Low-cost MEMS-INS/GPS integration using nonlinear filtering approaches

Some important key issues in GNSS/INS integration mainly arise in the field of creating and developing low-cost, robust and at the same time highly accurate navigation systems, putting a focus of interest onto powerful sensor fusion algorithms. The so-called tightly-coupled integration is one of the most promising approaches to fuse the GNSS (global navigation satellite systems) data with INS (inertial navigation system) measurements. However, when modeling the underlying problem, the system process and observation models turn out to be nonlinear, and the GNSS stochastic measurement errors are often non-Gaussian distributed (e.g., due to multipath effects). Among other estimation approaches, the so-called particle filter (PF) as a nonlinear/non-Gaussian estimation method is especially theoretically attractive to be used in this field. However, its large computational burden usually limits its practical usage. In order to reduce the computational burden without degrading the system estimation accuracy, recently, an unscented particle filter (UPF) has been proposed, which combines the PF with the unscented Kalman filter (UKF). In this thesis, only one UKF is used in the algorithm, and the re-sampling step is not required anymore. Thus, the number of particles can be largely reduced, and the implementation of the PF on a hardware platform turns out to be feasible.Aktuelle Entwicklungen auf dem Gebiet der Fusion von inertialer Navigation und satellitengestützten Positionierungsverfahren zielen klar auf kosteneffiziente, robuste und gleichzeitig hochpräzise Lösungen ab. Leistungsfähige Sensordatenfusionsansätze spielen hier eine Schlüsselrolle, wobei die sogenannte "Tightly Coupled Integration" zur Fusion der satellitengestützten Navigationsdaten mit den Messdaten eines inertialen Systems besonders vielversprechend erscheint. Als erschwerender Umstand ergeben sich hier allerdings nichtlineare Prozess- und Beobachtungsmodelle, die in Verbindung mit nicht länger gaußverteilten Beobachtungsfehlern, beispielsweise aufgrund von Mehrwegeausbreitung, nichtlineare, möglichst optimale Datenfusionsverfahren, wie beispielsweise Partikelfilter-Ansätze erfordern. Theoretisch elegant und leistungsfähig auf der einen Seite, benötigen diese Ansätze in der praktischen Realisierung vielfach eine ungemein hohe Anzahl von einzelnen "Partikeln", so dass der hierdurch verursachte Berechnungsaufwand die praktische Einsatzfähigkeit unter Echtzeitbedingungen vielfach entweder im Hinblick auf die Filterperformance oder auf die Taktzeit limitiert. Ein Ansatz zur Lösung dieser Problematik besteht in der Kombination eines Partikelfilters mit einem Unscented Kalman Filter. Hierbei wird der sonst bei Partikelfiltern übliche, aber zeitaufwändige, Resampling Schritt nicht mehr benötigt. Auch die Anzahl der benötigten Partikel kann stark reduziert werden, so dass eine Realisierung auf einer Signalprozessorplattform möglich wird

A Hyper-pixel-wise Contrastive Learning Augmented Segmentation Network for Old Landslide Detection Using High-Resolution Remote Sensing Images and Digital Elevation Model Data

As a harzard disaster, landslide often brings tremendous losses to humanity, so it's necessary to achieve reliable detection of landslide. However, the problems of visual blur and small-sized dataset cause great challenges for old landslide detection task when using remote sensing data. To reliably extract semantic features, a hyper-pixel-wise contrastive learning augmented segmentation network (HPCL-Net) is proposed, which augments the local salient feature extraction from the boundaries of landslides through HPCL and fuses the heterogeneous infromation in the semantic space from High-Resolution Remote Sensing Images and Digital Elevation Model Data data. For full utilization of the precious samples, a global hyper-pixel-wise sample pair queues-based contrastive learning method, which includes the construction of global queues that store hyper-pixel-wise samples and the updating scheme of a momentum encoder, is developed, reliably enhancing the extraction ability of semantic features. The proposed HPCL-Net is evaluated on a Loess Plateau old landslide dataset and experiment results show that the model greatly improves the reliablity of old landslide detection compared to the previous old landslide segmentation model, where mIoU metric is increased from 0.620 to 0.651, Landslide IoU metric is increased from 0.334 to 0.394 and F1-score metric is increased from 0.501 to 0.565

A test paper generation algorithm based on diseased enhanced genetic algorithm

With the continuous progress of society, tests, and exams appear more and more frequently in people's lives. Faced with the ever-increasing demand for test papers, efficient test paper generation algorithms have become more important. In this paper, we improved and proposed a Diseased Enhanced Genetic Algorithm (DEGA) based on the Genetic Algorithm (GA), and applied it to the test paper generation algorithm. I the crossover operator, the crossover probability that will change in different situations of the population is adopted. According to the characteristics of the test paper generation algorithm, we use the method based on the hamming distance to calculate the distance between individuals in the population. Aiming at the shortcoming that the mutation operator is too random, we designed and used a disease operator that includes three modules: natural disease, infection, and mutation. It effectively guarantees the distance between individuals in the population and improves the shortcoming that GA is easy to fall into a locally optimal solution. Finally, using the College English Test Band 4 (CET-4) questions from 2014 to 2021 as the data set, comparative experiments were carried out on the test paper generation algorithm based on Random Sampling Algorithm (RSA), GA, Enhanced Genetic Algorithm (EGA) and DEGA. The results show that when using the test paper generation algorithm based on DEGA, the generation of test papers is faster, the number of iterations is less, and the algorithm results are significantly better than other algorithms

On Position and Attitude Estimation for Remote Sensing with Bistatic SAR

Non

2D fin field-effect transistors integrated with epitaxial high-k gate oxide

Precise integration of two-dimensional (2D) semiconductors and high-dielectric-constant (k) gate oxides into three-dimensional (3D) vertical-architecture arrays holds promise for developing ultrascaled transistors(1-5), but has proved challenging. Here we report the epitaxial synthesis of vertically aligned arrays of 2D fin-oxide heterostructures, a new class of 3D architecture in which high-mobility 2D semiconductor fin Bi2O2Se and single-crystal high-k gate oxide Bi2SeO5 are epitaxially integrated. These 2D fin-oxide epitaxial heterostructures have atomically flat interfaces and ultrathin fin thickness down to one unit cell (1.2 nm), achieving wafer-scale, site-specific and high-density growth of mono-oriented arrays. The as-fabricated 2D fin field-effect transistors (FinFETs) based on Bi2O2Se/Bi2SeO5 epitaxial heterostructures exhibit high electron mobility (mu) up to 270 cm2 V-1 s(-1), ultralow off-state current (I-OFF) down to about 1 pA mu m(-1), high on/off current ratios (I-ON/I-OFF) up to 10(8) and high on-state current (I-ON) up to 830 mu A mu m(-1) at 400-nm channel length, which meet the low-power specifications projected by the International Roadmap for Devices and Systems (IRDS)(6). The 2D fin-oxide epitaxial heterostructures open up new avenues for the further extension of Moore's law