Resolving information on algol systems

Space Science Reviews - Our awareness of the facts on Algol systems, arising from both statistical evidence and examination of particular examples, is presented. The statistical information derives...     

Position error bound for UWB localization in dense cluttered environments

For most outdoor applications, systems such as global positioning system (GPS) provide users with accurate location estimates. However, similar range-only localization techniques in dense cluttered environments typically lack accuracy and reliability due, notably, to dense multipath, line-of-sight (LOS) blockage and excess propagation delays through materials. In particular, range measurements between a receiver and a transmitter are often positively biased. Furthermore, the quality of the range measurement degrades with distance, and the geometric configuration of the beacons also affects the localization accuracy. In this paper we derive a fundamental limit of localization accuracy for an ultrawide bandwidth (UWB) system operating in such environments, which we call the position error bound (PEB). The impact of different ranging estimation errors due to beacons distance and biases on the best positioning accuracy is investigated. The statistical characterization of biases coming from measurement campaigns can easily be incorporated into this analysis. We show that the relative importance of information coming from different beacons varies depending on the propagation conditions, such as whether the beacon is LOS or non-line-of-sight (NLOS). We show, in particular, that any a priori information knowledge on NLOS beacons can significantly improve the localization accuracy, especially in dense cluttered environments. Finally we put forth the concept of localization outage probability and epsi-localization accuracy outage, and use them to characterize the quality of localization throughout the area.     

Statistical analysis of real clutter at different range resolutions

A statistical analysis is presented of real radar clutter data collected using the McMaster I FIX radar in 1998 and stored in the Grimsby database. We first show the deviations of the amplitude statistics from the Rayleigh model and the suitability of the K- and Weibull-distribution for the first-order amplitude statistical characterization. Thus we focus on the I and Q components of the available data and study their statistical compatibility with the compound Gaussian model. Towards this goal it has been necessary devising appropriate testing procedures; in particular, with reference to the higher order statistics agreement, we have designed a validation procedure involving the clutter representation into generalized spherical coordinates. Remarkably the results have confirmed the suitability of the spherically invariant random processes (SIRPs) for the correct modeling of the radar clutter. Finally we have performed a spectral analysis highlighting the close matching between the estimated clutter spectral density and the exponential model.     

A Stratified Statistical Filter with Nonlinear Application

This paper introduces a statistical filter (or, more strictly, a filtering algorithm) which has intended application in the area of nonlinear systems. Within this context, the filter enables one to investigate the convergence effects produced by varying the initial estimates associated with the respective state variables, together with the various system parameters. The present algorithm is not intended to replace the more powerful optimal statistical filters used in linear theory, but rather to provide a simulation tool which can readily be applied to a given nonlinear system. The application considered in this paper bears a similarity to a tracking problem which might be encountered by an optical device, where angular information is the primary observable quanity. In this particular application, angular observations are available, and statistical estimates are desired for a position variable, together with an unknown parameter. The application is introduced primarily for the purpose of demonstrating the behavior of the filter when applied to a relatively simple nonlinear system.     

可形变人脸模型研究进展综述

正确理解和跟踪人脸是构建能够在复杂人类环境中自主工作的智能机器系统的首要和关键步骤，可形变模型通过统计学习方法给出对象形状的紧凑数学表示，是达成这一目标的重要手段。从机器学习和计算机视觉领域角度，对人脸图像的可形变模型构建这一研究课题的最新研究进展进行了综述。特别地，对其中涉及的关键问题进行了归纳，并建立了一个抽象的统一贝叶斯可形变模型；在此框架下，对标准的点分布模型PDM模型及其最新研究进展进行了回顾、对一些主要的方法进行了介绍、对这些方法优缺点进行了对比分析。     

Detecting small moving objects using temporal hypothesis testing

This paper addresses the problem of detecting small, moving, low amplitude objects in image sequences that also contain moving nuisance objects and background noise. We formulate this problem in the context of a hypothesis testing procedure on individual pixel temporal profiles, leading to a computationally efficient statistical test. The technique assumes we have reasonable deterministic and statistical models for the temporal behavior of the background noise, target, and clutter, on a single pixel basis. Based on these models we develop a generalized likelihood ratio test (GLRT) and perfect measurement performance analysis, and present the resulting decision rule. We also propose a parameter estimation technique and compare its performance to the Cramer Rao bound (CRB). We demonstrate the effectiveness of the technique by applying the resulting algorithm to real world infrared (IR) image sequences containing targets of opportunity. The approach could also be applicable to other image sequence processing scenarios, using acquisition systems besides IR imaging, such as detection of small moving objects or structures in a biomedical or biological imaging scenario, or the detection of satellites, meteors or other celestial bodies in night sky imagery acquired using a telescope     

武器系统综合效能的模糊积分评估方法

对系统综合效能的评价是进行系统决策的前提和重要依据,在研制和使用系统时,都要对其进行评价,以便为选择研制方案或使用策略提供支持。指出了概率和统计理论在综合效能评估问题研究中的不足。定义了模糊测度和模糊积分的概念,提出了基于模糊测度和模糊积分的武器系统综合效能评估模型,给出了效能评估的基本过程并进行了应用实例分析研究。     

On-board statistical detection and compensation of anomalous pilot-aircraft interactions

A statistical method for the on-board detection and compensation of adverse oscillations in pilot-aircraft systems is presented. A recursively updated linear model representing the pilot-aircraft system is used to continuously check for stability loss. The instability-related oscillations are detected early, using a statistical hypothesis test. Furthermore, a specially designed stability augmentation system assists the pilot during the instability periods. The method's effectiveness is demonstrated via data obtained from a flight simulator and a detailed simulation model.     

多台同步可靠性增长模型存在的问题

探讨多台可靠性增长模型的统计意义,指出多台同步纠正可靠性增长试验丢掉了本可得到的大量的试验信息,失去了统计试验意义,其 AMSAA-BISE模型不能成立,多台同步试验方法不可取     

马赫数1.95超声速欠膨胀喷流声辐射数值分析

针对马赫数为1.95的欠膨胀超声速喷流声辐射特性,采用高精度计算格式的大涡模拟（LES）方法进行数值研究。通过对喷流平均流及湍流脉动统计结果的对比,确定数值方法的精确性。细致分析了超声速喷流流动特征,特别关注了其中激波胞格和湍流相互作用现象。基于流动与声场辐射间的关联性,分析了超声速喷流马赫波、宽频激波噪声辐射特性及形成机理。数值结果表明超声速喷流的剪切层演化及激波胞格与湍流间的相互作用构成欠膨胀超声速喷流的主要噪声源。     

Statistical Technique for Radar Amplitude Calibration

A statistical technique for amplitude calibration of radar systems is presented. As distinct from the input-output crosscorrelation measurement of a linear system impulse response [1], this technique measures the amplitude transfer function of a memoryless, nonlinear system. A generalized theory is developed and calibration accuracy bounds are derived. Used in the calibration of a modern pulsed radar, the technique is compared with conventional reference pulse calibration. The relative merits of the deterministic and statistical approaches are compared.     

颗粒填充复合材料的应变统计特征与刚度模量

针对填充颗粒体积份数高、随机分布的复合材料,基于一种特定应变分解方式,利用有限元数值分析,研究了任意宏观变形条件下代表性体积单元内应变分布的统计规律,分别得到了颗粒与基体内应变的平均值、应变的涨落值与宏观应变之间的统计关系,并对颗粒填充复合材料的等效剪切模量和等效体积模量进行了预测。     

基于5G的通导融合位置认证系统性能分析

随着无人化智能移动装备在工业、交通等安全敏感领域的普及应用,民用导航定位系统中的定位安全问题日益突出。位置认证是对终端的物理位置声明进行认证的过程,是导航定位安全技术的重要组成部分。基于第五代(5G)移动通信网络的通导融合位置认证系统具有覆盖范围广、认证精度高、用户容量大、建设运维成本低等多重优势。本文首先介绍了多基站位置认证系统的检测判决原理,提出了漏检平均距离的定义作为位置认证系统精度的量化评价指标。在此基础上,通过数值仿真分析了信号带宽、基站同步误差、信噪比对位置认证系统精度的影响,并利用5G信道模型评估了典型场景下的位置认证系统的性能。结果表明,在具备3个以上的视距基站时,基于5G的通导融合位置认证系统可以实现米级的位置认证精度。     

基于5G的通导融合位置认证系统性能分析

随着无人化智能移动装备在工业、交通等安全敏感领域的普及应用，民用导航定位系统中的定位安全问题日益突出。位置认证是对终端的物理位置声明进行认证的过程，是导航定位安全技术的重要组成部分。基于第五代(5G)移动通信网络的通导融合位置认证系统具有覆盖范围广、认证精度高、用户容量大、建设运维成本低等多重优势。本文首先介绍了多基站位置认证系统的检测判决原理，提出了漏检平均距离的定义作为位置认证系统精度的量化评价指标。在此基础上，通过数值仿真分析了信号带宽、基站同步误差、信噪比对位置认证系统精度的影响，并利用5G信道模型评估了典型场景下的位置认证系统的性能。结果表明，在具备3个以上的视距基站时，基于5G的通导融合位置认证系统可以实现米级的位置认证精度。     

Multi-channel data compression

With the development of space telemetry technology, it has brought forward higher requests for large capacity memory of hard recovered solid-state recorders. Therefore, data compression becomes more important. The compression feasibility and potentiality of telemetry data are examined by analyzing the statistical characteristics of actual telemetry data recovered from the recorder. Aiming at the disadvantages of present data format In data compression for multi-channel telemetry data acquisition systems, we introduce data packet structure and a real-time compression algorithm for low complex hardware design put forward accordingly. The principles and implementation of data package compression are described. Simulation results show that this technology can meet the requirements of multi-channel real-time data compression with high compression ratio and fast compression speed, which possesses great application values.     

Performance improvement for constellation SAR using signal processing techniques

A new concept of spaceborne synthetic aperture radar (SAR) implementation has recently been proposed - the constellation of small spaceborne SAR systems. In this implementation, several formation-flying small satellites cooperate to perform multiple space missions. We investigate the possibility to produce high-resolution wide-area SAR images and fine ground moving-target indicator (GMTI) performance with constellation of small spaceborne SAR systems. In particular, we focus on the problems introduced by this particular SAR system, such as Doppler ambiguities, high sparseness of the satellite array, and array element errors. A space-time adaptive processing (STAP) approach combined with conventional SAR imaging algorithms is proposed which can solve these problems to some extent. The main idea of the approach is to use a STAP-based method to properly overcome the aliasing effect caused by the lower pulse-repetition frequency (PRF) and thereby retrieve the unambiguous azimuth wide (full) spectrum signals from the received echoes. Following this operation, conventional SAR data processing tools can be applied to focus the SAR images fully. The proposed approach can simultaneously achieve both high-resolution SAR mapping of wide ground scenes and GMTI with high efficiency. To obtain array element errors, an array auto-calibration technique is proposed to estimate them based on the angular and Doppler ambiguity analysis of the clutter echo. The optimizing of satellite formations is also analyzed, and a platform velocity/PRF criterion for array configurations is presented. An approach is given to make it possible that almost any given sparse array configuration can satisfy the criterion by slightly adjusting the PRF. Simulated results are presented to verify the effectiveness of the proposed approaches.     

Effects of Geodetic Uncertainties on a Damped Inertial Navigation System

Gravity uncertainties are an inexorable source of error in all inertial navigation systems and are particularly important in high-quality inertial navigation systems. In this paper the steady-state rms errors that are excited in a damped inertial navigation system are analytically determined for four gravity uncertainty models and two vehicle maneuver models. The statistical approach used in this paper is compared with an alternate scheme (?direct simulation?) that does not require statistical models for gravity uncertainties.     

Position Error Bound Calculation for GNSS using Measurement Residuals

In safety-of-life applications of satellite navigation, the protection level (PL) equation translates what is known about the pseudo-range errors into a reliable limit on the positioning error. The current PL equations for satellite-based augmentation systems (SBAS) rely on Gaussian statistics. This approach is very practical: the calculations are simple and the receiver computation load is small. However, when the true distributions are far from Gaussian, such a characterization forces an inflation of the PLs that degrades performance. This happens in particular with errors with heavy tail distributions or for which there is not enough data to evaluate the distribution density up to small quantiles. We present a way of computing the optimal protection level when the pseudo-range errors are characterized by a mixture of Gaussian modes. First, we show that this error characterization adds a new flexibility and helps account for heavy tails without losing the benefit of tight core distributions. Then, we state the positioning problem using a Bayesian approach. Finally, we apply this method to PL calculations for the wide area augmentation system (WAAS) using real data from WAAS receivers. The results are very promising: vertical PLs are reduced by 50% without degrading integrity.     

Navigation by Back Triangulation

The development of electronic surveillance systems that can accurately measure the direction of arrival of signals from ground radar stations has opened the possibility of using statistical techniques to determine navigation information for the measuring vehicle. The method of combining "bearing" observations from several radar sites for navigational purposes, referred to as "back triangulation", will be discussed and demonstrated by simulation.     

Identification of ground targets from sequential high-range-resolution radar signatures

An approach to identifying targets from sequential high-range-resolution (HRR) radar signatures is presented. In particular, a hidden Markov model (HMM) is employed to characterize the sequential information contained in multiaspect HRR target signatures. Features from each of the HRR waveforms are extracted via the RELAX algorithm. The statistical models used for the HMM states are formulated for application to RELAX features, and the expectation-maximization (EM) training algorithm is augmented appropriately. Example classification results are presented for the ten-target MSTAR data set.