The performance of Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) integrated navigation can be severely degraded in urban canyons due to the non-line-of-sight (NLOS) signals and multipath effects. Therefore, to achieve a high-precision and robust integrated system, real-time fault detection and localization algorithms are needed to ensure integrity. Currently, the residual chi-square test is used for fault detection in the positioning domain, but it has poor sensitivity when faults disappear. Three-dimensional (3D) light detection and ranging (LiDAR) has good positioning performance in complex environments. First, a LiDAR aided real-time fault detection algorithm is proposed. A test statistic is constructed by the mean deviation of the matched targets, and a dynamic threshold is constructed by a sliding window. Second, to solve the problem that measurement noise is estimated by prior modeling with a certain error, a LiDAR aided real-time measurement noise estimation based on adaptive filter localization algorithm is proposed according to the position deviations of matched targets. Finally, the integrity of the integrated system is assessed. The error bound of integrated positioning is innovatively verified with real test data. We conduct two experiments with a vehicle going through a viaduct and a floor hole, which, represent mid and deep urban canyons, respectively. The experimental results show that in terms of fault detection, the fault could be detected in mid urban canyons and the response time of fault disappearance is reduced by 70.24% in deep urban canyons. Thus, the poor sensitivity of the residual chi-square test for fault disappearance is improved. In terms of localization, the proposed algorithm is compared with the optimal fading factor adaptive filter (OFFAF) and the extended Kalman filter (EKF). The proposed algorithm is the most effective, and the Root Mean Square Error (RMSE) in the east and north is reduced by 12.98% and 35.1% in deep urban canyons. Regarding integrity assessment, the error bound can overbound the positioning errors in deep urban canyons relative to the EKF and the mean value of the error bounds is reduced.

LiDAR simultaneous localization and mapping (SLAM) is widely used in intelligent vehicles for environment perception, self-localization, and mapping. However, in specific environments such as long corridors, highways, or caves, LiDAR SLAM may fail to perceive changes in the surrounding environment, leading to an inability to estimate motion, known as LiDAR degeneracy. LiDAR degeneracy significantly affects the accuracy of LiDAR localization. To address this issue, this paper proposes a LiDAR degeneracy recognition algorithm based on a dynamic threshold and mitigates LiDAR degeneracy by introducing intermittent visual-inertial odometry (VIO). We model dynamic thresholds from position accuracy demands through simple mathematical derivations, avoiding heuristic tuning. We also propose an intermittent VIO that operates only during LiDAR degeneracy, achieving comparable or better position performance with minimal computational resource utilization. To improve the intermittent VIO, we use LiDAR for fast initialization of the VIO and visual feature depth association, and we also introduce LiDAR points as features in the VIO motion estimation. Through experiments on simulated and public datasets, our method accurately identifies LiDAR degeneracy and effectively mitigates it. Experimental results show that our method outperforms state-of-the-art methods with lower average CPU usage while achieving similar or higher accuracy.

Radio frequency interference (RFI) can have adverse effects on commercial electronics. Current properties of high-performance integrated circuits (ICs), such as very small feature sizes, high clock frequencies, and reduced voltage levels, increase the susceptibility of these circuits to RFI, causing them to be more prone to smaller RFI levels. Besides, recent developments of mobile devices and wireless networks create a hostile electromagnetic environment for ICs. Therefore, it is important to generalize the susceptibility of ICs to RFI. In this study, we investigate the susceptibility levels and frequency ranges of RFI to the clock network of a digital device, an 8-bit ripple counter, designed and fabricated using AMI 0.5 μm process technology. Our experimental setup is designed to couple a pulse-modulated RF signal using the direct pin injection method. Our experiments show that relatively low levels of RFI (e.g., 16.8 dBm delivered RF peak power with carrier frequency of 1 GHz) could adversely affect the normal functioning of the device under testing. In the end, SPICE simulations show the sensitivity of a flip-flop, the basic building block of clock network, which further explains the experimental results.

It is a challenge to distinguish between different cloud types because of the complexity and diversity of cloud coverage, which is a significant clutter source that impacts on target detection and identification from the images of space-based infrared sensors. In this paper, a novel strategy for cloud classification in wide-swath passive sensor images is developed, which is aided by narrow-swath active sensor data. The strategy consists of three steps, that is, the orbit registration, most matching donor pixel selection, and cloud type assignment for each recipient pixel. A new criterion for orbit registration is proposed so as to improve the matching accuracy. The most matching donor pixel is selected via the Euclidean distance and the square sum of the radiance relative differences between the recipient and the potential donor pixels. Each recipient pixel is then assigned a cloud type that corresponds to the most matching donor. The cloud classification of the Moderate Resolution Imaging Spectroradiometer (MODIS) images is performed with the aid of the data from Cloud Profiling Radar (CPR). The results are compared with the CloudSat product 2B-CLDCLASS, as well as those that are obtained using the method of the International Satellite Cloud Climatology Project (ISCCP), which demonstrates the superior classification performance of the proposed strategy.

Various intelligent trash cans on market at present have many drawbacks. They are complex, expensive, and have only a few functions. A new type of intelligent garbage classification box is designed, which contains four garbage classification buckets. It has the ability to automatically open or close the trash can when users pass by through infrared sensor, classify the garbage into four categories using Raspberry PI, rotate the platform on which the garbage classification buckets are based to put the garbage in place. In addition, the capacity of the garbage sorting bucket is also monitored by ultrasonic ranging sensors. When the bucket is almost full, the garbage can will compress the garbage below or set off an alarm to warn users. The intelligent garbage sorting box is of much use and possesses a high degree of automation, meeting the needs of the majority of home users. So it has a good market application prospect.