scholarly journals Unscented Particle Smoother and Its Application to Transfer Alignment of Airborne Distributed POS

2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Xiaolin Gong ◽  
Xiaorui Zheng ◽  
Xing-Gang Yan ◽  
Zhaoxing Lu

This paper deals with the problem of state estimation for the transfer alignment of airborne distributed position and orientation system (distributed POS). For a nonlinear system, especially with large initial attitude errors, the performance of linear estimation methods will degrade. In this paper, a nonlinear smoothing algorithm called the unscented particle smoother (UPS) is proposed and utilized in the off-line transfer alignment of airborne distributed POS. In this algorithm, the measurements are first processed by the forward unscented particle filter (UPF) and then a backward smoother is used to achieve the improved solution. The performance of this algorithm is compared with that of a similar smoother known as the unscented Rauch-Tung-Striebel smoother. The simulation results show that the UPS effectively improves the estimation accuracy and this work offers a new off-line transfer alignment approach of distributed POS for multiantenna synthetic aperture radar and other airborne earth observation tasks.

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2120
Author(s):  
Wen Ye ◽  
Bin Gu ◽  
Yun Wang

With the demand for high resolution remote sensing, load array technology has gradually become an effective measure to improve imaging resolution. However, the external flow and internal engine vibration disturbance may lead to the flexible deformation of wings. The traditional rigid baseline error compensation method cannot solve the problem of serious coupling movement error caused by flexible deformation. To address the problem, a transfer alignment model based on fiber Bragg grating for distributed position and orientation system is proposed in this paper. Firstly, based on the multidimensional requirements of flexible deformation information, the layout scheme of fiber Bragg grating was designed, then the continuous strain in the wing surface was obtained after the quadratic fitting of strain measured by fiber Bragg gratings, and the deformation displacement and angle are calculated. Thirdly, flexible deformation compensation for distributed position and orientation system based on fiber Bragg grating was studied. The state equation including position error, velocity error, misalignment angle, and inertial device error was established. The position and attitude information compensated by the flexible lever arm was used as the quantitative measurement. The filtering estimation improved the measurement accuracy of the slave inertial navigation systems. At last, the experiment was carried out and showed that the accuracy of the transfer alignment has been improved significantly.


Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2459 ◽  
Author(s):  
Xing Peng ◽  
Xinwu Li ◽  
Changcheng Wang ◽  
Haiqiang Fu ◽  
Yanan Du

Synthetic aperture radar tomography (TomoSAR) is an important way of obtaining underlying topography and forest height for long-wavelength datasets such as L-band and P-band radar. It is usual to apply nonparametric spectral estimation methods with a large number of snapshots over forest areas. The nonparametric iterative adaptive approach for amplitude and phase estimation (IAA-APES) can obtain a high resolution; however, it only tends to work well with a small number of snapshots. To overcome this problem, this paper proposes the nonparametric iterative adaptive approach based on maximum likelihood estimation (IAA-ML) for the application over forest areas. IAA-ML can be directly used in forest areas, without any prior information or preprocessing. Moreover, it can work well in the case of a large number of snapshots. In addition, it mainly focuses on the backscattered power around the phase centers, helping to detect their locations. The proposed IAA-ML estimator was tested in simulated experiments and the results confirmed that IAA-ML obtains a higher resolution than IAA-APES. Moreover, six P-band fully polarimetric airborne SAR images were applied to acquire the structural parameters of a forest area. It was found that the results of the HH polarization are suitable for analyzing the ground contribution and the results of the HV polarization are beneficial when studying the canopy contribution. Based on this, the underlying topography and forest height of a test site in Paracou, French Guiana, were estimated. With respect to the Light Detection and Ranging (LiDAR) measurements, the standard deviation of the estimations of the IAA-ML TomoSAR method was 2.11 m for the underlying topography and 2.80 m for the forest height. Furthermore, compared to IAA-APES, IAA-ML obtained a higher resolution and a higher estimation accuracy. In addition, the estimation accuracy of IAA-ML was also slightly higher than that of the SKP-beamforming technique in this case study.


Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Lijun Song ◽  
Zhongxing Duan ◽  
Bo He ◽  
Zhe Li

The centralized Kalman filter is always applied in the velocity and attitude matching of Transfer Alignment (TA). But the centralized Kalman has many disadvantages, such as large amount of calculation, poor real-time performance, and low reliability. In the paper, the federal Kalman filter (FKF) based on neural networks is used in the velocity and attitude matching of TA, the Kalman filter is adjusted by the neural networks in the two subfilters, the federal filter is used to fuse the information of the two subfilters, and the global suboptimal state estimation is obtained. The result of simulation shows that the federal Kalman filter based on neural networks is better in estimating the initial attitude misalignment angle of inertial navigation system (INS) when the system dynamic model and noise statistics characteristics of inertial navigation system are unclear, and the estimation error is smaller and the accuracy is higher.


2019 ◽  
Vol 11 (14) ◽  
pp. 1668 ◽  
Author(s):  
Indrajit Kurmi ◽  
David C. Schedl ◽  
Oliver Bimber

We apply a multi-spectral (RGB and thermal) camera drone for synthetic aperture imaging to computationally remove occluding vegetation for revealing hidden objects, as required in archeology, search-and-rescue, animal inspection, and border control applications. The radiated heat signal of strongly occluded targets, such as a human bodies hidden in dense shrub, can be made visible by integrating multiple thermal recordings from slightly different perspectives, while being entirely invisible in RGB recordings or unidentifiable in single thermal images. We collect bits of heat radiation through the occluder volume over a wide synthetic aperture range and computationally combine them to a clear image. This requires precise estimation of the drone’s position and orientation for each capturing pose, which is supported by applying computer vision algorithms on the high resolution RGB images.


2020 ◽  
Vol 12 (18) ◽  
pp. 2925
Author(s):  
Thomas Miraglio ◽  
Karine Adeline ◽  
Margarita Huesca ◽  
Susan Ustin ◽  
Xavier Briottet

Gap Fraction, leaf pigment contents (content of chlorophylls a and b (Cab) and carotenoids content (Car)), Leaf Mass per Area (LMA), and Equivalent Water Thickness (EWT) are considered relevant indicators of forests’ health status, influencing many biological and physical processes. Various methods exist to estimate these variables, often relying on the extensive use of Radiation Transfer Models (RTMs). While 3D RTMs are more realistic to model open canopies, their complexity leads to important computation times that limit the number of simulations that can be considered; 1D RTMs, although less realistic, are also less computationally expensive. We investigated the possibility to approximate the outputs of a 3D RTM (DART) from a 1D RTM (PROSAIL) to generate in very short time numerous extensive Look-Up Tables (LUTs). The intrinsic error of the approximation model was evaluated through comparison with DART reference values. The model was then used to generate LUTs used to estimate Gap Fraction, Cab, Car, EWT, and LMA of Blue Oak-dominant stands in a woodland savanna from AVIRIS-C data. Performances of the approximation model for estimation purposes compared to DART were evaluated using Wilmott’s index of agreement (dr), and estimation accuracy was measured with coefficients of determination (R2) and Root Mean Squared Error (RMSE). The low approximation error of the proposed model demonstrated that the model could be considered for canopy covers as low as 30%. Gap Fraction estimations presented similar performances with either DART or the approximation (dr 0.78 and 0.77, respectively), while Cab and Car showed improved performances (dr increasing from 0.65 to 0.77 and 0.34 to 0.65, respectively). No satisfying estimation methods were found for LMA and EWT using either models, probably due to the high sensitivity of the scene’s reflectance to Gap Fraction and soil modeling at such low LAI. Overall, estimations using the approximated reflectances presented either similar or improved accuracy. Our findings show that it is possible to approximate DART reflectances from PROSAIL using a minimal number of DART outputs for calibration purposes, drastically reducing computation times to generate reflectance databases: 300,000 entries could be generated in 1.5 h, compared to the 12,666 total CPU hours necessary to generate the 21,840 calibration entries with DART.


2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Y. Zhang ◽  
B. P. Wang ◽  
Y. Fang ◽  
Z. X. Song

The existing sparse imaging observation error estimation methods are to usually estimate the error of each observation position by substituting the error parameters into the iterative reconstruction process, which has a huge calculation cost. In this paper, by analysing the relationship between imaging results of single-observation sampling data and error parameters, a SAR observation error estimation method based on maximum relative projection matching is proposed. First, the method estimates the precise position parameters of the reference position by the sparse reconstruction method of joint error parameters. Second, a relative error estimation model is constructed based on the maximum correlation of base-space projection. Finally, the accurate error parameters are estimated by the Broyden–Fletcher–Goldfarb–Shanno method. Simulation and measured data of microwave anechoic chambers show that, compared to the existing methods, the proposed method has higher estimation accuracy, lower noise sensitivity, and higher computational efficiency.


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