To solve the problem in which the conventional ARMA modeling methods for gyro random noise require a large number of samples and converge slowly, an ARMA modeling method using a robust Kalman filtering is developed. The ARMA model parameters are employed as state arguments. Unknown time-varying estimators of observation noise are used to achieve the estimated mean and variance of the observation noise. Using the robust Kalman filtering, the ARMA model parameters are estimated accurately. The developed ARMA modeling method has the advantages of a rapid convergence and high accuracy. Thus, the required sample size is reduced. It can be applied to modeling applications for gyro random noise in which a fast and accurate ARMA modeling method is required.

In order to solve the problem of low face recognition accuracy of traditional algorithms and excessive long training time in deep learning methods, a novel lightweight and short training time multiface recognition method is proposed in this paper. Firstly, an extraction model of facial feature vectors is established based on local binary mode and principal component analysis. Secondly, the beetle antennae search algorithm (BAS) is optimized using adaptive factors, and the ABAS algorithm is proposed. This paper uses the ABAS algorithm to optimize the initial threshold of the neural network and proposes the ABASNet method. Thirdly, ABASNet method is combined with the face features extracted by the face feature vector extraction model to be used for multi-face classification tasks. Finally, H-softmax (hierarchical softmax) is used to replace softmax in neural networks. By reducing the amount of calculation in the process of multi-category face classification, the training time of the ABASNet network is reduced. Through ablation experiments and comparative experiments with various methods such as IKDA + PNN algorithm and PCANet algorithm, to verify the accuracy, robustness and short training time of the ABASNet method. Among them, the maximum accuracy of the method in the ORL face dataset, ExtYaleB face dataset and FERET face dataset is 99.35%, 99.54% and 99.18% respectively. In addition, actual test results indicate that the training time and recognition time of the method in this paper are 21 seconds and 0.06 seconds respectively, which has demonstrated the lightweight and real-time performance of the proposed method. At the same time, more test results show that the proposed ABASNet method can also be implemented in embedded devices and other classification tasks.

In order to improve the accuracy of visual SLAM algorithms in a dynamic scene, instance segmentation is widely used to eliminate dynamic feature points. However, the existing segmentation technology has low accuracy, especially for the contour of the object, and the amount of calculation of instance segmentation is large, limiting the speed of visual SLAM based on instance segmentation. Therefore, this paper proposes a contour optimization hybrid dilated convolutional neural network (CO-HDC) algorithm, which can perform a lightweight calculation on the basis of improving the accuracy of contour segmentation. Firstly, a hybrid dilated convolutional neural network (HDC) is used to increase the receptive field, which is defined as the size of the region in the input that produces the feature. Secondly, the contour quality evaluation (CQE) algorithm is proposed to enhance the contour, retaining the highest quality contour and solving the problem of distinguishing dynamic feature points from static feature points at the contour. Finally, in order to match the mapping speed of visual SLAM, the Beetle Antennae Search Douglas–Peucker (BAS-DP) algorithm is proposed to lighten the contour extraction. The experimental results have demonstrated that the proposed visual SLAM based on the CO-HDC algorithm performs well in the field of pose estimation and map construction on the TUM dataset. Compared with ORB-SLAM2, the Root Mean Squared Error (Rmse) of the proposed method in absolute trajectory error is about 30 times smaller and is only 0.02 m.

Vessel detection and tracking is of utmost importance to river traffic. Efficient detection and tracking technology offer an effective solution to address challenges related to river traffic safety and congestion. Traditional image-based object detection and tracking algorithms encounter issues such as target ID switching, difficulties in feature extraction, reduced robustness due to occlusion, target overlap, and changes in brightness and contrast. To detect and track vessels more accurately, a vessel detection and tracking algorithm based on the LiDAR point cloud was proposed. For vessel detection, statistical filtering algorithms were integrated into the Euclidean clustering algorithm to mitigate the effect of ripples on vessel detection. Our detection accuracy of vessels improved by 3.3% to 8.3% compared to three conventional algorithms. For vessel tracking, L-shape fitting of detected vessels can improve the efficiency of tracking, and a simple and efficient tracking algorithm is presented. By comparing three traditional tracking algorithms, an improvement in multiple object tracking accuracy (MOTA) and a reduction in ID switch times and number of missed detections were achieved. The results demonstrate that LiDAR point cloud-based vessel detection can significantly enhance the accuracy of vessel detection and tracking.

In the micro-strapdown inertial navigation systems of Internet of Things, modeling and filtering of gyro random noise are a useful approach to reducing sensor error and enhancing navigation accuracy. Time series is a popular choice for the gyro random noise modeling process. This paper contains two parts to research the modeling methods of gyro random noise. In the first part, for the problem that conventional autoregressive (AR) modeling methods for fiber optic gyro random noise require mass data due to the slow convergence speed of parameter estimation, furthermore, these methods do not perform well if there are too few samples (e.g., less than 3000), and a novel AR modeling method based on the adaptive Kalman filter is proposed. The proposed method can work with smaller sample sizes due to its rapid parameter estimation convergence speed; its algorithm also yields very precise results. These advantages make it well-suited to applications that require a fast AR modeling method for gyro random noise. In the second part, an experiment reveals that the random noise characteristic of some micro-electromechanical systems gyros is adapted to the moving average (MA) model. However, from the available literature, research on a gyro random noise modeling method based on an MA model is very rare. Therefore, we discuss and analyze two MA modeling methods for gyro random noise and give the comparisons between their modeling results.