Observation angular distance error modeling and matching threshold optimization for terrestrial star tracker.

As the criterion to determine whether stars are identified, the star image identification matching threshold is an important parameter in terrestrial star trackers. It not only determines the identification success rate, but also affects the redundant matching quantity and identification efficiency. This paper focuses on the problem that the identification efficiency of terrestrial star trackers is restricted by inappropriate matching thresholds and presents an adaptive dynamic adjustment star identification threshold model. Compared to the existing matching threshold, the presented model clarifies the transformation of observation star angular distance errors and dynamically estimates the corresponding identification threshold as the variation of observation angular distances and attitudes. Therefore, we believe it completes the star identification with an excellent redundant matching quantity and identification efficiency. Numerical simulation and night sky experimental results showed that the identification efficiency was improved by more than 46.54% and 22.61%, respectively, while the identification success rate remained at 100%.

A novel method for subdivided locating of star image in star identification

The successful identification of stars is a fundamental prerequisite for satellite attitude determination by star sensors. Conventional star identification algorithms typically construct specific subgraphs using a set of bright stars or generate patterns based on angular distances between stars and their neighboring stars. However, these methods often fail when insufficient detectable stars or bright noncooperative space objects are in the field of view (FOV). Seldom studies use angular distance matching among all stars for direct identification as it is complex to calculate and store. To solve the problem, we proposed a graph algorithm based on angular distance matching score transfer, which utilizes a graph data structure, where nodes store angular distance matching scores between stars and transfer scores through graph edge weights, overcoming the limitations of incomplete utilization of angular distance matching results. We also established a math model for edge weights based on the probabilities of star pair occurrences. Simulation tests and night sky image experiments demonstrate the robustness of this algorithm against position errors, brightness errors, and fake stars. By equally using information from all sensor stars, even in cases with multiple missing stars, numerous fake stars, and extremely bright interfering stars, the identification rate remains consistently above 99.53%. This algorithm represents a highly robust star identification method and lays the foundation for future research in intelligent sensing of space environmental objects using star sensors.

In this paper, a laboratory model is designed to evaluate the performance of star tracker algorithms for attitude determination of a satellite. Star tracker is the most accurate attitude sensor that determines satellite direction by applying centroiding algorithm, star identification and attitude determination. To utilize such algorithms, first, high quality of star images are needed which should be provided through the star tracker camera. Then, such images are given to processors and it determines the attitude of camera and satellite in three axes based on mentioned algorithms. First, in preliminary design, we define important star tracker parameters, like accuracy, detector, processor and field of view. In this paper, we have considered the range accuracy in yaw/pitch axis less than 20 arcsec and for roll axis less than 100 arcsec. To improve the attitude determination accuracy, we have applied an adaptive structure in centroiding algorithm, as only brightness stars are selected and identification algorithm is done based on them. Another important parameter is speed of identification algorithm which is handled by an ARM processor and improving pyramid algorithm, we reached less than 25 ms. Duo to this time, updating rate would be desired. Also knowing the coordination of bore sight direction that is distance between focal lens and imager is another important parameter that affects the accuracy of attitude and by ground calibration of camera, this parameter could be estimated carefully. Finally, implementation results on real images that are captured by a star tracker demonstrate optimum performance of algorithms.

Star sensor, which is the key factor in remote sensing satellite geometric accuracy, became more and more important in attitude determination and is widely used in current remote sensing satellites during the last years, for its highest absolute attitude measuring accuracy. Although the parameters of the stellar camera underwent a quantitative pre-launch calibration, the strong vibration that a satellite suffers during its launch and the change of space environmental factors are the major reasons that cause the variation of the parameters of the stellar camera, which then lead to the deterioration in attitude measurement accuracy of star tracker. For ZY 3 domestic APS star sensors on-board application, this paper propose a new on orbit calibration method for stellar camera based on the angular distance, with analyzing the regularity of lens distortion change, of which interior orientation elements and the lens distortion are simultaneously calibrated and the corresponding formula is derived. The experimental results with stellar image data from ZY-3 satellite demonstrate the advancement and effectiveness of this calibration method in improving the calibration accuracy of the stellar camera parameters.

In order to quickly navigate with the known rough azimuth and location information by three FOVs positioning and orientation device,a fast recognition method for local star pattern is proposed. First,we analyse the reasons of the low efficiency when performing the stars identification using global recognition database.Next,we note that it is a preferred method to perform star identification in one FOV firstly and identify the remaining stars between different FVOs,and give the angular distance error threshold. Then,we propose a method to generate the local recognition database,which can reduce the redundancy of identification information and improve recognition efficiency. The simulaion and field experiment results show that the correct iden-tification rate using this recognition method is improved up to 99. 19%,and rocognition time is about 24. 3ms,which can satisfy the system requirements for fast and efficient navigation. It also proves the correctness of the preferred recognition order.

To endow a star sensor with strong robustness, low algorithm complexity, and a small database, this paper proposes an all-sky star identification algorithm based on spherical polar pattern matching. The proposed algorithm consists of three main steps. First, the guide star is rotated to be a polar star, and the polar and azimuth angles of neighboring stars are used as polar pattern elements of the guide star. Then, the relative azimuth histogram is applied to the spherical polar pattern matching, and a star pair after spherical polar pattern matching is identified through angular distance cross-verification. Finally, a reference star image is generated from the identified star pair to complete the matching process of all guide stars in the field of view. The proposed algorithm is verified by simulation experiments. The simulation results show that for a star sensor with a medium field of view (15° × 15°, 1024 × 1024 pixel) and a limiting magnitude of 6.0 Mv, the required database size is 161 KB. When false and missing star spots account for 50% of the guide stars and the star spot extraction error is 1.0 pixel, the average star identification time is 0.35 ms (@i7-4790), and the identification probability is 99.9%. However, when false and missing star spots account for 100% of the guide stars and the star spot extraction error is 5.0 pixel, the average star identification time is less than 2.0 ms, and the identification probability is 97.1%.

Laser three-dimensional (3D) image is a novel non-cooperative target 3D image acquisition technology, and the improvements in detection capability and imaging accuracy of the system are critically dependent on efficient echo-signal processing technique and 3D reconstruction method. The registration process is an essential step in array 3D imaging laser point cloud data processing. Registration of point clouds is an effective method that solves the problem caused by the target self-occlusion in the laser 3D imaging system. The accurate registration result will help provide better support for subsequent applications, such as object reconstruction and target recognition. In this study, a set of thresholds in the iterative closest point (ICP) algorithm is analysed on the basis of the characteristics of the laser array 3D imaging system and is combined with the range error and visual lateral resolution of the system, which are both important parameters in the imaging system. To improve the accuracy and speed of registration, the stop threshold of the iterative algorithm and the corresponding point-distance threshold in the algorithm are established in a novel way based on the range error and visual lateral resolution of the system. This forms the foundation, based on which an adaptive threshold ICP algorithm is proposed. The principal idea of the algorithm is to improve the threshold set that has a considerable effect on the accuracy and speed of registration. At first, the characteristics of the imaging point clouds of the laser array 3D imaging system are analysed in the algorithm. Based on this analysis, the distance between the two point clouds and corresponding points with ideal registrations are estimated theoretically, according to the range error and visual lateral resolution of the system. The simulation results show that the theoretically estimated results and actual results have the same variation tendency, thus providing a theoretical basis for subsequent improvements. Next, the estimated results are added according to the iterative closest point algorithm. This implies that the registration thresholds are capable of changing and adapting under different iterations and imaging systems, thus improving the speed and accuracy of registrations. This phenomenon is not seen in other algorithms. Experiments involving laser array imaging of a point cloud and laser scanning of depth imaging data show that the algorithm is practical and effective for both imaging types of point clouds and can improve the speed and accuracy of registration notably. The effectiveness and feasibility of the proposed algorithm are thus verified. In addition, for its full consideration of the imaging system, the basic idea of the proposed algorithm can be used for designing future applications as required.

This paper presents a robust control algorithm for exponentially stable tracking control of a differentially steered Wheeled Mobile Robot (WMR). The exponential tracking property is guaranteed with kinematic uncertainties, such as wheel slippage and radius errors. The control algorithm has been designed for use in a WMR which is being specifically developed for applications in highway maintenance and construction. For such applications, the uncertainties arise due to the road surface conditions and the high loads encountered (both inertial and tool forces). However, the system under development is equipped with a two dimensional cable extension transducer which has been proven to be a rugged and accurate position measurement system (Hong, et al., 1995), so that the robust control algorithm developed is suitable and ensures good tracking performance. The algorithm is applicable to any differentially steered WMRs provided that accurate position measurement is available. The efficiency of the robust control algorithm is illustrated with numerical simulations.

Axial powder stream concentration between the nozzle end and the deposition point is an important process parameter in the laser aided material deposition process. The powder concentration is greatly influenced by the nozzle geometry in use. This paper describes the numerical and experimental analysis of this important parameter in relation to the coaxial nozzle. The experiments are performed with the different nozzle geometries to generate various flow patterns of the gravity fed powder in a cold stream. The results of the experimental analysis are compared with the numerical simulation and found justified. These results are used in concluding the significance of important nozzle parameters for various powder concentration modes. Introduction Laser deposition process consists of feeding the metal powder into a hot spot called as melt pool to form a melted zone which solidifies into a bead. The coaxial nozzle is widely used for this metal deposition process [3, 9]. The metal powder is fed through the nozzle in the melt pool in a coaxial system instead of the external tubes as in the side nozzle. Laser aided deposition quality largely depends on the powder stream structure below the nozzle [7]. The variation of the powder stream concentration in axial direction affects the material delivery rate at the deposition point and the interaction of the laser beam radiation with the powder stream [4]. High efficiency of the powder deposition in a coaxial system is an important subject for the study. The focusability of the stream structure is a key factor of the energy utilization and catchment efficiency of the coaxial nozzle [5]. It is found that most of the laser power reaches the substrate with some energy loss in the particles in laser deposition process [9]. This is sometimes caused by the shadow effect of one particle over another due to the absorption of the beam energy by the powder stream. The stream structure is mainly influenced by the powder flow settings and nozzle arrangements [5]. It is also found that the powder catchment increases with increase in the powder flow velocity [9]. The process of deposition involves control of various parameters like powder type, powder flow velocity and gas velocities. Though these are actual process parameters, the initial powder flow is defined by the nozzle geometry in use. Thus, optimizing the deposition nozzle for such process is not an easy task and hence requires a lot of experimentation and numerical simulation. It is difficult to find the literature explaining about the effect of nozzle geometry at the powder outlet area of the coaxial nozzle on the powder concentration mode. Though the above mentioned studies have been carried out, the detailed study needed to be done entailing the effect of the nozzle geometry at the powder passage on the flow mode. The initial consideration should be given in generating various types of powder streams by using different nozzle geometries at the powder outlet area. It is necessary to understand the proper powder flow behavior to determine the displacement of the powder between the nozzle and deposition point. This will help in getting the value for the distance of maximum concentration point of the merging streams

This paper proposes a novel method to predict the radar detection errors in a complex environment by considering slant range and angular error. This method involves analysis of the electromagnetic wave propagation path loss in troposphere using discrete mixed Fourier transform based split–step parabolic equation. For analysis, modeling of the terrain profile using a digital elevation model and estimating the refractive index by interpolating upper–air observations is conducted. The direction of refracted antenna’s main beam is predicted using the path loss, and by comparing it with the antenna boresight, the radar errors are computed according to the received timing of the radar echo. To apply the proposed method to real scenarios, the atmospheric environment within the analysis area is modeled using meteorological data collected on a specific day. By applying the proposed method to an arbitrary date and location, the radar errors in a long–range propagation environment where refraction occurs are analyzed.

The reliability of star sensor in a harsh environment has recently become a research hotspot. In some harsh environment such as plume interference, a large number of false stars can be observed, leading to failure in star identification. In this paper, we propose a false star filtering algorithm, which can be used as a preprocessing algorithm for any existing star identification algorithm. By utilizing the difference between the motion of false stars and true stars, the algorithm performs angular distance tracking and star voting on multiple consecutive frames of star images and achieves false star filtering. The software simulation results show that for the star images containing more than 700 false stars, the algorithm is able to find out all true stars in less than 10 frames, and the success rate of the algorithm remains high when the star sensor rotates at up to 1°/s. The algorithm is also implemented on an existing star senor and evaluated with star images generated by a dynamic star simulator. The experimental results indicate that with the help of the proposed algorithm, the robustness of a normal star identification algorithm can be significantly improved.

Disturbance observer based reliable H∞ fuzzy attitude tracking control for Mars entry vehicles with actuator failures

We investigate the value of greenways by studying the impact of accessibility to Memphis Shelby Farms Greenline on surrounding housing values by looking at access measured by the cognitive distance (or straight-line distance, hereafter cognitive distance), a measure of the perceived financial benefit of greenways and nearest actual travel distance, a measure of real benefit of greenways. We measure the benefit of greenways by showing that a 3-mile actual travel distance range to greenways gives optimum distance accessibility and produces a positive premium for nearby houses. However, the cognitive distance, a method commonly used by previous research, while not practical, has a higher value impact than the actual distance. Moreover, the cognitive benefit decreases at a faster rate than the real benefit as distance to greenways increases.

Online viewpoint optimization method for high-accuracy intraoperative optical measurement in orthopedic surgeries: An active navigation approach