Traditional global navigation satellite systems (GNSS) based on vector tracking (VT) have various structures. However, some GNSS measurements have no connection with the dynamic of the user when the loop loses lock in the shade, e.g. urban canyon, tree shade, etc. To improve the navigation accuracy in a weak environment, a distributed GNSS/inertial navigation system (INS) integrated navigation system based on VT is proposed. The change in the pseudo-range rate and pseudo-range from the loop is employed in the GNSS receiver. The reliable navigation results can be output from a distributed navigation filter according to effective GNSS measurements and modified INS information. The field test shows that the proposed distributed GNSS/INS navigation system can achieve reliable navigation in weak signal environments.

Aiming to avoid personal injury caused by the failure of timely medical assistance following a fall by seafarer members working on ships, research on the detection of seafarer’s falls and timely warnings to safety officers can reduce the loss and severe consequences of falls to seafarers. To improve the detection accuracy and real-time performance of the seafarer fall detection algorithm, a seafarer fall detection algorithm based on BlazePose–LSTM is proposed. This algorithm can automatically extract the human body key point information from the video image obtained by the vision sensor, analyze its internal data correlation characteristics, and realize the process from RGB camera image processing to seafarer fall detection. This fall detection algorithm extracts the human body key point information through the optimized BlazePose human body key point information extraction network. In this section, a new method for human bounding-box acquisition is proposed. In this study, a head detector based on the Vitruvian theory was used to replace the pre-trained SSD body detector in the BlazePose preheating module. Simultaneously, an offset vector is proposed to update the bounding box obtained. This method can reduce the frequency of repeated use of the head detection module. The algorithm then uses the long short-term memory neural network to detect seafarer falls. After extracting fall and related behavior data from the URFall public data set and FDD public data set to enrich the self-made data set, the experimental results show that the algorithm can achieve 100% accuracy and 98.5% specificity for the seafarer’s falling behavior, indicating that the algorithm has reasonable practicability and strong generalization ability. The detection frame rate can reach 29 fps on a CPU, which can meet the effect of real-time detection. The proposed method can be deployed on common vision sensors.

Global Navigation Satellite System-Reflectometry (GNSS-R) is one of the main technologies used for sea ice remote sensing detection, and is based on the multipath interference effect of satellite signals. To improve the GNSS-R sea ice detection performance in terms of accuracy, robustness to noise, and data utilization, a linear discriminant analysis (LDA)-based method was proposed in this paper. Delay-Doppler maps (DDMs) collected from TechDemoSat-1 (TDS-1) were employed as input and classified into different types based on the signal-to noise ratio (SNR) related to the noise effect. For low-effect-noise DDMs, the LDA-based sea-ice detection method presented an accuracy of 95.03%, verifying the feasibility of LDA-based GNSS-R sea-ice detection. For the middle noise effect and high noise effect DDMs, the LDA-based method is more robust to noise effects than the convolutional neural network (CNN) method. Although the detection accuracy decreased when the SNR decreased or integral delay waveform average (IDWA) increased, the LDA-based method was more robust than the CNN-based one. The data utilization and melting period were also analyzed to account for variations in detection accuracy. The LDA-based method used 67.82% more data than previous experiments with threshold IDWA≤58210.32 and SNR>-17.48dB. The melting periods were analyzed based on the noise, SNR, surface reflectivity, and permittivity. When the status of sea ice changes, outliers of surface reflectivity appear, the average permittivity varies in [10, 60], and the detection accuracy decreases during the melting period of sea ice. The results show that the correlation coefficient with the National Oceanic and Atmospheric Administration (NOAA) data is up to 0.93, with different threshold IDWA or IDWA. The LDA-based method predicted results that greatly matched the sea ice distribution from the NOAA data.

Global navigation satellite systems and inertial navigation systems (GNSSs/INSs) based on the cubature Kalman filter (CKF) can provide an effective land vehicle navigation and positioning solution. The accuracy of CKF depends mainly on the prior probability density function (PDF), which is inaccurate when the GNSS signal is temporarily blocked in an urban environment. Although the improved CKF (ICKF) can reduce cubature point errors by using the historical information obtained iteratively, the generation of cubature points is still related to measurement noise. To obtain a more accurate PDF, adjustment off the sensor measurement noise is necessary. In this paper, an adaptive ICKF for GNSS/INS system based on the Mahalanobis distance called MD-ICKF is proposed, which expands the usability of the micro-electronic mechanical system based integrated navigation system in an urban environment. Two lower triangular matrices obtained by Cholesky decomposition are used to adjust the cubature point errors during each iteration. Furthermore, the measurement noise will be inflated to obtain a more accurate PDF when it exceeds the threshold calculated by the MD and chi-square test. The field experiment results show that the proposed method has better positioning accuracy and performance than CKF and ICKF in an occluded environment