This paper conducts a performance evaluation for the ultra‐tight integration of a Global positioning system (GPS) and an inertial navigation system (INS), using nonlinear filtering approaches with an interacting multiple model (IMM) algorithm. An ultra‐tight GPS/INS architecture involves the integration of in‐phase and quadrature components from the correlator of a GPS receiver with INS data. An unscented Kalman filter (UKF), which employs a set of sigma points by deterministic sampling, avoids the error caused by linearization as in an extended Kalman filter (EKF). Based on the filter structural adaptation for describing various dynamic behaviours, the IMM nonlinear filtering provides an alternative for designing the adaptive filter in the ultra‐tight GPS/INS integration. The use of IMM enables tuning of an appropriate value for the process of noise covariance so as to maintain good estimation accuracy and tracking capability. Two examples are provided to illustrate the effectiveness of the design and demonstrate the effective improvement in navigation estimation accuracy. A performance comparison among various filtering methods for ultra‐tight integration of GPS and INS is also presented. The IMM based nonlinear filtering approach demonstrates the effectiveness of the algorithm for improved positioning performance
As the globe undergoes extreme climate changes, disaster events and their disaster scale continue to increase; therefore, it has become imperative to devise disaster prevention measures. Traditional flood monitoring devices, while operating in harsh environments, are often influenced by changes in weather conditions such light, rain, and fog. Consequently, recorded images are often blurred or damaged, which increases the possibility of errors in judgment or delays in the hazard mitigation process. In this study, an automated identification method for flood monitoring based on real-time video images is proposed. The method can be used by the Water Conservancy Agency of the Ministry of Economic Affairs automated for automated identification of flooded areas and for automated determination of the water levels of the main rivers from image data. This paper presents results to show that the capability to detect temporal changes in image sequences is crucial for an automated image-based flood alarm system used in disaster-monitoring applications.
Street lighting is a fundamental aspect of security systems in homes, industrial facilities, and public places. To detect parking lot occupancy in outdoor environments, street light control plays a crucial role in smart surveillance applications that can perform robustly in extreme surveillance environments. However, traditional parking occupancy systems are mostly implemented for outdoor environments using costly sensor-based techniques. This study uses the Jetson TX2 to develop a method that can accurately identify street parking occupancy and control streetlights to assist occupancy detection, thereby reducing costs, and can adapt to various weather conditions. The proposed method adopts You Only Look Once version 3 (YOLO v3, Seattle, WA, USA) based on MobileNet version 2 (MobileNet v2, Salt Lake City, UT, USA), which is area-based and uses voting to stably recognize occupancy status. This solution was verified using the CNRPark + EXT dataset, a simulated model, and real scenes photographed with a camera. Our experiments revealed that the proposed framework can achieve stable parking occupancy detection in streets.
In this paper, the application of the fuzzy interacting multiple model unscented Kalman filter (FUZZY-IMMUKF) approach to integrated navigation processing for the maneuvering vehicle is presented. The unscented Kalman filter (UKF) employs a set of sigma points through deterministic sampling, such that a linearization process is not necessary, and therefore the errors caused by linearization as in the traditional extended Kalman filter (EKF) can be avoided. The nonlinear filters naturally suffer, to some extent, the same problem as the EKF for which the uncertainty of the process noise and measurement noise will degrade the performance. As a structural adaptation (model switching) mechanism, the interacting multiple model (IMM), which describes a set of switching models, can be utilized for determining the adequate value of process noise covariance. The fuzzy logic adaptive system (FLAS) is employed to determine the lower and upper bounds of the system noise through the fuzzy inference system (FIS). The resulting sensor fusion strategy can efficiently deal with the nonlinear problem for the vehicle navigation. The proposed FUZZY-IMMUKF algorithm shows remarkable improvement in the navigation estimation accuracy as compared to the relatively conventional approaches such as the UKF and IMMUKF.
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