model error to achieve high accuracy state estimation. An adaptive filtering algorithm based on strong tracking idea is used in modifying the state equation of unscented Kalman filter (UKF), so that the algorithm can effectively improve the tracking ability of the state model. By using the robust filtering method to construct a new cost function used to modify the measurement covariance formula of the Kalman filter, the error of measurement model can be effectively suppressed. The QR decomposition is introduced to the time update and measurement update to avoid the covariance non-positive definite. We propose the SVD method to address the problem of numerical sensitivity in the filtering process. The purpose of this method is to replace the calculation of the inverse of the filter gain matrix and further improve the robustness of the algorithm. The simulation results showed that the proposed algorithm has higher accuracy and better robustness than the traditional filtering method.

In target tracking, the tracking process needs to constantly update the data information. For maneuvering target, model mismatch and loss of high-order moment information disrupt the accuracy of the state estimation. In this paper, an adaptive high-order unscented Kalman filter (AHUKF) algorithm is proposed for the case of errors occurring in the capturing of model dynamic behavior using the classical unscented Kalman filter (UKF) algorithm. By introducing the free parameter, the analytical solution of the high-order unscented transformation (UT) was obtained, the basis for choosing free parameters was analyzed, and the stability of the algorithm was discussed. A method for obtaining the optimal adaptive factor based on the prediction residual estimation covariance matrix was proposed, which reduces the influence of the dynamic model error and was applied to the target tracking model. In this paper, the proposed AHUKF is applied to a target tracking problem with state mutation, different sampling intervals, and different turn rates, respectively. Simulation results for target tracking illustrate that the proposed algorithm is more accurate and robust than the UKF, high-order unscented Kalman filter (HUKF) and adaptive unscented Kalman filter (AUKF).

The utilization of deep learning algorithms for side-scan sonar target detection is impeded by the restricted quantity and representativeness of side-scan sonar (SSS) samples. To address this issue, this paper proposes a method for image augmentation using a CC-WGAN network. First, the generator incorporates the Convolutional Block Attention Module (CBAM) to enhance the assimilation of global information and local features in the input images. This integration also improves stability and avoids mode collapse problems associated with the original Generative Adversarial Network. Subsequently, the CBAM is incorporated into the discriminator to facilitate a better understanding of the relevance and significance of input data, thereby enhancing the model’s generalization ability. Finally, based on this model, existing few-sample SSS images are augmented, and we utilize the augmented images for discrimination and detection with YOLOv5. The experimental results show that following training with the SSS dataset that is augmented by this network, the accuracy of target detection increased by 7.6%, validating the feasibility of our proposed method. This method presents a novel solution to the problem of low model accuracy in underwater target detection with side-scan sonar due to limited samples.

The study of unmanned ground vehicles (UGVs) operating under unstructured roads is of great significance to intelligent transportation, agricultural development and military technology. In order to ensure reliable and stable operation of UGVs on unstructured terrain, it is necessary to identify the current road terrain and perform vehicle stability adjustment. Road terrain identification is a prerequisite for stability control. Most of the existing road terrain identification methods use a single vehicle sensor, which has the problem that complex algorithms need to be applied for data processing, which reduces the real-time performance. Moreover, the single sensor is weak in anti-interference and limited in recognizing the road. To address these problems, a method is proposed to collect vehicle motion data using on-board gyroscope sensors and velocity sensors. Back propagation (BP) neural network is used to identify the category of the road. For the problem that the conventional proportional-integral differential (PID) algorithm cannot be adapted to different road stability control, a multi-loop adaptive proportional-integral differential (PID) control system with the velocity loop as the outer loop and the torque (current) loop as the inner loop is proposed. In order to verify the feasibility and effectiveness of the method, experiments are conducted on a UGV using robot operating system (ROS), and the results verify the feasibility and superiority of the road identification and stability control method proposed in this paper. It provides a good theoretical basis and valuable technical guidance for the UGV operation and control on unstructured roads.

During the launch of the railgun, the interface of the armature/rail (A/R) will undergo severe melting and wear. The molten armature material will further solidify and deposit on the surface of the rail to form an aluminum deposition layer, which will change the original contact state and affect the subsequent armature launch performance. The evolution of the deposition layer under flat rails and D-shaped rails was experimentally studied. The study found that the deposition layer presents a changing trend of thickness on both sides and thin in the middle. With the increases in the number of experiments, the thickness of the deposition layer gradually accumulates, however, in the end, the thickness of the deposition layer tends to stabilize. Experiments have found that the horizontal distribution of the D-shaped rail deposition layer is more uniform than that of the flat rail, and the increase rate of the thickness of the deposition layer is lower than that of the flat rail. Based on the analysis of the A/R contact resistance and current density distribution, we have given an explanation for the difference in the thickness distribution of the deposition layer. In addition, the thickness of the deposition layer tends to be stable under the two rail conditions because the bond strength of the deposition layer and the rail decreases after repeated launch, and the tensile stress increases, which leads to the peeling off of the deposition layer.

In this paper, a reweighted sparse representation algorithm based on noncircular sources is proposed, and the problem of the direction of arrival (DOA) estimation for multiple-input multiple-output (MIMO) radar with mutual coupling is addressed. Making full use of the special structure of banded symmetric Toeplitz mutual coupling matrices (MCM), the proposed algorithm firstly eliminates the effect of mutual coupling by linear transformation. Then, a reduced dimensional transformation is exploited to reduce the computational complexity of the proposed algorithm. Furthermore, by utilizing the noncircular feature of signals, the new extended received data matrix is formulated to enlarge the array aperture. Finally, based on the new received data, a reweighted matrix is constructed, and the proposed method further designs the joint reweighted sparse representation scheme to achieve the DOA estimation by solving the $l_1$ -norm constraint minimization problem. The proposed method enlarges the array aperture due to the application of signal noncircularity, and in the presence of mutual coupling, the proposed algorithm provides higher resolution and better angle estimation performance than ESPRIT-like, $l_1$ -SVD and $l_1$ -SRDML (sparse representation deterministic maximum likelihood) algorithms. Numerical experiment results verify the effectiveness and advantages of the proposed method.