基于无人机GPS的测量雷达标校方法研究

针对测量雷达常规标校方法存在的场地及设施限制性问题,提出了一种基于无人机加装GPS设备的雷达标校方法.利用无人机加装差分GPS设备作为跟踪目标并提供真值数据,根据系统误差修正模型,通过最小二乘法对雷达测量误差进行回归分析,解算出系统误差修正参数,即可完成雷达标校.通过模拟数据解算得出了该标校方法适用的航线范围,通过飞行测试对标校方法进行了验证,结果表明,利用解算得出的标定参数进行系统误差修正能够满足雷达精度要求,证明了该方法有效、可行.     

基于北斗卫星的GPS轨迹数据异常双频定位方法

当前GPS轨迹定位方法均采用单频定位,在数据异常情况下不能保障定位精度,故此提出一种基于北斗卫星的GPS轨迹数据双频定位方法研究。先基于北斗卫星的定位原理建立用于空间几何距离测量和地面监测点精准定位的数学模型,并确定出伪距和载波相位的观测值的权重;利用北斗卫星确定出标的物的空间几何距离,及空间位置信息;由于定位系统本身及大气电离层的影响,得到空间定位信息内包含有误差项,基于北斗卫星系统可以修正GPS轨迹误差项和异常数据,实现对标的物位置信息的精准定位。测试数据表明提出定位方法的精度更高,综合定位偏差值为0.56%,同时定位误差的均值和方差控制表现更好。     

多基准站GPS定位靶场测试方法研究

在靶场试验过程中,传统的单基准站由于基线长度的限制使其作用范围有限,从而超出一定距离后,差分精度随着大气误差相关性的减弱而明显受到影响;为了解决靶场GPS单频伪距差分定位距离远时精度下降问题,提出了多基准站GPS定位伪距差分定位方法;该方法深入分析伪距方程和伪距差分定位之间的关系;建立靶场基准站网络实时接收目标定位数据和基准站定位数据并实时传送到控制中心;利用基于距离的线性内差模型,设计多基准站数据处理算法和多基线测量数据加权处理方法,实时解算目标定位信息;测试结果表明,多基准站GPS定位伪距差分定位方法,能够有效地解决常规单基准站伪距差分定位精度随用户站与基准站距离增加而降低的问题,同时提高了测试精度。     

Spoofing Signal Generation Based on Manipulation of Code Delay and Doppler Frequency of Authentic GPS Signal

We propose an efficient spoofing signal generation method that uses the processing results of a global positioning system (GPS) receiver for authentic GPS signals. Conventional methods of generating spoofing signals use expensive GPS simulators because the structures of the spoofing signals must be almost identical to those of the GPS signals. Simulators require GPS ephemeris at a specific time and target position. Subsequently, a complicated process is used to generate navigation data using the ephemeris and model error sources such as the satellite clock bias and ionospheric delay. In contrast, the proposed method can generate spoofing signals for the desired target position without requiring GPS simulators; it does so by adjusting the signal processing results of the receiver. Using the navigation results of the receiver, such as position and velocity, the pseudorange delay and spoofing Doppler frequency between the estimated position of the receiver and the target spoofing position are obtained; these are then applied to shift the signal-tracking results of the receiver to create a new signal for the target spoofing position. Our experimental results indicate that the proposed algorithm can effectively generate spoofing signals with characteristics highly similar to those of authentic GPS signals. In addition, we confirmed that the spoofing signals generated by the proposed method are difficult to be detected using conventional spoofing detection techniques.

     

遥测GPS/BD定位数据实时引导雷达局部组网技术

为满足靶场航落区外弹道测试以及关键段测试需求,利用弹载GPS/BD导航信息误差固定、不会随时间累加的特点,结合遥测地面站波速角宽的测试优势,提出一种利用遥测GPS/BD定位数据实时引导雷达局部组网技术。该方法首先对遥测地面站接收到的GPS/BD数据进行实时解算、坐标转换;然后通过指控网络将引导数据实时发送至雷达,实现遥测引导雷达。为保证整个传输过程数据可靠稳定,通过添加校验后再传输的方法,提高传输数据质量。通过实际应用,该方法可以稳定可靠的引导雷达完成测试任务。     

GPS信号仿真器方案设计与实现

研究优化GPS导航系统仿真器设计,要实现一种便携式GPS卫星信号仿真器。由于系统要求实时性强和定位精度高,嵌入式ARM+DSP+FPGA的硬件架构的仿真器平台有限的运算和存储能力不能满足要求,因此提出了GPS轨道仿真的三次样条插值仿真算法,运算量小、计算速度快、误差小,同时采用模块化设计并预留接口,方便扩展开发,可作为多种卫星的仿真器。进行仿真的结果表明,实现了GPS复杂系统仿真的实时性,仿真信号频谱特性较好,定位准确,可为导航系统设计和军用实验提供了可靠依据。     

GNSS accuracy enhancement based on pseudo range error estimation in an urban propagation environment

For new ITS applications, positioning solutions will require to be more accurate and available. The most common technique used today is composed of a GPS receiver, sometimes aided by other sensors. GPS, and GNSS in general, suffer from masking effects and propagation disturbances in urban areas that cause biases on pseudo range measurements. Mitigation solutions sometimes propose to detect and exclude outliers but in land transportation applications, such a decision reduces dramatically the service availability and thus, the interest of satellite-based solutions. In order to optimize the use the satellites received, we propose a new positioning algorithm based on signals only with pseudo range error modeling in association with an adapted filtering process. The model and the filter have been validated with simulation data performed along an urban bus line and have shown that both positioning error and availability can be improved. Along the trajectory tested, the mean accuracy has been reduced from 5.3 m with a classical filter to 2.6 m with our algorithm with 89% of the points more accurate than 5 m instead of 64% before.     

基于最小边界扇形的移动对象轨迹实时化简算法

为了对全球定位系统(GPS)设备采集到的移动对象原始轨迹数据进行简化,提高轨迹数据的使用效率,降低移动终端的通信代价和计算开销,提出了一种基于最小边界扇形(MBS)的移动对象轨迹实时化简算法。该算法不同于用一条折线来近似原始轨迹的方法,它利用扇形预测范围来估计、简化原始轨迹,在角度和距离两个层面上对简化误差进行控制。提出了新的误差度量方法--基于等极径的误差度量方法,并讨论了GPS定位误差对简化算法的影响。实验结果表明,所提算法的简化轨迹高效、稳定,所得到的简化轨迹与原始轨迹之间误差较小(不超过误差阈值的20%),对GPS定位误差有较好的容错能力。     

The integration of strap-down INS and GPS based on adaptive error damping

The concept and results of integration of a strap-down inertial navigation system (INS) based on low-accuracy inertial sensors and the global positioning system (GPS) have been presented in this paper. This system is aimed for the purposes of navigation, automatic control, and remote tracking of land vehicles. The integration is made by the implementation of an extended Kalman filter (EKF) scheme for both the initial alignment and navigation phases. Traditional integration schemes (centralized and cascaded) are dominantly based on the usage of high-accuracy inertial sensors. The idea behind the suggested algorithm is to use low-accuracy inertial sensors and the GPS as the main source of navigation information, while the acceptable accuracy of INS is achieved by the proper damping of INS errors. The main advantage of integration consists in the availability of reliable navigation parameters during the intervals of absence of GPS data. The influence of INS error damping coefficients is different depending on the fact whether the moving object is maneuvering or is moving with a constant velocity at that time. It is proposed that INS error damping gain coefficients generally should take higher values always when GPS data are absent, while at the same time their values in the error model (EKF prediction phase) can be additionally adapted according to the actual values of vehicle acceleration. The analysis of integrated navigation system performances is made experimentally. The data are acquired along the real land vehicle’s trajectory while the intervals of absence of GPS data are introduced artificially on the parts characterized both by maneuver and by constant velocity.     

基于电信数据的室外定位技术研究

越来越多的移动计算依赖位置信息提供基于位置的服务,移动设备的室外定位技术至关重要。目前广为采用的方式是GPS,但移动设备端的GPS位置信息依赖移动设备如手机的GPS传感器获取,电信运营商虽然为用户提供通话和数据服务,却无法获得用户的精确GPS位置。针对这种情况,提出利用手机端和电信基站之间的连接信号数据(简称电信数据),实现移动设备的定位服务。考虑到电信运营商积累了海量的电信数据,因此通过研究基于电信数据的室外定位技术,使得运营商获取用户位置成为可能。提取电信特征数据、以手机所在GPS位置作为标签数据,研究了五种基于机器学习模型的室外定位算法,实现了从基站信号数据到GPS坐标点的预测,通过大量的实验对比了这些方法的定位精度和运行时间、不同数据收集模式的定位精度、不同特征的定位精度以及探索了后处理对定位精度的提升效果。最终通过实验可知,基于栅格化的随机森林分类模型是效果最好的方法,能够达到15~20m的平均误差和10m的中位误差,比前期回归算法在2G和4G数据分别实现了39.46%和54.28%的精度提升,取得与GPS定位接近的定位精度。     

基于注意机制的实时红外舰船检测

为了提高红外舰船图象检测的实时性 ,提出了一种基于多分辨率注意机制的红外舰船图象检测方法 ,该方法是利用注意机制来降低待处理数据量 ,并将注意过程分为“预注意”和“注意”两个阶段 ,同时采用非线性采样模型 ,在降低预注意分辨率的同时 ,使该方法能适应目标大小变化 ;检测时 ,将红外舰船图象中舰船发动机或烟囱所在的热区域作为“预注意”的特征 ,先将注意引导到感兴趣区域上 ,再在感兴趣区域内检测吃水线特征 .实验证明 ,该方法能有效地提高红外舰船检测的效率 ,并对目标大小变化有一定的适应能力 .     

A low-cost INS/GPS integration methodology based on random forest regression

This paper, for the first time, introduces a random forest regression based Inertial Navigation System (INS) and Global Positioning System (GPS) integration methodology to provide continuous, accurate and reliable navigation solution. Numerous techniques such as those based on Kalman filter (KF) and artificial intelligence approaches exist to fuse the INS and GPS data. The basic idea behind these fusion techniques is to model the INS error during GPS signal availability. In the case of outages, the developed model provides an INS error estimates, thereby maintaining the continuity and improving the navigation solution accuracy. KF based approaches possess several inadequacies related to sensor error model, immunity to noise, and computational load. Alternatively, neural network (NN) proposed to overcome KF limitations works unsatisfactorily for low-cost INS, as they suffer from poor generalization capability due to the presence of high amount of noise.In this study, random forest regression has shown to effectively model the highly non-linear INS error due to its improved generalization capability. To evaluate the proposed method effectiveness in bridging the period of GPS outages, four simulated GPS outages are considered over a real field test data. The proposed methodology illustrates a significant reduction in the positional error by 24–56%.     

TransGPerf: Exploiting Transfer Learning for Modeling Distributed Graph Computation Performance

It is challenging to model the performance of distributed graph computation. Explicit formulation cannot easily capture the diversified factors and complex interactions in the system. Statistical learning methods require a large number of training samples to generate an accurate prediction model. However, it is time-consuming to run the required graph computation tests to obtain the training samples. In this paper, we propose TransGPerf, a transfer learning based solution that can exploit prior knowledge from a source scenario and utilize a manageable amount of training data for modeling the performance of a target graph computation scenario. Experimental results show that our proposed method is capable of generating accurate models for a wide range of graph computation tasks on PowerGraph and GraphX. It outperforms transfer learning methods proposed for other applications in the literature.     

基于GPS和神经网络的滑坡位移监测算法

针对使用GPS接收机进行滑坡位移监测时,单点GPS定位误差较大的问题,提出了一种基于GPS和神经网络的滑坡位移监测算法。GPS接收机在滑坡发生之前和滑坡发生之后测得的定位数据耦合在一起,不是线性可分的。采用具有非线性可分特性的神经网络,把耦合在一起的定位数据分成两类,一类属于未滑坡的GPS数据,另一类属于发生滑坡的GPS数据,避免了对GPS定位误差这一非线性非高斯问题进行准确建模的过程。利用GPS接收机测得的样本训练集训练神经网络,用训练后的神经网络模型来验证测试集的分类效果。实测实验结果表明,对于低精度的GPS接收机,当滑坡程度分别达到3米、5米、8米时,训练样本分类正确率分别是95.85%、99.23%、99.99%,测试样本分类正确率分别是82.94%、89.44%、91.05%,说明所提出的算法适用于较大程度的滑坡。     

Focussed adaptivity for laminated plates

Accurate computation of the critical response quantities for laminated composite structures has become essential, especially from the design and design certification point of view. Worst case scenario analysis (corresponding to the load envelope) of the structure require computation of local quantities of interest. In such a situation, control of both modelling error and discretisation error for the quantities of interest is required.In this study, for a fixed plate model, a novel adaptive procedure is presented, based on a posteriori estimation of the error in the quantity of interest. This focussed adaptive procedure involves prediction of the desired optimal mesh sizes in the neighborhood of the region of interest and away from the region of interest, based on an a priori estimate of the error in the quantity of interest. The final desired mesh is obtained in one shot. It is found that the error estimator, for the quantity of interest is reasonably robust. Further, the adaptive procedure is very effective in controlling the local error to within the specified tolerances.     

基于量化误差与分形理论的高计算效率无监督聚类研究*

已有的矢量聚类算法需学习较多的复杂数据方可获得较好的聚类效果,而对于多维的大数据性能较弱,对此,提出一种基于量化误差与分形理论的高计算效率无监督聚类算法。首先,为数据集建立量化误差的参数化模型,基于数据集的空间结构获得数据集的率失真曲线;然后,通过对率失真曲线的估算,获得数据空间的有效维度;最终,利用分形理论,通过搜索数据集的量化模型参数获得目标数据集的最优类簇数量。实验结果表明,本文的量化误差参数化模型可较好地估算数据集的有效维度,同时,本算法对数值型数据集的最优类簇估算与计算效率优于已有的矢量聚类算法。     

RFID assisted vehicle positioning in VANETs

With technological advancement, recent VANET applications such as safe driving and emergency rescue often demand high position accuracy. Unfortunately, however, conventional localization systems, e.g., GPS, hardly meet new accuracy requirements. To overcome this limitation, this paper proposes an RFID-assisted localization system. The proposed system employs the DGPS concept to improve GPS accuracy. A vehicle obtains two different position data: GPS coordinate from its own GPS receiver and accurate physical position via RFID communication. Then, it computes GPS error and shares it with neighbors to help them correct inaccurate GPS coordinates. To evaluate the proposed system, we conduct extensive experiments both on a simulator and on a real world test-bed. The simulation shows that, with the RFID-assisted localization system, vehicles can acquire accurate position both on a freeway and in an urban area. The results from the test-bed experiments demonstrate that the proposed system is feasible in the real VANET environment.     

Fast motion estimation based on search range adjustment and matching point decimation

This study introduces a new fast motion estimation (ME) based on both an adaptive search range adjustment and a matching point decimation. In particular, the authors present a maximum matching error constraint in the matching phase that can eliminate an impossible candidate block much earlier than a conventional partial distortion elimination (PDE) scheme. The constraint is computed during the matching error computation based on sum of absolute difference (SAD) between two blocks. The basic idea of the proposed scheme is based on adjusting a given search range adaptively and early eliminating invalid matching blocks effectively. The adaptive search range adjustment is first performed by analysing the contents of a scene. Next, a maximum partial matching error in reordered sub-blocks of an optimal block is obtained, and it is set as a trigger to eliminate invalid blocks for ME. The main contributions of the proposed scheme are that (i) it can reduce a search range adaptively based on the analysis of scene contents; (ii) it can make an early decision for an impossible candidate before complete SAD computation; (iii) the proposed constraint can reduce the computational cost considerably for SAD calculation; and (iv) the proposed matching ideas can be applied to conventional PDE algorithms without significant changes. In order to evaluate the proposed scheme, several baseline approaches are described and compared. The experimental results show that the proposed algorithm can reduce the computational cost more than 86% for ME at the cost of 0.02%dB quality degradation on against the conventional PDE algorithm.     

机载激光扫描测距仪的误差分析

对机载激光扫描测距仪在实际测量中的扫描角、目标表面倾斜度、激光束发散角效应、GPS定位精度及INS姿态测量精度等可能引起测量误差的因素进行了分析,并给出了定量化的评估,为机载激光扫描测距系统的测距精度的提高和测距性能的改进提供了有益的参考依据。     

Adaptive Fuzzy Prediction of Low-Cost Inertial-Based Positioning Errors

Kalman filter (KF) is the most commonly used estimation technique for integrating signals from short-term high performance systems, like inertial navigation systems (INSs), with reference systems exhibiting long-term stability, like the global positioning system (GPS). However, KF only works well under appropriately predefined linear dynamic error models and input data that fit this model. The latter condition is rather difficult to be fulfilled by a low-cost inertial measurement unit (IMU) utilizing microelectromechanical system (MEMS) sensors due to the significance of their long- and short-term errors that are mixed with the motion dynamics. As a result, if the reference GPS signals are absent or the Kalman filter is working for a long time in prediction mode, the corresponding state estimate will quickly drift with time causing a dramatic degradation in the overall accuracy of the integrated system. An auxiliary fuzzy-based model for predicting the KF positioning error states during GPS signal outages is presented in this paper. The initial parameters of this model is developed through an offline fuzzy orthogonal-least-squares (OLS) training while the adaptive neuro-fuzzy inference system (ANFIS) is implemented for online adaptation of these initial parameters. Performance of the proposed model has been experimentally verified using low-cost inertial data collected in a land vehicle navigation test and by simulating a number of GPS signal outages. The test results indicate that the proposed fuzzy-based model can efficiently provide corrections to the standalone IMU predicted navigation states particularly position.