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Robust aiding of inertial navigation systems in GNSS-denied environments is critical for the removal of accumulated navigation error caused by the drift and bias inherent in inertial sensors. One way to perform such an aiding uses matching of geophysical measurements, such as gravimetry, gravity gradiometry or magnetometry, with a known geo-referenced map. Although simple in concept, this map-matching procedure is challenging: The measurements themselves are noisy, their associated spatial location is uncertain, and the measurements may match multiple points within the map (i.e., non-unique solution). In this paper, we propose a probabilistic multiple-hypotheses tracker to solve the map-matching problem and allow robust inertial navigation aiding. Our approach addresses the problem both locally, via probabilistic data association, and temporally by incorporating the underlying platform kinematic constraints into the tracker. The estimated platform position from the output of map matching is then integrated into the navigation state using an unscented Kalman filter. Additionally, we present a statistical measure of local map information density -- the map feature variability -- and use it to weight the output covariance of the proposed algorithm. The effectiveness and robustness of the proposed algorithm are demonstrated using a navigation scenario involving gravitational map matching. [ABSTRACT FROM AUTHOR]

Information geometry enables a deeper understanding of the methods of statistical inference. In this paper, the problem of nonlinear parameter estimation is considered from a geometric viewpoint using a natural gradient descent on statistical manifolds. It is demonstrated that the nonlinear estimation for curved exponential families can be simply viewed as a deterministic optimization problem with respect to the structure of a statistical manifold. In this way, information geometry offers an elegant geometric interpretation for the solution to the estimator, as well as the convergence of the gradient-based methods. The theory is illustrated via the analysis of a distributed mote network localization problem where the Radio Interferometric Positioning System (RIPS) measurements are used for free mote location estimation. The analysis results demonstrate the advanced computational philosophy of the presented methodology. [ABSTRACT FROM AUTHOR]

In this paper, the dynamic characteristics of ultra-high speed grinding spindle system is analyzed by using digital analysis methods. The spindle system is the key components of the machine tool, and its performance directly determines the machining stability and accuracy. Liquid hybrid bearing, with its superior performance has been widely applied to high speed and heavy machine tools. In order to study the spindle system, Fluent software was used to analyze the dynamic characteristics of liquid hybrid bearing. With the increase of the rotational speed, the capacity of liquid hybrid bearing changes significantly, and the relationship between the capacity and rotational is linear in intermediate stage. At the same time, the stiffness and damping of the liquid hybrid bearing has a corresponding increase with the increase of rotational speed. When the rotational speed increase to a certain extent, the dynamic performance of the spindle system will improve. Finally, the concentrated mass method and the finite element analysis method were used to analyze the dynamic characteristics of the spindle system, respectively. The results obtained by the both methods have good consistency, and the critical speed is about 14000 rve/min. [ABSTRACT FROM AUTHOR]

Because negative pressure can stimulate vascular proliferation, improve blood circulation and promote osteogenic differentiation of bone marrow stromal cells, we investigated the therapeutic effect of negative pressure on femoral head necrosis (FHN) in a rabbit model. Animals were divided into four groups (n = 60/group): [1] model control, [2] core decompression, [3] negative pressure and [4] normal control groups. Histological investigation revealed that at 4 and 8 weeks postoperatively, improvements were observed in trabecular bone shape, empty lacunae and numbers of bone marrow hematopoietic cells and fat cells in the negative pressure group compared to the core decompression group. At week 8, there were no significant differences between the negative pressure and normal control groups. Immunohistochemistry staining revealed higher expression of vascular endothelial growth factor (VEGF) and bone morphogenetic protein-2 (BMP-2) in the femoral heads in the negative pressure group compared with the core decompression group. Transmission electron microscopy revealed that cell organelles were further developed in the negative pressure group compared with the core decompression group. Microvascular ink staining revealed an increased number of bone marrow ink-stained blood vessels, a thicker vascular lumen and increased microvascular density in the negative pressure group relative to the core decompression group. Real-time polymerase chain reaction revealed that expression levels of both VEGF and BMP-2 were higher in the negative pressure group compared with the core decompression group. In summary, negative pressure has a therapeutic effect on FHN. This effect is superior to core decompression, indicating that negative pressure is a potentially valuable method for treating early FHN. [ABSTRACT FROM AUTHOR]

Inverse synthetic aperture radar (ISAR) imaging is conventionally addressed by means of fast Fourier transform (FFT) method. In this paper, a frame processing method derived based on frame theory for ISAR imaging is proposed as an alternative to FFT based approaches. In this method, a finite set of K consecutive target returns after a front-end processing by radar receiver are treated as a vector in a Hilbert space where a frame structure is defined. A frame and its dual are constructed as the functions of waveform and processing parameters and the target rotation rate. The latter can be estimated online. For certain classes of high resolution radar (HRR) waveforms, we show that it is possible to use standard methods of frame analysis and synthesis to reconstruct the ISAR image from radar returns. This method is analogous to those of reflective tomography, and does not suffer from the “range walk” problem exhibited by the FFT based approaches which poses constraints on the feasible aperture size for observing a target. Both simulated and experimental results are presented to demonstrate the effectiveness of the proposed method. Practical implementation and issues of the proposed frame processing technique are also discussed. [ABSTRACT FROM PUBLISHER]

In many radar and sonar tracking systems, the target state typically includes target position and velocity components that are estimated from a time sequence of target position and Doppler measurements. The use of measured Doppler information directly in the target trajectory estimation leads to a nonlinear filter implementation such as the extended Kalman filter (EKF), particle filter etc. We investigate a method for including Doppler measurements as part of the data association process and then assess the benefits of this approach. It is well understood that data association performance can dominate the total performance of a tracker that is designed to track targets in the presence of clutter. In this case, the Doppler component of the measurements may be used in combination with the target position measurement as an additional discriminant of measurement origin. We have developed a simple but efficient Doppler data association (DDA) method which utilises both position and Doppler measurements for single and multi-target tracking. If the Doppler measurements are not used in trajectory state estimation, then the nonlinear filters for the incorporation of Doppler measurements are not required, however a significant improvement in tracking performance is stilt observed. The proposed DDA method is demonstrated using both the linear multi-target integrated probabilistic data association algorithm (LMIPDA) and the linear multi-target integrated track splitting algorithm (LMITS) in an active sonar underwater multi-target tracking scenario. [ABSTRACT FROM AUTHOR]

Compared to conventional approaches, the integration of population size analysis with habitat suitability assessment on a large scale can provide more evidence to explain the mechanisms of habitat isolation and fragmentation, and thus make regional conservation plans. In this paper, we analyzed the habitat suitability for giant pandas in the Minshan Mountains, China, using the ecological-niche factor analysis (ENFA) method, and then evaluated the current conservation status of this endangered species. The results showed that (1) giant pandas were distributed in a narrow altitudinal range in which vegetation cover was dominated by coniferous forest, mixed coniferous and deciduous broadleaf forest, and deciduous broadleaf forest with scattered bamboo understory, and (2) roads and human settlements had strong negative effects on the panda habitat selection. According to habitat analysis, the total habitat area of giant panda in the Minshan Mountains was 953,173 ha, which was fragmented into 12 habitat units by major roads, rivers, and human settlements. The habitat of the mid-Minshan was less fragmentized, but was seriously fragmented in the north. The panda population size estimation showed that 676 individuals inhabited the study area, and 94.53% of them were in the mid-Minshan, but small panda populations less than 30 individuals inhabited the isolated and fragmented habitat patches in the north. The nature reserves in the Minshan Mountains have formed three conservation groups, which covered 41.26% of panda habitat and protected 70.71% of panda population of the study area, but there still exists two conservation gaps, and the connectivity among these reserves is still weak. Due to habitat isolation and extensive human disturbances, giant pandas in the north (i.e., Diebu, Zhouqu, and Wudou) are facing threats of local extinction. In order to protect pandas and their habitats in this area, some effective conservation approaches, such as establishing new reserves in gap areas, creating corridors among patches, and seasonally controlling traffic flux in key roads, should be implemented in the future to link these isolated habitats together. [ABSTRACT FROM AUTHOR]

When detecting a target using radar in the presence of multipath fading, the signal to noise ratio (SNR) at receiver can be dramatically reduced at certain ranges due to multipath signal cancellation, which leads to a significant drop of the detection probability at those ranges. The detection of a low elevation sea-surface target (LESST) is one example. For target tracking, this special case contrasts to the usual situation where we may often assume a constant probability of detection. Automatic target tracking requires a measure of track quality to discriminate between true and false tracks. This track quality measure should take both the existence of target and the SNR of the sensor into account, and be consistent with tracking performance. This paper investigates automatic target tracking using the integrated probabilistic data association (IPDA) type filters in the presence of multipath fading. Several existing single-scan probabilistic data association (PDA)-based filters, which calculate the probabilistic measure of track quality while estimating the track state, are evaluated in the scenario of tracking a nonmaneuvering LESST in the presence of both multipath fading and clutter. In addition, a variable Markov chain (VMC) IPDA tracker (IPDA-VMC), which has joint merits of both IPDA and the IPDA with a three state Markov chain (IPDA-M2), has been designed and evaluated here. In the derivation of track quality measure of both IPDA-M2 and IPDA-VMC, the possibility that a target is present but is temporarily unable to be detected has been explicitly considered. A tracker using this track quality measure for false track discrimination has significantly improved LESST tracking performance. [ABSTRACT FROM AUTHOR]

The variable structure multiple model (VSMM) approach to the maneuvering target tracking problem is considered. A new VSMM design, the minimal submodel-set switching (MSMSS) algorithm for tracking a maneuvering target is presented. The MSMSS algorithm adaptively determines the minimal set of models from the total model set and uses this to perform multiple model (MM) estimation. In addition, an iterative MSMSS algorithm with improved maneuver detection and termination properties is developed. Simulation results demonstrate that, compared with a standard interacting MM (IMM), the proposed algorithms require significantly lower computation while maintaining similar tracking performance. Alternatively, for a computational load similar to IMM, the new algorithms display significantly improved performance. [ABSTRACT FROM AUTHOR]

A method is proposed for estimating the target rotation rate for ISAR imaging based on the frame processing technique. The method utilises the intrinsic structures of the frame which span the Hilbert space of radar return from which the rotational information of the target is extracted. For a prominent point, the intensity of the projection is maximum when the frame component corresponding to the prominent point is built with the target rotation rate. [ABSTRACT FROM AUTHOR]

Single Image Super-resolution (SISR) produces high-resolution images with fine spatial resolutions from a remotely sensed image with low spatial resolution. Recently, deep learning and generative adversarial networks (GANs) have made breakthroughs for the challenging task of single image super-resolution (SISR). However, the generated image still suffers from undesirable artifacts such as the absence of texture-feature representation and high-frequency information. We propose a frequency domain-based spatio-temporal remote sensing single image super-resolution technique to reconstruct the HR image combined with generative adversarial networks (GANs) on various frequency bands (TWIST-GAN). We have introduced a new method incorporating Wavelet Transform (WT) characteristics and transferred generative adversarial network. The LR image has been split into various frequency bands by using the WT, whereas the transfer generative adversarial network predicts high-frequency components via a proposed architecture. Finally, the inverse transfer of wavelets produces a reconstructed image with super-resolution. The model is first trained on an external DIV2 K dataset and validated with the UC Merced Landsat remote sensing dataset and Set14 with each image size of 256 × 256. Following that, transferred GANs are used to process spatio-temporal remote sensing images in order to minimize computation cost differences and improve texture information. The findings are compared qualitatively and qualitatively with the current state-of-art approaches. In addition, we saved about 43% of the GPU memory during training and accelerated the execution of our simplified version by eliminating batch normalization layers. [ABSTRACT FROM AUTHOR]

This paper considers the two-dimensional (2D) anchorless localization problem for sensor networks in global positioning system (GPS)-denied environments. We present an efficient method, based on the multidimensional scaling (MDS) algorithm, in order to estimate the positions of the nodes in the network using measurements of the inter-node distances. The proposed method takes advantage of the mobility of the nodes to address the location ambiguity problem, i.e., rotation and flip ambiguity, which arises in the anchorless MDS algorithm. Knowledge of the displacement of the moving node is used to produce an analytical solution for the noise-free case. Subsequently, a least squares estimator is presented for the noisy scenario and the associated closed-form solution derived. The simulations show that the proposed algorithm accurately and efficiently estimates the locations of nodes, outperforming alternative methods. [ABSTRACT FROM AUTHOR]

Recovering a distance from ambiguous phase measurements observed at multiple signal frequencies involves the estimation of both integer and scalar quantities simultaneously. The problem is optimally addressed by existing methods only when the measurement noise is IID. A noise 'whitening' procedure is derived with a least-square estimation structure inherent to the Lattice method proposed previously which thus extends this Lattice-based algorithm to cope with coloured phase measurement noise in an optimal manner demonstrated via simulation. [ABSTRACT FROM AUTHOR]

Background: Psoroptes cuniculi can parasitise the ear canal of the rabbit, and cause the afflicted animals to cease feeding and become severely debilitated, sometimes resulting in death. In this study, we examined the oxidative status and inflammatory level of the peripheral blood of rabbits infested with Psoroptes cuniculi and investigated the pathogenesis of this disease. Methods: A total of 24 rabbits were divided into a healthy rabbit group and two infested rabbit groups. After weighing the rabbits, approximately 5 ml of blood was obtained from each animal. Then, the blood serum was extracted and used to assess the levels of antioxidant enzymes and inflammatory factors. Results: Compared to the healthy rabbits, the activities of catalase and glutathione-S-transferase and the level of malonyldialdehyde were increased, but the activity of superoxide dismutase was reduced in the infested rabbits. At the same time, a variety of inflammatory cells were activated, and the levels of inflammatory factors such as prostaglandin E2, interleukin-6, interleukin-8 and transforming growth factor-β1 were increased in peripheral blood. Conclusion: Animal acariasis was associated with immunosuppressive disorders and inflammatory reaction. These results advance our understanding of the pathogenesis of Psoroptes cuniculi infestation in rabbits and can help guide the effectual treatment of this disease in clinics. [ABSTRACT FROM AUTHOR]