High Alignment Accuracy Research Articles

HAlign 4: A New Strategy for Rapidly Aligning Millions of Sequences.

HAlign is a high-performance multiple sequence alignment software based on the star alignment strategy, which is the preferred choice for rapidly aligning large numbers of sequences. HAlign3, implemented in Java, is the latest version capable of aligning an ultra-large number of similar DNA/RNA sequences. However, HAlign3 still struggles with long sequences and extremely large numbers of sequences. To address this issue, we have implemented HAlign4 in C ++. In this version, we replaced the original suffix tree with Burrows-Wheeler Transform (BWT) and introduced the wavefront alignment algorithm to further optimize both time and memory efficiency. Experiments show that HAlign4 significantly outperforms HAlign3 in runtime and memory usage in both single-threaded and multi-threaded configurations, while maintains high alignment accuracy comparable to MAFFT. HAlign4 can complete the alignment of 10 million COVID-19 sequences in about 12 minutes and 300GB of memory using 96 threads, demonstrating its efficiency and practicality for large-scale alignment on standard workstations. Source code is available at https://github.com/malabz/HAlign-4, dataset is available at https://zenodo.org/records/13934503. Supplementary data are available at Bioinformatics online.

REDalign: accurate RNA structural alignment using residual encoder-decoder network

BackgroundRNA secondary structural alignment serves as a foundational procedure in identifying conserved structural motifs among RNA sequences, crucially advancing our understanding of novel RNAs via comparative genomic analysis. While various computational strategies for RNA structural alignment exist, they often come with high computational complexity. Specifically, when addressing a set of RNAs with unknown structures, the task of simultaneously predicting their consensus secondary structure and determining the optimal sequence alignment requires an overwhelming computational effort of O(L6)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(L^6)$$\\end{document} for each RNA pair. Such an extremely high computational complexity makes these methods impractical for large-scale analysis despite their accurate alignment capabilities.ResultsIn this paper, we introduce REDalign, an innovative approach based on deep learning for RNA secondary structural alignment. By utilizing a residual encoder-decoder network, REDalign can efficiently capture consensus structures and optimize structural alignments. In this learning model, the encoder network leverages a hierarchical pyramid to assimilate high-level structural features. Concurrently, the decoder network, enhanced with residual skip connections, integrates multi-level encoded features to learn detailed feature hierarchies with fewer parameter sets. REDalign significantly reduces computational complexity compared to Sankoff-style algorithms and effectively handles non-nested structures, including pseudoknots, which are challenging for traditional alignment methods. Extensive evaluations demonstrate that REDalign provides superior accuracy and substantial computational efficiency.ConclusionREDalign presents a significant advancement in RNA secondary structural alignment, balancing high alignment accuracy with lower computational demands. Its ability to handle complex RNA structures, including pseudoknots, makes it an effective tool for large-scale RNA analysis, with potential implications for accelerating discoveries in RNA research and comparative genomics.

Automated Robotic Cell Fabrication Technology for Stacked‐Type Lithium‐Oxygen Batteries

AbstractRechargeable lithium‐oxygen batteries (LOBs) are gaining interest as next‐generation energy storage devices due to their superior theoretical energy density. While recent years have seen successful operation of LOBs with high cell‐level energy density, the technology for cell fabrication is still in its infancy. This is because the cell fabrication procedure for LOBs is quite different from that of conventional lithium‐ion batteries. The study presents a fully automated sequential robotic experimental setup for the fabrication of stacked‐type LOB cells. This approach allows for high accuracy and high throughput fabrication of the cells. The developed system enables the fabrication of over 80 cells per day, which is 10 times higher than conventional human‐based experiments. In addition, the high alignment accuracy during the electrode stacking and electrolyte injection process results in improved battery performance and reproducibility. The effectiveness of the developed system was also confirmed by investigating a multi‐component electrolyte to maximize battery performance. We believe the methodology demonstrated in the present study is beneficial for accelerating the research and development of LOBs.

Predicting the wear on the non-working side of the rail profile registration method and its validation

Abstract Rail cross-section profile detection can assess the wear and tear of measured rails, providing crucial references for railway maintenance and upkeep. It is challenging to use the conventional method to register only the profile of rail head detection accurately. After the rail edge adjustment in some conventional railways, it becomes difficult to determine the base point of rail profile registration. Due to the wear of non-working edge of rail, a rail profile registration method is proposed. Firstly, a wear prediction model based on the generalized regression neural network is constructed by optimizing smoothing parameters through ten-fold cross-validation to achieve the optimal values. This model predicts the wear values on the non-working rail edge, providing reliable coordinates for two wear points as alignment reference points. Secondly, an initial alignment between the measured profile and the target profile is achieved using the nearest point iterative algorithm, which ensures that both profiles are in the same region and oriented similarly. Thirdly, the weight is assigned to the wear measurement points based on their respective wear values. The predicted positions of wear points are applied for calculating the translation and rotation parameters. These parameters could align the measured profile and facilitate the final profile alignment. Lastly, the experimental profiles under rail adjustment conditions were registered, verifying the accuracy of the proposed method. The research results indicate that under rail adjustment conditions, the mean squared error (MSE) between the calculated and actual values of lateral wear is 0.094 mm2, which is lower than the MSE from manual measurements. The calculated lateral wear value for experimental profiles achieved high alignment and calculation accuracy. This method can be applied in practical projects, providing an effective solution for rail head profile alignment and serving as a reference for profile alignment on the non-working rail edge with incomplete measurement data.

Study on the method of transfer alignment based on the distributed inertial network of guided submunition

Aiming at the problem of poor transfer alignment accuracy between missiles with a high length-to-diameter ratio as a carrier missile and the internally carried guided submunition, this study proposes a transfer alignment method based on the distributed inertial network of the guided submunition. Initially, considering the influence of the deflection deformation of the carrier missile body and the dynamic nonlinear deformation of the lever arm on the transfer alignment accuracy, the corresponding error model is established and compensated for the sub-inertial guide velocity to improve the accuracy of the observed quantities. Additionally, in response to the high failure rate of low-cost inertial guidance systems equipped with guided submunition, we used an inertial network data fusion architecture based on the isolation of device failure monitoring, which effectively isolates the faulty data through the fusion of inertial measurements at the sensor level and optimal fusion of filtering to further improve the reliability of transmission alignment. The effectiveness of the method proposed in this paper is verified through a large number of simulation experiments. Compared with the traditional transfer alignment method between a single master inertial guide and a sub inertial guide, the method in the paper has obvious improvement in the transfer alignment accuracy. In addition, this method is not affected by the failure of inertial devices in the distributed inertial network, and can continuously maintain a high transfer alignment accuracy. For this kind of missile-borne distributed inertial network with a sub-node sets layer, it is found after comparison that: the transmission alignment error is basically the same for different slave nodes in the same sub-node sets; between different sub-node sets, the closer to the master inertial guide the transmission alignment error is smaller. This study provides a new solution idea and certain reference value for solving the problem of poor transfer alignment accuracy between missiles with a high length-to-diameter ratio as a carrier missile and the internally carried guided submunition.

Process Window of Mini-LED Display Panel Packaging using Laser Assisted Bonding Technology

For high-resolution display panels using mini-LEDs, high alignment accuracy and high yield of LED transfer and bonding technology are very important parameters of packaging technologies. These parameters of packaging technologies are highly dependent on the interconnection material and bonding process. The use of laser assisted bonding (LAB) technology and epoxy-based interconnection materials has been introduced in mini-LED display panel packaging to improve bonding accuracy and yield. The bonding process of the entire display panel was performed by a tiling SITRAB process because the laser irradiation area is smaller than the LED bonding area on the substrate. For the SITRAB process, an epoxy-based SITRAB material to remove oxide on the surface of solder bump has been developed as a solvent free material. For good wetting of the solder bump during the SITRAB process, the temperature of the solder bumps must be higher than the melting temperature of solder. In general, the stage temperature of the laser process is determined to be around 100C in order to minimize the laser power. However, if the stage temperature is high, the initial properties of the SITRAB material (such as low viscosity and deoxidation function) may be lost due to the high temperature. In this study, the process window of the SITRAB material was determined for a stable SITRAB process. The process window was identified according to three different pot lifes: room temperature pot life (RPL), stage pot life (SPL), and laser pot life (LPL).

Nanocavity tuning and formation controlled by the phase change of sub-micron-square GST patterns on Si photonic crystals.

It has been well established that photonic crystal nanocavities with wavelength sized mode volume enable various integrable photonic devices with extremely small consumption energy and small footprint. In this study, we explore the possibility of non-volatile functionalities employing photonic crystal nanocavities and phase change material, Ge2Sb2Te5 (GST). Recently, non-volatile photonic devices based on GST have attracted significant interest and are expected to enable energy-efficient photonic processing, especially for optical computing. However, the device size and the area of GST in previous studies have been rather large. Here, we propose and fabricate Si photonic crystal nanocavities on which submicron-square GST patterns are selectively loaded. Because of the strong light confinement, extremely small area of GST is sufficient to manipulate the cavity mode. We have succeeded to fabricate 30-nm-thick and several-100nm-square GST blocks patterned at the center of photonic crystal cavity with a high alignment accuracy. We confirmed that the resonant wavelength and Q-factor of cavity modes are controlled by the phase change of GST. Moreover, cavity formation controlled by submicron-sized GST is also demonstrated by GST-loaded photonic-crystal line-defect waveguides. Our approach in which we place sub-micron-sized GST inside a photonic crystal nanocavity is promising for realizing extremely energy-efficient non-volatile integrable photonic devices, such as switches, modulators, memories, and reconfigurable novel devices.

Quantification of Cervical Elasticity During Pregnancy Based on Transvaginal Ultrasound Imaging and Stress Measurement.

To overcome these limitations, we develop a quantitative cervical elastography system by adding a stress sensor to a clinically used transvaginal ultrasound imaging system. We record the cervical deformation in B-mode images and measure the probe-surface stress through the sensor. Then we quantify the strain using a customized algorithm and estimate the cervical Young's modulus through stress-strain linear regression. In phantom experiments, we demonstrate the system's high accuracy (alignment with the quasi-static compression method, p-value = 0.369 > 0.05), robustness (alignment between 60°- and 90°-contact measurements, p-value = 0.638 > 0.05), repeatability (consistency of single sonographers' measurements, coefficient of variation < 0.06), and reproducibility (alignment between two sonographers' measurements, Pearson correlation coefficient = 0.981). Applying it to pregnant participants, we observe significant cervical softening (p-value < 0.001): Young's modulus decreases 3.95% weekly and its geometric mean value during the first (11 to 13 weeks), second, and third trimesters are 13.07 kPa, 7.59 kPa, and 4.40 kPa, respectively. The proposed system is accurate, robust, and safe, and enables longitudinal and inter-examiner comparisons. The system applies to different ultrasound machines with minor software updates, which allows for studies of cervical softening patterns in pregnancy for larger populations, facilitating insights into conditions such as preterm birth.

Dam UAV Image Stitching Technology Studies Based on Weak Texture

Extraction of image feature point pairs is an important step in the image alignment process. In this paper, we propose a grid-based adaptive threshold feature detection method for weakly textured images using a single threshold extraction algorithm that cannot propose uniformly distributed feature points. Finally, the designed experiments show that the feature points extracted by this method have a lower dispersion than the traditional KAZE algorithm by 70% and a higher alignment accuracy than the traditional KAZE algorithm by 10%.

A Polar Moving Base Alignment Based on Backtracking Scheme

In the polar region, the gravity vector and Earth’s rotation vector tend to be in the same direction, leading to a slower convergence speed and longer alignment time of the moving base alignment. When the alignment time is short, the alignment cannot converge, resulting in low azimuth accuracy. To address this issue, we propose a polar moving base alignment method based on a backtracking scheme. Notably, this work first derives a polar coarse alignment method with the inertial frame based on the transverse Earth model. On this basis, we designed a polar coarse alignment method based on a backtracking scheme and optimized the data storage scheme. Then, a backward navigation algorithm based on the transverse inertial navigation mechanical arrangement scheme was derived, and a polar fine alignment method based on a backtracking scheme was designed. Semi-physical simulation experiments showed that the alignment algorithm based on a backtracking scheme could converge in the 180 s with high alignment accuracy, which is 70% faster than the current polar moving base alignment method.

PQG-A2SA: Performance Quantification Guided Audio-to-Score Alignment for Orchestral Music

Audio-to-score alignment is a multi-modal task that aims at generating an accurate mapping between symbolic and signal-level representations of musical signals, which is important for music performance analysis and retrieval. Among numerous music genres, orchestral music is a category of music with complex performance characteristics such as multi-instrument, non-percussive instrument and music expressiveness. However, previous methods do not take sufficient account of the performance characteristics of orchestral music, leading to limitations in alignment accuracy on orchestral music of these methods. To solve this problem, we present a performance quantification guided audio-to-score alignment (PQG-A2SA) method with high alignment accuracy for orchestral music at note-level. Specially, the PQG-A2SA contains two parts, namely an Inter Onset Interval (IOI) guided conditionally-constrained Dynamic Time Wrapping (DTW) and an articulation guided onset and offset detection. Different from the previous work, the IOI-guided conditionally-constrained DTW is designed to achieve a preliminary mapping between symbolic and chord-level representations of musical signals. In the second module, the onset and offset detection model under different musical articulations are established, thus refining the alignment results. We provide extensive experimental validation and analysis of our method. Our PQG-A2SA method can improve 9.0% in onset align rate and 17.5% in offset align rate at most compared with the state-of-the-art methods.

Efficient Signal Alignment Algorithm for Undersampling Digital Predistortion

Undersampling is an effective way to reduce the cost of digital predistortion (DPD), but it poses challenges for signal alignment, especially for fractional delay alignment. In this letter, a Taylor expansion-based method for fractional delay approximation solution is proposed. To verify the proposed alignment algorithm, the basis manifold regularization (BMR) is introduced to ensure that the DPD can converge to a satisfactory performance even at an ultralow sampling rate. Simulations and experimental tests demonstrate that the proposed method not only has high alignment accuracy but also low complexity in comparison with traditional methods.

Muscle5: High-accuracy alignment ensembles enable unbiased assessments of sequence homology and phylogeny

Multiple sequence alignments are widely used to infer evolutionary relationships, enabling inferences of structure, function, and phylogeny. Standard practice is to construct one alignment by some preferred method and use it in further analysis; however, undetected alignment bias can be problematic. I describe Muscle5, a novel algorithm which constructs an ensemble of high-accuracy alignment with diverse biases by perturbing a hidden Markov model and permuting its guide tree. Confidence in an inference is assessed as the fraction of the ensemble which supports it. Applied to phylogenetic tree estimation, I show that ensembles can confidently resolve topologies with low bootstrap according to standard methods, and conversely that some topologies with high bootstraps are incorrect. Applied to the phylogeny of RNA viruses, ensemble analysis shows that recently adopted taxonomic phyla are probably polyphyletic. Ensemble analysis can improve confidence assessment in any inference from an alignment.

Transfer Printable Single-Crystalline Coupons for III-V on Si Integration

The next-generation internet (6G) requires highly functional devices that e.g. realize frequencies in the THz range for higher data rates and lower latencies. Those requirements exceed the physical limits of established CMOS technologies based on silicon (Si). Hence, there is demand for other semiconductor materials with superior electronic and optical properties that complement Si. One of the key candidates is the III-V compound semiconductor, indium phosphide (InP). Due to its high electron mobility and direct band gap, InP-based devices allow access to frequencies >100 GHz and operate at the optical fibre compatible wavelength of 1.55 μm.1 With the perspective of leveraging the advantages of Si-based CMOS technology and III-V semiconductors, hetero-integration of III-V materials on Si is of great interest. However, existing integration approaches entail certain disadvantages: (i) High dislocation densities due to the lattice mismatch of InP and Si for integration via hetero-epitaxial growth;2 (ii) limited integration density and the requirement of accurate alignment for flip-chip integration; and (iii) high process-related losses of Si and III-V materials as well as thermal stress and low thermal conductivity of adhesive layers degrading device performance for wafer/die bonding technologies.3 Another promising approach for III-V-on-Si integration is micro-transfer-printing (μTP) that involves pick-up and transfer of µm-small chips from a source substrate to a target substrate with high alignment accuracy by using an elastomeric stamp. Advantages of μTP are high integration densities and efficient material use. The technique was already implemented for III-V-on-Si photonic integrated circuits by transfer of epitaxial III-V layers.4 However, using sacrificial III-V interlayers for release and adhesives for bonding still leads to transfer issues and low operation temperature for the devices, respectively.We pursue a new approach to hetero-integration of III-V on Si that aims at the transfer of single-crystalline InP coupons onto Si via μTP. This will be achieved by obtaining crystalline coupons with a thickness of d ≤ 10 µm and two polished surfaces that attain low roughness, needed i.a. for µTP. If the high structural quality of the single-crystalline InP source material can be maintained, this process will provide high quality templates for subsequent epitaxial growth. Towards this goal, we developed a sophisticated micro-preparation process in cooperation with the Leibniz Institute for High Performance Microelectronics IHP.5 Starting from 4-inch single crystals with homogeneous, low dislocation density of 2×103 cm- 2 grown at IKZ,6 thinned InP dies were obtained by sawing, grinding and employing an optimized two-step chemical mechanical polishing (CMP). In order to produce µm-sized transfer-printable coupons, the InP dies were micro structured by means of photolithography assisted patterning and wet (under-)etching (Fig. 1). The coupons can then be picked up with a stamp and transferred to the target wafer. Main innovation of this process is the resin which serves as low stress fixing layer for CMP as well as sacrificial layer for later release.The optimized CMP process with abrasive-free final polishing yielded InP platelets of the desired thickness below 10 μm with low thickness deviation < 1 µm and excellent surface roughness of S q ≈ 0.3 nm (Fig. 2a,b,d). This value even meets the requirements for adhesive-free bonding (S q ≤ 2 nm) and subsequent epitaxial growth (S q ≤ 0.5 nm). X-ray rocking curve mapping provides accurate spatial maps of lattice deformations in the material that may be a consequence of the mechanical processing. Rocking curve widths mappings of the 004 reflection of a (001) sample before and after thinning are homogeneous and below 25 arcsec in the majority of the sample area. Overall no signs of systematic crystal quality deterioration in the product platelets compared to bulk samples have been detected.In summary, the feasibility of μm-thin InP platelet fabrication was demonstrated. Final platelets meet the prerequisites of low and uniform thickness, high planarity, low roughness and little crystal quality deterioration. Furthermore, first InP platelets could successfully be patterned to 100–400 µm-sized coupons using optical lithography and wet etching (Fig. 2c). This opens a path to take the next steps towards hetero-integration on Si by means of µTP with high potential for adhesive-free bonding.

Automated Computer Vision-Enabled Manufacturing of Nanowire Devices.

We present a high-throughput method for identifying and characterizing individual nanowires and for automatically designing electrode patterns with high alignment accuracy. Central to our method is an optimized machine-readable, lithographically processable, and multi-scale fiducial marker system─dubbed LithoTag─which provides nanostructure position determination at the nanometer scale. A grid of uniquely defined LithoTag markers patterned across a substrate enables image alignment and mapping in 100% of a set of >9000 scanning electron microscopy (SEM) images (>7 gigapixels). Combining this automated SEM imaging with a computer vision algorithm yields location and property data for individual nanowires. Starting with a random arrangement of individual InAs nanowires with diameters of 30 ± 5 nm on a single chip, we automatically design and fabricate >200 single-nanowire devices. For >75% of devices, the positioning accuracy of the fabricated electrodes is within 2 pixels of the original microscopy image resolution. The presented LithoTag method enables automation of nanodevice processing and is agnostic to microscopy modality and nanostructure type. Such high-throughput experimental methodology coupled with data-extensive science can help overcome the characterization bottleneck and improve the yield of nanodevice fabrication, driving the development and applications of nanostructured materials.

Knowledge Fusion Analysis and Research Based on Knowledge Graph

With the rapid popularization of Internet technology, it is a new challenge to mine the hidden knowledge behind the data in an organized and directed way. Knowledge graph not only describes the content of semantic associations and entities existing in the objective world, but also uses graph structure to visually present the structured knowledge system for system users. Therefore, in the development of modern technology, knowledge graph has been widely concerned by industry and academia. In the era of big data, in order to build high-quality knowledge graph, knowledge fusion is very critical, among which entity alignment and entity link are important parts of knowledge fusion task. Starting from the field of film and television, this paper uses the entity alignment technology of NovEA model and the candidate entity ranking structure based on CNN-DSSM to deeply discuss the key technologies of knowledge fusion during the construction of multi-level film and television knowledge graph. The final experimental results prove that, compared with other models of the same task, the NovEA model studied in this paper has higher alignment accuracy, and the candidate entity ranking structure based on CNN-DSSM is better designed.

Knowledge Fusion Analysis and Research Based on Knowledge Graph

With the rapid popularization of Internet technology, it is a new challenge to mine the hidden knowledge behind the data in an organized and directed way. Knowledge graph not only describes the content of semantic associations and entities existing in the objective world, but also uses graph structure to visually present the structured knowledge system for system users. Therefore, in the development of modern technology, knowledge graph has been widely concerned by industry and academia. In the era of big data, in order to build high-quality knowledge graph, knowledge fusion is very critical, among which entity alignment and entity link are important parts of knowledge fusion task. Starting from the field of film and television, this paper uses the entity alignment technology of NovEA model and the candidate entity ranking structure based on CNN-DSSM to deeply discuss the key technologies of knowledge fusion during the construction of multi-level film and television knowledge graph. The final experimental results prove that, compared with other models of the same task, the NovEA model studied in this paper has higher alignment accuracy, and the candidate entity ranking structure based on CNN-DSSM is better designed.

A MEMS Device Integrating Multiple Cantilever Displacement Sensors to Evaluate Flat Machined Surfaces

Multi-point scanning measurement, which is effective in eliminating motion errors of the stage in on-machine profile measurement, requires multiple displacement sensors of equal pitch to measure displacements simultaneously. However, it is not easy to arrange small sensors with high alignment accuracy when applying the multi-point method at a narrow pitch. In addition, if many sensors can be arranged in parallel, improvement in measurement accuracy can be expected. Therefore, a new micro electro mechanical system (MEMS) device for straightness measurement, one that integrates 10 cantilever displacement sensors, has been proposed. This device can be expected to solve the problem involved in the multi-point method because of the characteristics of MEMS, as the semiconductor processing method can make mechanical structures with high accuracy and it can easily make the device with many identical structures. The device is designed to measure waviness less than 100 μm in height. Ten cantilevers of 11 mm length are fabricated in parallel with 1.8 mm pitch on a side of a base substrate 20 mm square. The strain induced by a displacement of the probe placed near the front edge of the cantilever is detected as a change in the resistance of the piezo resistor at the foot of the cantilever. In the fabrication process of this device, crystal anisotropic etching is performed for 12 hours to form probes 250 μm high. A new fabrication process is also proposed in which a protective process is added to prevent damage to the circuits already formed during the etching. A prototype is investigated, and it is found that the resistance value increases about 0.45% in proportion to the displacement of 100 μm. It is therefore confirmed that this device has the basic ability to detect displacement.

Establishment and Extension of a Fast Descriptor for Point Cloud Registration

Point cloud registration (PCR) is a vital problem in remote sensing and computer vision, which has various important applications, such as 3D reconstruction, object recognition, and simultaneous localization and mapping (SLAM). Although scholars have investigated a variety of methods for PCR, the applications have been limited by low accuracy, high memory footprint, and slow speed, especially for dealing with a large number of point cloud data. To solve these problems, a novel local descriptor is proposed for efficient PCR. We formed a comprehensive description of local geometries with their statistical properties on a normal angle, dot product of query point normal and vector from the point to its neighborhood point, the distance between the query point and its neighborhood point, and curvature variation. Sub-features in descriptors were low-dimensional and computationally efficient. Moreover, we applied the optimized sample consensus (OSAC) algorithm to iteratively estimate the optimum transformation from point correspondences. OSAC is robust and practical for matching highly self-similar features. Experiments and comparisons with the commonly used descriptor were conducted on several synthetic datasets and our real scanned bridge data. The result of the simulation experiments showed that the rotation angle error was below 0.025° and the translation error was below 0.0035 m. The real dataset was terrestrial laser scanning (TLS) data of Sujiaba Bridge in Chongqing, China. The results showed the proposed descriptor successfully registered the practical TLS data with the smallest errors. The experiments demonstrate that the proposed method is fast with high alignment accuracy and achieves a better performance than previous commonly used methods.

A Fast Robust In-Motion Alignment Method for Laser Doppler Velocimeter-Aided Strapdown Inertial Navigation System

With ultra-high velocity measurement accuracy, laser Doppler velocimeter (LDV) is promising to replace the odometer to provide vehicle velocity information in the process of land integrated navigation. This paper investigates the fast in-motion initial alignment for LDV-aided strapdown inertial navigation system (SINS). Aiming at the problem that the installation misalignment angles and scale factor error of LDV will reduce the alignment accuracy and the alignment speed, a Robust Double Gain Square-Root Unscented Quaternion Estimator (RDGSR-USQUE) method with good effect at large misalignment angle is proposed. In RDGSR-USQUE, the influence of unknown parameter error is considered in the construction of process model and measurement model and a double gain structure is proposed to improve the convergence speed based on the usually neglected posterior measurement residual information. RDGSR-USQUE method improves the defects of the traditional Unscented Quaternion Estimator (USQUE) method, such as poor noise resistance, slow convergence speed under large misalignment angle and easy to lead to non-positive definite covariance matrix. This will help to estimate and compensate unknown parameter errors while estimating attitude, so as to improve the accuracy of process model and measurement model, and finally improve the accuracy of attitude estimation. The performance of the proposed scheme is verified by vehicle field test. The results show that the proposed method has higher alignment accuracy, faster convergence speed and stronger robustness than three other compared methods, and the estimated installation misalignment angle and scale factor of LDV are satisfactory in general cases.