Resolution Properties Research Articles

Financion: A quantum approach to financial market modelling

In this paper we will show that, in the financial market, the concept of chaos is removed and replaced with that of complexity. Indeed, it has always shown the characteristics of a dynamical system with high complexity. In particular, we present a new theory to describe the financial phenomenologies: a theory that is able to combine the different founding aspects of the financial market. This study provides a useful answer to the fundamental aspects of the market: its multiscale or multi resolution properties; its quantum-relativistic properties; the usefulness of describing, at a fundamental level, the interactions between bullish o + and bearish o – financial operators; the usefulness of declining financial interactions between operators in terms of scattering, annihilation or creation of elementary liquidity constituents; the introduction of a specific field, named financial field, and its gauge boson, the so-called “financion” as a mediator of financial interactions; the possibility of describing the financial market as a dynamical system, capable of absorbing financions and that is quanta of interaction liquidity. Thanks to this new paradigm, we have been able to describe and define the market scale length, i.e. revenue, price, risk (λ, ρ, σ). Furthermore, we have analyzed Kahneman and Tversky’s Prospect Theory to describe the decision-making process under risk conditions, not considering the price as a function of time, p = p(t), but by carrying out the analysis of price dynamics as trajectories in the transformed energy-entropy space ES of the financial market , where there are more stable and reliable indications than common indicators.

Automated segmentation of macular edema for the diagnosis of ocular disease using deep learning method

Macular edema is considered as a major cause of visual loss and blindness in patients with ocular fundus diseases. Optical coherence tomography (OCT) is a non-invasive imaging technique, which has been widely applied for diagnosing macular edema due to its non-invasive and high resolution properties. However, the practical applications remain challenges due to the distorted retinal morphology and blurred boundaries near macular edema. Herein, we developed a novel deep learning model for the segmentation of macular edema in OCT images based on DeepLab framework (OCT-DeepLab). In this model, we used atrous spatial pyramid pooling (ASPP) to detect macular edema at multiple features and used the fully connected conditional random field (CRF) to refine the boundary of macular edema. OCT-DeepLab model was compared against the traditional hand-crafted methods (C-V and SBG) and the end-to-end methods (FCN, PSPnet, and U-net) to estimate the segmentation performance. OCT-DeepLab showed great advantage over the hand-crafted methods (C-V and SBG) and end-to-end methods (FCN, PSPnet, and U-net) as shown by higher precision, sensitivity, specificity, and F1-score. The segmentation performance of OCT-DeepLab was comparable to that of manual label, with an average area under the curve (AUC) of 0.963, which was superior to other end-to-end methods (FCN, PSPnet, and U-net). Collectively, OCT-DeepLab model is suitable for the segmentation of macular edema and assist ophthalmologists in the management of ocular disease.

Clinical application of 3D virtual and printed models for cerebrovascular diseases

ObjectiveThree-dimensional (3D) printing techniques are rapidly advancing in the medical industry and in clinical practice. We aimed to evaluate the usefulness of 3D virtual and printed models of 6 representative cerebrovascular diseases using the software we developed. MethodsSix cases consisted of 4 intracranial aneurysms (IAs) including complex ones with intrasaccular thrombosis, large size and a skull base location; 1 cavernous malformation in the pons; and 1 arteriovenous malformation in the parietal lobe. The 3D modeling process was performed retrospectively in 3 cases and prospectively in 1 IA. Segmentation of raw data and rendering and modification for 3D virtual models were processed mostly automatically. ResultsMost intracranial structures were satisfactorily made, including the skull, brain, vessels, thrombus, tentorium and major cranial nerves. Based on 3D modeling, surgical plan was changed in 1 prospective IA case. However, it was still difficult to discriminate small vessels and cranial nerves, to feel a realistic tactile sense and to directly perform presurgical simulations, such as dissection, removal, clipping and microanastomosis. ConclusionsThe 3D modeling was thought to be very helpful in experiencing the operative views from various directions in advance, in selecting an appropriate surgical approach, and in educating physicians and patients. With advancements in radiological resolution, processing techniques and material properties, 3D modeling is expected to simulate real brain tissues more closely.

Medical display application for degraded image sharpness restoration based on the modulation transfer function: initial assessment for a five-megapixel mammography display monitor.

An image-display application for medical liquid-crystal display (LCD) monitors called the sharpness recovery (SR) function has been developed to compensate for image sharpness as a function of deficiencies in the modulation transfer function (MTF) of a monitor. We investigated the effects of the SR function for a five-megapixel (MP) mammography LCD monitor on the resolution and noise properties of the displayed images by measuring the MTF and overall noise power spectrum (NPS), respectively. Furthermore, the effectiveness of the SR function for the 5-MP monitor in displaying subtle microcalcifications on digital mammograms was verified using a two-alternative-forced-choice sensitivity measurement as an initial application for medical image interpretation. Four radiologists compared the visibility of 45 regions of interest with a malignant microcalcification cluster shown on SR-processed and unprocessed mammograms. SR processing improved the MTF of the displayed images by approximately 40% at the Nyquist frequency of the 5-MP monitor, whereas it slightly increased the overall NPS values. All observers indicated that the fraction of cases considered to have better visibility of microcalcifications with the SR processing was significantly greater than that without the processing (averaging 82%, with the 95% confidence interval ranging from 70 to 93%). The SR processing for the 5-MP monitor yielded a significant improvement in the resolution properties of the displayed images, with a certain increase in the image noise. The SR function has the potential to improve the observer performance of radiologists, particularly when reading subtle microcalcifications reproduced on 5-MP monitors.

Distribution-based imaging for multiple sclerosis lesion segmentation using specialized fuzzy 2-means powered by Nakagami transmutations

Distribution-based imaging is a promising methodology mainly to differentiate suspicious regions from surrounding tissues by applying a distribution to the images vertically or horizontally, ideally in both directions. The methodology is very useful for contouring and highlighting desired regions even under near-zero contrast conditions; it also leads to flexible segmentation of the lesions by parametric kernels and provides robust results when supported by solid post-segmentation protocols. Given these benefits, what we propose in this research is a specialized fuzzy 2-means algorithm enhanced by parametric distribution-based imaging framework to offer novel solutions for multiple-sclerosis (MS) identification and segmentation from flair MRI images. The interchangeable distributions employed in this research are Rayleigh, Weibull, Gamma, Exponential and Chi-square, which all are mathematically transmuted from Nakagami distribution. The Nakagami m-parameter is defining the shape of the distributions unless a special parameter exists; while the highlighted areas are segmented by fuzzy 2-means. All parameters are optimized using a set of MICCAI 2016 MS lesion segmentation challenge taken by Siemens Verio 3T scanner and 0.8245 dice score is achieved by Nakagami-Gamma. However, when the optimized framework is tested by other 4 sets with same resolution and size properties, the highest average dice score 0.7113 is obtained by Nakagami–Rayleigh; while Nakagami-Gamma transmutation is resulted in 0.7112 dice score with significantly better sensitivity.

Development and Evaluation of the Fourier Spectral Distortion Metric.

A spatial resolution metric is presented for tomosynthesis. The Fourier spectral distortion metric (FSD) was developed to evaluate specific resolution properties of different imaging techniques for digital tomosynthesis using a star pattern image to plot modulation in the frequency domain. The FSD samples the spatial resolution of a star-pattern image tangentially over an acute angle and for a range of spatial frequencies in a 2D image or 3D image reconstruction slice. The FSD graph portrays all frequencies present in a star pattern quadrant. In addition to the fundamental input frequency of the star pattern, the FSD graph shows spectral leakage, square wave harmonics, and residual noise. The contrast transfer function (CTF) is obtained using the FSD graph. The CTF is analogous to the modulation transfer function (MTF), but it is not normalized to unity at zero spatial frequency. Unlike the MTF, this metric separates the fundamental input-frequency from the other signals in the Fourier domain. This metric helps determine optimal image reconstruction parameters, the in-plane limit of spatial resolution with respect to aliased signals, and a threshold criterion for an image to support super resolution and reduce aliasing artifacts. Various sampling parameters were evaluated to optimize this metric and ascertain measurement accuracy. The FSD adequately compares resolution properties of 2D images and 3D image reconstruction slices for various x ray imaging modes without suppressing aliased signals.

The resolution property via Azumaya algebras

Abstract Using formal-local methods, we prove that a separated and normal tame Artin surface has the resolution property. By proving that normal tame Artin stacks can be rigidified, we ultimately reduce our analysis to establishing the existence of Azumaya algebras. Our construction passes through the case of tame Artin gerbes, tame Artin curves, and algebraic space surfaces, each of which we establish independently.

Terahertz radiation enhancement based on LT-GaAs by optimized plasmonic nanostructure

Terahertz waves have unique penetrability, safety, spectral resolution, and many other useful properties. Therefore, terahertz waves are widely employed in medical treatment, detection, imaging, security, and spectroscopy. They also possess great development prospects in photoconductive antennas (PCAs) for telecommunication and other devices. However, terahertz PCAs are still limited by the weak photocurrents and small light absorption rates, making the development of PCAs with improved conversion efficiencies highly desirable. In this study, a new type of plasmonic PCAs with high photoelectric conversion efficiency was proposed. The interaction between the 800 nm fs laser and staggered nanorods was studied by the finite difference time domain (FDTD) method. The geometric parameters of the nanorods arrays were optimized. Compared to conventional photoconductive antennas without nanostructures, the PCAs with optimized nano-plasmonic structure I showed threefold higher electric field radiations under 800 nm fs laser irradiation. Optimized plasmonic nanostructure II increases photocurrent up to 335.553 nA that indicates 10 times enhancement in comparison with conventional structure. Hence, the developed structures greatly enhanced the light absorption of the photosensitive layer based on low-temperature-grown gallium arsenide. In sum, the proposed staggered hexagonal plasmonic structures (structure II) could effectively improve the light-terahertz conversion efficiencies of PCAs.

Non-linear Memristive Synaptic Dynamics for Efficient Unsupervised Learning in Spiking Neural Networks.

Spiking neural networks (SNNs) are a computational tool in which the information is coded into spikes, as in some parts of the brain, differently from conventional neural networks (NNs) that compute over real-numbers. Therefore, SNNs can implement intelligent information extraction in real-time at the edge of data acquisition and correspond to a complementary solution to conventional NNs working for cloud-computing. Both NN classes face hardware constraints due to limited computing parallelism and separation of logic and memory. Emerging memory devices, like resistive switching memories, phase change memories, or memristive devices in general are strong candidates to remove these hurdles for NN applications. The well-established training procedures of conventional NNs helped in defining the desiderata for memristive device dynamics implementing synaptic units. The generally agreed requirements are a linear evolution of memristive conductance upon stimulation with train of identical pulses and a symmetric conductance change for conductance increase and decrease. Conversely, little work has been done to understand the main properties of memristive devices supporting efficient SNN operation. The reason lies in the lack of a background theory for their training. As a consequence, requirements for NNs have been taken as a reference to develop memristive devices for SNNs. In the present work, we show that, for efficient CMOS/memristive SNNs, the requirements for synaptic memristive dynamics are very different from the needs of a conventional NN. System-level simulations of a SNN trained to classify hand-written digit images through a spike timing dependent plasticity protocol are performed considering various linear and non-linear plausible synaptic memristive dynamics. We consider memristive dynamics bounded by artificial hard conductance values and limited by the natural dynamics evolution toward asymptotic values (soft-boundaries). We quantitatively analyze the impact of resolution and non-linearity properties of the synapses on the network training and classification performance. Finally, we demonstrate that the non-linear synapses with hard boundary values enable higher classification performance and realize the best trade-off between classification accuracy and required training time. With reference to the obtained results, we discuss how memristive devices with non-linear dynamics constitute a technologically convenient solution for the development of on-line SNN training.

Development and Evaluation of 3D-Printed Cardiovascular Phantoms for Interventional Planning and Training.

Catheter-based interventions are standard treatment options for cardiovascular pathologies. Therefore, patient-specific models could help training physicians' wire-skills as well as improving planning of interventional procedures. The aim of this study was to develop a manufacturing process of patient-specific 3D-printed models for cardiovascular interventions. To create a 3D-printed elastic phantom, different 3D-printing materials were compared to porcine biological tissues (i.e., aortic tissue) in terms of mechanical characteristics. A fitting material was selected based on comparative tensile tests and specific material thicknesses were defined. Anonymized contrast-enhanced CT-datasets were collected retrospectively. Patient-specific volumetric models were extracted from these datasets and subsequently 3D-printed. A pulsatile flow loop was constructed to simulate the intraluminal blood flow during interventions. Models' suitability for clinical imaging was assessed by x-ray imaging, CT, 4D-MRI and (Doppler) ultrasonography. Contrast medium was used to enhance visibility in x-ray-based imaging. Different catheterization techniques were applied to evaluate the 3D-printed phantoms in physicians' training as well as for pre-interventional therapy planning. Printed models showed a high printing resolution (~30 µm) and mechanical properties of the chosen material were comparable to physiological biomechanics. Physical and digital models showed high anatomical accuracy when compared to the underlying radiological dataset. Printed models were suitable for ultrasonic imaging as well as standard x-rays. Doppler ultrasonography and 4D-MRI displayed flow patterns and landmark characteristics (i.e., turbulence, wall shear stress) matching native data. In a catheter-based laboratory setting, patient-specific phantoms were easy to catheterize. Therapy planning and training of interventional procedures on challenging anatomies (e.g., congenital heart disease (CHD)) was possible. Flexible patient-specific cardiovascular phantoms were 3D-printed, and the application of common clinical imaging techniques was possible. This new process is ideal as a training tool for catheter-based (electrophysiological) interventions and can be used in patient-specific therapy planning.

Discussion of different Remote sensing satellite possibilities for scientifical Earth observations

More than 1,000 satellites are launched into space, and they differ in their functions, rotation orbits, resolution, and other properties. Scientists divide the satellites into low-resolution, medium-resolution, high-resolution, and very high-resolution satellites by their properties. Now, the biggest challenge facing scientists is to use some of these different resolution images in their field. To get the expected result, it is very important to analyze the image that needs an which gives more accurate results. Therefore, the main attention of this article is aimed to find the answer to these problems. In this article 3 satellite images which have different resolution are analyzed. The possibility of middle-resolution images of MODIS, high-resolution images of Landsat, and very high-resolution images of WorldView-2 (WV-2) satellites using GIS are analyzed. A research area was the Syrdarya region, and downloaded different images of satellites of this area and compared with using e Cognition. According to the results, a more accurate satellite image for irrigation sets information is WorldView-2 images. In comparison analysis, it shows more accurate properties than other satellite images. As irrigation sets are small objects for the analysis, very high spatial resolution satellite images are important. Water discharge and surface change happen very fast; thus, it requires daily monitoring of the condition. And in this case, the temporal resolution of the MODIS and Landsat is 16 day, and it is a too long period.

Respiratory-correlated 4D digital tomosynthesis with deep convolutional neural networks for image-guided radiation therapy

4D digital tomosynthesis (DTS) techniques for image-guided radiation therapy (IGRT) are able to reduce radiation dose, scan and reconstruction time compared to 4D cone-beam computed tomography (CBCT). In spite of these benefits, the 4D DTS techniques cause the degradation of image quality due to an intrinsic imaging strategy and consequently reduce treatment accuracy. In this study, a deep learning-based convolutional neural network (CNN) framework was proposed for 4D DTS imaging. The proposed CNN framework consisted of the data restoration network based on a U-Net and the denoising network combined with a 2D wavelet transform, and the network training was implemented with clinical images. The quality of the 4D DTS images obtained from the proposed model was evaluated in terms of quantitative accuracy, spatial resolution and noise property. The results showed that the proposed CNN framework improved the quantitative accuracy of 4D DTS images by 3–19%, and the spatial resolution and noise for the proposed CNN framework were reduced by 2.24–7.33% and 8.92–40.07%, respectively, in comparison to other imaging models. These results represented that the degradation of the 4D DTS image quality can be recovered using the proposed CNN framework, and the proposed model is suitable for maintaining spatial resolution as well as suppressing noise and artifacts. In conclusion, the proposed CNN framework can be potentially used to improve the quality of 4D DTS images for the IGRT.

Comparison between Variance and Edge Methods for Measuring Image Resolution Properties in Digital Mammography

We studied the image resolution properties in digital mammography using the variance and edge response function methods. The associated measurements were made using a polymethylmethacrylate phantom of 0-40 mm thickness while maintaining the incident dose to the detector constant, as well as with and without using the phantom but by varying the level of exposure to the detector. The results obtained using the two methods were compared on the object plane of the mammography unit with compensation for the magnification and rejection of the scattered radiation. In this unit, the imaging process of resolution enhancement may be performed according to the phantom thickness, shape of test device, exposure level, and so on. The modulation transfer function values of the variance method were slightly higher than those of the edge method, approximately 6% at 2 cycles/mm and 8% at 4 cycles/mm without resolution enhancement processing. We concluded that the variance method is more suitable than the edge method for the constancy test or quality control because of its lower coefficient of variation.

Exploiting a texture framework and high spatial resolution properties of panchromatic images to generate enhanced multi-layer products: Examples of Pleiades and historical CORONA space photographs

ABSTRACT Remotely sensed high spatio-temporal resolution panchromatic images have been extensively used globally to visually detect and interpret changes in landscape components, create land cover maps via the on-screen manual digitization, and to pan-sharpen multi-spectral images among other uses. Despite this attractive array of uses, lack of distinct spectral signatures for panchromatic images from surface elements, e.g. landscape cover types, creates a drawback in their exploitation during any automated classification process, hence limiting their use in the field of remote sensing for land use/land cover change studies. Moreover, the complexities of some panchromatic data (e.g. CORONA) on the one hand, and the traditional texture computation approach on the other hand present additional hurdles in utilizing panchromatic images. This contribution looks at the possibility of exploiting panchromatic images (e.g. Pleiades and historical CORONA products) for remote sensing applications by (i) proposing a new approach that optimizes and generates new multi-layer datasets from panchromatic images that could be useful, e.g. in image classification analysis, (ii)exploiting the combinatorial texture approach to enhance the products generated by the framework in (i) above, and (iii) assessing the capability of the proposed method to handle complex datasets exemplified, e.g. by CORONA. To evaluate the approach, Kurdistan, Iran and Syria regions are selected for study employing the maximum likelihood classification (MLC) scheme. The MLC results indicate an increase in overall accuracy and Kappa coefficient by 32% and 0.42 (compared to raw CORONA image), and 21% and 0.28 (compared to raw Pleiades image). For Iran and Syria, compared to the raw CORONA image, the MLC results show increase by 35% and 0.47, and 42% and 0.56, respectively. Furthermore, based on the results of the accuracy assessment that show an overall accuracy of 85% and Kappa coefficient of 0.80 for Kurdistan, 94% and 0.92 for Iran, and 96% and 0.95 for Syria, the proposed method can be said to have the potential of handling complex panchromatic datasets such as CORONA.

Dinuclear Triple-Stranded Helicates Composed of Tetradentate Ligands with Aluminum(III) Chromophores: Optical Resolution and Multi-color Circularly Polarized Luminescence Properties.

New chiroptical chromophores, dinuclear triple-stranded helicates, composed of tetradentate ligands with aluminum(III) ions, are described. These are synthesized in two steps using inexpensive pyrrole derivatives, hydrazine, and aluminum chloride. These molecular architectures (ALPHY) show multi-color (cyan, yellow, and orange) photoluminescence in solution and in the solid-state, which depends on the substituents of the ligands. The photoluminescence quantum yields of helicates were up to 54 %. The right-handed (P) and left-handed (M) helicates are so stable that they do not undergo racemization in some solvents and are mirror images according to circular dichroism and circularly polarized luminescence (CPL) with an absolute luminescence dissymmetry factor (glum ) of up to the 10-3 order. Mixing the different helicates produces white-light emission with CPL characters. This study offers a glimpse into the potential applications of chromophores with diverse photophysical properties.

Representation of high resolution rock properties on a coarser grid for hydraulic fracture modeling

This study presents a homogenization procedure for the high resolution rock properties that are used as an input data for hydraulic fracture simulations. In particular, the high resolution data for fracture toughness (both vertical and horizontal), elastic constants, leak-off coefficient, and stress are suitably upscaled for a given element size, so that the small scale variations within this element are captured implicitly. Homogenization of the elastic properties is performed assuming that each individual layer is transversely isotropic and is based on Backus averaging. Fracture energy is used to homogenize the fracture toughness and to account for the contrast of elastic properties on the fracture height containment. The average energy is used in the horizontal direction and the maximum energy is used in the vertical direction. To preserve the total force acting on the element, the weighted average is used for stress homogenization. In addition, a correction to the vertical fracture toughness that is caused by the difference between the high resolution stress and the homogenized stress is introduced. Finally, a weighted average is used to homogenize leak-off. A series of numerical examples that consider synthetic data with periodic variation of parameters is used to validate the homogenization procedure. One of the most important implications of using the homogenization is an increase of the apparent vertical fracture toughness (that represents small scale property variations), which can be of the same order or even larger than the intrinsic rock toughness. Thus, high resolution property variations have a potential to considerably alter fracture geometry and can lead to a more pronounced fracture height containment.

High-Resolution Strain/Stress Measurements by Three-Axis Neutron Diffractometer.

Resolution properties of the unconventional high-resolution neutron diffraction three-axis setup for strain/stress measurements of large bulk polycrystalline samples are presented. Contrary to the conventional two-axis setups, in this case, the strain measurement on a sample situated on the second axis is carried out by rocking the bent perfect crystal (BPC) analyzer situated on the third axis of the diffractometer. Thus, the so-called rocking curve provides the sample diffraction profile. The neutron signal coming from the analyzer is registered by a point detector. This new setup provides a considerably higher resolution (at least by a factor of 5), which however, requires a much longer measurement time. The high-resolution neutron diffraction setting can be effectively used, namely, for bulk gauge volumes up to several cubic centimeters, and for plastic deformation studies on the basis of the analysis of diffraction line profiles, thus providing average values of microstructure characteristics over the irradiated gauge volume.

Technical evaluation of image quality in synthetic mammograms obtained from 15° and 40° digital breast tomosynthesis in a commercial system: a quantitative comparison.

Digital breast tomosynthesis (DBT) has recently gained interest both for breast cancer screening and diagnosis. Its employment has increased also in conjunction with digital mammography (DM), to improve cancer detection and reduce false positive recall rate. Synthetic mammograms (SMs) reconstructed from DBT data have been introduced to replace DM in the DBT + DM approach, for preserving the benefits of the dual-acquisition modality whilst reducing radiation dose and compression time. Therefore, different DBT models have been commercialized and the effective potential of each system has been investigated. In particular, wide-angle DBT was shown to provide better depth resolution than narrow-angle DBT, while narrow-angle DBT allows better identification of microcalcifications compared to wide-angle DBT. Given the increasing employment of SMs as supplement to DBT, a comparison of image quality between SMs obtained in narrow-angle and wide-angle DBT is of practical interest. Therefore, the aim of this phantom study was to evaluate and compare the image quality of SMs reconstructed from 15° (SM15) and 40° (SM40) DBT in a commercial system. Spatial resolution, noise and contrast properties were evaluated through the modulation transfer function (MTF), noise power spectrum, maps of signal-to-noise ratio (SNR), image contrast, contrast-to-noise ratio (CNR) and contrast-detail (CD) thresholds. SM40 expressed higher MTF than SM15, but also lower SNR and CNR levels. SM15 and SM40 were characterized by slight different texture, and a different behavior in terms of contrast was found. SM15 provided better CD performances than SM40. These results suggest that the employment of wide/narrow-angle DBT + SM images should be optimized based on the specific image task.

3D printed agar/ calcium alginate hydrogels with high shape fidelity and tailorable mechanical properties

In this study, calcium alginate/agar (CA/Ag) 3D structures were printed as strip assembles with high resolution and tailorable mechanical properties by a thermal-assisted 3D printing method. Specifically, alginate and agar were combined to minimize the Barus effect, and further improved the printing resolution. The introduction of agar altered the rheological properties of the ink, such as increasing its viscosity to obtain a 3D printing structure with higher precision. The alginate chains were crosslinked by calcium ions, which connected different layers together and had good interface adhesion among layers in 3D printing constructs. In addition, after printing, the crosslinking of calcium alginate affected the swelling behavior and mechanical properties of printing gels. The width of extrusion gel stripes was close to the diameter of needle, demonstrating that the printing resolution is well controlled by minimizing the Barus effect of concentrated solution. Furthermore, the printed gel structures showed low cytotoxicity, indicating that these biocompatible 3D printed structures are promising substitutes for tissue engineering. Most importantly, soft polyacrylamide (PAAm) network was introduced into 3D printed CA/Ag hydrogels to toughen interfacial surfaces between adjacent stripes by combination of rigid calcium alginate network and soft PAAm network. These 3D printed hydrogels with excellent mechanical properties, high compatibility and high shape fidelity can be regarded as a potential candidate in bio-medical field.

The Impact of Multipath Information on Time-of-Arrival Estimation

Time-of-arrival (TOA) based localization plays a central role in current and future localization systems. Such systems, exploiting the fine delay resolution properties of wideband and ultra-wideband (UWB) signals, are particularly attractive for ranging under harsh propagation conditions in which significant multipath may be present. While multipath has been traditionally considered detrimental in the design of TOA estimators, it can be exploited to benefit ranging. This paper investigates the impact of a priori multipath information on TOA estimation. To this end, bounds on the performance of TOA estimation in dense multipath channels are provided and discussed under different operating conditions. The effects of channel dispersion and transmission bandwidth on ranging systems are investigated showing that, contrary to communication systems, diversity behavior is only exhibited in the medium signal-to-noise ratio regime.