A Prototype of beam position and phase measurement electronics for the LINAC in ADS

This article presents a prototype of beam position and phase measurement(BPPM)electronics designed for the LINAC in China Accelerator Driven Sub-critical system(ADS).The signals received from the Beam Position Monitor(BPM)detectors are narrow pulses with a repetition frequency of 162.5 MHz and a dynamic range more than40 dB.Based on the high-speed high-resolution Analog-to-Digital conversion technique,the input RF signals are directly converted to In-phase and Quadrature-phase(IQ)streams through under-sampling,which simplifies both the analog and digital processing circuits.All signal processing is integrated in one single FPGA,in which real-time beam position,phase and current can be obtained.A series of simulations and tests have been conducted to evaluate the performance.Initial test results indicate that this prototype achieves a phase resolution better than 0.1 degree and a position resolution better than 20μm over a 40 dB dynamic range with the bandwidth of 780 kHz,which is well beyond the application requirements.

一种基于Multiple Sampling的高精度相位差测量算法研究

阐述了一种相位差测量算法。首先介绍了Multiple Sampling算法的理论根据,并且进行了算法仿真实验和误差分析,然后在此基础上进行了谐波及噪声分析,提出了针对可变频信号的算法仿真方案,结果表明在增大白噪声幅度的情况下,初相最坏误差仍控制在高精度工程标准内。

High accuracy amplitude and phase measurements based on a double heterodyne architecture

In the digital low level RF （LLRF） system of a circular （particle） accelerator, the RF field signal is usually down converted to a fixed intermediate frequency （IF）. The ratio of IF and sampling frequency determines the processing required, and differs in various LLRF systems. It is generally desirable to design a universally compatible architecture for different IFs with no change to the sampling frequency and algorithm. A new RF detection method based on a double heterodyne architecture for wide IF range has been developed, which achieves the high accuracy requirement of modern LLRF. In this paper, the relation of IF and phase error is systematically analyzed for the first time and verified by experiments. The effects of temperature drift for 16 h IF detection are inhibited by the amplitude and phase calibrations.

Multiple Measurement Models of Articulated Arm Coordinate Measuring Machines

The existing articulated arm coordinate measuring machines（AACMM） with one measurement model are easy to cause low measurement accuracy because the whole sampling space is much bigger than the result in the unstable calibration parameters. To compensate for the deficiency of one measurement model, the multiple measurement models are built by the Denavit-Hartenberg＇s notation, the homemade standard rod components are used as a calibration tool and the Levenberg-Marquardt calibration algorithm is applied to solve the structural parameters in the measurement models. During the tests of multiple measurement models, the sample areas are selected in two situations. It is found that the measurement errors＇ sigma value（0.083 4 ram） dealt with one measurement model is nearly two times larger than that of the multiple measurement models（0.043 1 ram） in the same sample area. While in the different sample area, the measurement errors＇ sigma value（0.054 0 ram） dealt with the multiple measurement models is about 40% of one measurement model（0.137 3 mm）. The preliminary results suggest that the measurement accuracy of AACMM dealt with multiple measurement models is superior to the accuracy of the existing machine with one measurement model. This paper proposes the multiple measurement models to improve the measurement accuracy of AACMM without increasing any hardware cost.

ADAPTIVE UPDATE RATE FOR PHASED ARRAY RADAR BASED ON IMMK-PF

Interacting Multiple Model Kalman-Particle Filter (IMMK-PF) has the advantages of particle filter and Kalman filter and good computation efficiency compared with Interacting Multiple Model Particle Filter (IMMPF). Based on IMMK-PF, an adaptive sampling target tracking algorithm for Phased Array Radar (PAR) is proposed. This algorithm first predicts Posterior Cramer-Rao Bound Matrix (PCRBM) of the target state, then updates the sample interval in accordance with change of the target dynamics by comparing the trace of the predicted PCRBM with a certain threshold. Simulation results demonstrate that this algorithm could solve the nonlinear motion and the nonlinear relationship between radar measurement and target motion state and decrease computation load.

GPS/BDS/INS tightly coupled integration accuracy improvement using an improved adaptive interacting multiple model with classified measurement update

An Extended Kalman Filter（EKF） is commonly used to fuse raw Global Navigation Satellite System（GNSS） measurements and Inertial Navigation System（INS） derived measurements. However, the Conventional EKF（CEKF） suffers the problem for which the uncertainty of the statistical properties to dynamic and measurement models will degrade the performance.In this research, an Adaptive Interacting Multiple Model（AIMM） filter is developed to enhance performance. The soft-switching property of Interacting Multiple Model（IMM） algorithm allows the adaptation between two levels of process noise, namely lower and upper bounds of the process noise. In particular, the Sage adaptive filtering is applied to adapt the measurement covariance on line. In addition, a classified measurement update strategy is utilized, which updates the pseudorange and Doppler observations sequentially. A field experiment was conducted to validate the proposed algorithm, the pseudorange and Doppler observations from Global Positioning System（GPS） and Bei Dou Navigation Satellite System（BDS） were post-processed in differential mode.The results indicate that decimeter-level positioning accuracy is achievable with AIMM for GPS/INS and GPS/BDS/INS configurations, and the position accuracy is improved by 35.8%, 34.3% and 33.9% for north, east and height components, respectively, compared to the CEKF counterpartfor GPS/BDS/INS. Degraded performance for BDS/INS is obtained due to the lower precision of BDS pseudorange observations.

ALGORITHMS FOR TRACKING MANEUVERING TARGET WITH PHASED ARRAY RADAR

Several typical algorithms for tracking maneuvering target with phased array radar are studied in this paper. The constant gain filter with multiple models is analyzed. A typical method for adaptively controlling the sampling interval is modified. The performance of the single model and multiple model estimator with uniform and variable sampling interval are evaluated and compared. It is shown by the simulation results that it is necessary to apply the adaptive sampling policy based on the multiple model method when the maneuvering targets are tracked by the phased array radar since saving radar resources is more important. The adaptive algorithms of variable sampling interval are better than the algorithms of variable model. The adaptive policy to determine the sampling interval based on multiple model are superior than those based on the single model filter, because IMM estimator can adapt to the maneuver more quickly and the prediction covariance of IMM is the more sensitive and more reliable index than residual to determine the sampling interval. With IMM based method, lower sampling interval is required for a certain accuracy.

基于压缩感知自适应测量矩阵的空气质量主动采样

随着城市化进程的不断加快,工业发展、人口聚集使得空气质量问题日益严峻。出于对采集成本的考虑,对空气质量的主动采样正受到越来越多的关注。但现有模型要么只能迭代选择采样位置,要么难以实时更新采样算法。基于此,提出了一种基于压缩感知自适应测量矩阵的空气质量主动采样方法,将采样位置的选择问题转化为矩阵的列子集选择问题。该方法首先利用历史完整数据进行字典学习,然后将学习后的字典经过列子集选择后得到能够指导批量采样的自适应测量矩阵,最后结合利用空气质量数据特性构建的稀疏基矩阵恢复出未采样的数据。该方法使用压缩感知模型一体化实现采样和推断,避免了使用多个模型的不足。此外,考虑到空气质量的时序变动问题,在每一次的主动采样后,字典还会利用最新数据进行在线更新以指导下一次的采样。两个真实数据集上的实验结果表明,经过字典学习后得到的自适应测量矩阵在低于20%的多个采样率下,恢复性能优于所有基线。

基于正余弦分解的两分段自适应非局部均值滤波方法

为解决包裹相位图中存留的散斑噪声问题,文中提出了一种基于正余弦分解的两分段自适应非局部均值滤波方法。该方法通过两次改进衰减参数的大小和相似性度量的方式实现了算法的自适应化。利用该方法对包裹相位图的正余弦分量去噪,去噪后利用反正切运算获取干净的包裹相位,对该相位进行解包裹运算。实验和仿真结果表明,所提方法既有效去除了包裹相位图中的噪声,也保留了相位图中的边缘信息。相比于分别使用SCA(Sine Cosine Algorithm)方法和BM3D(Block-Matching and 3D filtering)方法,通过所提方法去噪后的图像等效视数(Equivalent Number of Looks,ENL)最大,散斑抑制指数(Speckle Suppression Index,SSI)最小,且均方误差提升了约两倍,说明所提方法有效去除了包裹相位中的噪声,提高了相位解包裹的精度。

大频率动态微弱MPSK信号高精度测频算法

传统测频方法对多进制相移键控(multiple phase shift keying,MPSK)在信号采样数据非线性变换后直接进行快速傅里叶变换,方法简单易实现,但频率测量范围小且精度差,无法适应由目标高速机动、通信距离远导致的MPSK信号频率动态大、信噪比(signal to noise ratio,SNR)低的应用场景。针对该难题,提出了一种“粗+精”处理架构的高精度测频算法,粗测状态采用功率谱加窗法粗略测量多普勒频移,精捕状态采用变化率分槽修正和滑动分组平均压缩实现多普勒频移及其变化率的双重精确测量。试验与分析结果表明,所提算法在大频率动态、低SNR条件下,多普勒频移测量精度达到Hz级、多普勒变化率测量精度达到Hz/s级,显著提高了MPSK信号在大频率动态和低SNR环境下的测频精度和检测灵敏度。

超声波流量计流体异常诊断与处理方法研究

针对超声波流量计在测量液体流量的过程中,流体的异常状态会极大地影响流量计的测量精度这一问题,提出了一种基于脉宽比和幅值电压检测的方法,以此来监控信号质量,通过对测量系统进行了实时监测并及时诊断异常情况,从而保证超声波流量计的测量精度和可靠性。首先,设计了超声波流量计监测系统,实现对流量计数据的采集、分析与处理等功能;然后,采用脉宽比结合幅值电压监测方法判断了流体异常的类型;利用自适应采样算法的反馈机制调节了测量周期,以适应管道内流体的变化速度;最后,通过流量测量实验,测试了流体内不同气泡状态下脉宽比的变化规律,测量口径为15 mm管道的多个流量点,分析了流体稳态和少量连续气泡状态下传统测量方法与自适应算法测得的数据,对平均相对误差和重复性进行了对比。实验结果表明:异常诊断功能能够判别流体内气泡状态;基于自适应采样算法,超声波流量计在小流量0.1 m^(3)/h~1.5 m^(3)/h区间内平均误差达到1.02%,重复性也达到0.47%,准确度等级达到1.5级。经液体流量标准装置检定,流量计累积流量测量误差降低了60%,重复性提高了80%。研究结果表明:异常监测系统可以及时判断流体异常情况,同时自适应采样算法有效改善了超声波流量计的测量精度与稳定性。

基于相参积累激光扫频干涉液位测量方法研究

工业生产中,容器内液位高度的控制是生产运输过程中对确保设备正常运行和维护人员生命安全具有重要意义。本文提出采用激光扫频干涉测量系统对液位进行测量,该方法具有诸多优势,如测量范围大、非接触式测量、响应速度快和操作便捷等。由于液体对红外光的吸收率相对较高,导致测量系统采集到的回光信号非常微弱,同时信噪比也不够理想。为解决此问题,本文引入相参积累方法,并将其与测量系统相结合,以提高液位检测的精准度。在信号的各个周期中,信号携带的有效信息保持一致,噪声信息是则随机的,因此通过相参积累方法处理后,不仅可以增强信号强度,还可以提升信噪比。再通过扩相频率采样法进行信号的非线性校正,最后对数据进行处理就,即可以获得液位信息。实验结果表明,相参积累方法对增加信号强度与信噪比有显著效果,为解决低信噪比下提升测量精度问题提供了思路。

区间量测下自适应交互多模型箱粒子滤波机动目标跟踪

针对现有交互多模型箱粒子滤波(Interacting Multiple Model Box Particle Filter,IMMBPF)算法在区间量测目标跟踪过程中模型切换和跟踪精度方面的不足,结合自适应交互多模型算法,提出了一种自适应交互多模型箱粒子滤波(Adaptive IMMBPF,AIMMBPF)算法。该算法利用模型似然后验信息构建修正因子,并结合阈值对马尔可夫转移概率矩阵进行自适应修正,使得匹配模型的概率快速增大,并且可以减小非匹配模型的影响,即使在目标运动模型先验信息不足或者不准确情况下,也能对模型转移概率进行自适应更新。对于量测常受到未知分布和偏差的区间误差所影响而呈现区间形式的问题,将箱粒子代替普通粒子,拟合后验概率密度从而进行滤波。仿真结果表明,相比于原有算法,该算法在区间量测机动目标跟踪的应用中,拥有更优的模型匹配度和目标跟踪精度。

流动相在线混合模式和预混合模式对示差折光检测器的影响

目的:研究混合型流动相对不同品牌示差折光检测器造成的基线波动影响,提出改进和应对措施,为示差折光检测器及配套液相系统的使用提供参考和建议。方法:通过乙腈水70+30百分比作为流动相,利用不同品牌的示差折光检测器对葡萄糖进行测量,得出基线漂移、基线噪声、最小检测浓度、线性范围,考察示差折光检测器的性能。结果:通过预混合能降低流动相混合对示差折光检测器的干扰,但仍然会存在周期性的脉动干扰,使用泵后混合模式干扰最小。结论:示差折光检测器推荐使用多元泵后混合的配置,测量精度较高,如使用泵前混合,则应使用单并联式的输液泵,降低干扰,提高精度。

基于相关原理的非整周期信号相位差测量算法

为提高相关法在非整周期采样情况下的相位差测量精度,提出一种基于相关原理的非整周期信号相位差测量算法:首先采用自适应格型陷波器进行滤波,以求信号频率;然后利用滤波后的原2路同频信号产生具有90°相移的2个参考信号;将原2路同频信号分别与2路参考信号进行相关运算,再利用相关和正弦函数的一些性质得到相位差的计算公式。同时,针对引入两参考信号相移不是90°的情况,进行了误差分析并给出了相应的补偿措施。理论分析表明,该算法测量精度不受采样是否整周期的影响,能实现相位差的动态检测,与传统算法相比,该算法测量精度更高,抗噪性能更强。仿真分析与工程应用均证实了该算法的有效性和可行性。

一种基于自适应陷波器的电网频率测量新方法

该文提出一种测量和跟踪电网频率变化的新方法:采用两级自适应陷波滤波器结构,第一级陷波器用来滤除谐波并得到加强的基波成分,再经过降采样处理把基波频谱拓宽后,由第二级陷波器来估计基波频率。陷波器的自适应算法是非梯度搜索的,使其运算量大大简化,适用于实时处理的场合。并通过仿真来说明该方法具有测量精度高和响应时间快的特点。

基于多次互相关的非整周期信号相位差测量算法

针对工程实际中频率未知情况下存在相位差测量精度较差的问题,提出一种基于多次互相关的非整周期信号相位差测量算法:首先采用自适应格型陷波器进行滤波,以求信号频率;然后对滤波后的原2路增强信号分别进行希尔伯特变换,产生具有90相移的2路相移信号;并利用估计出的信号频率产生具有90相移的2路参考信号;将原2路增强信号和两路相移信号分别与2路参考信号进行相关运算,再利用相关和正弦函数的一些性质即可得到相位差的计算公式。实验结果表明,该算法测量精度不受采样是否整周期的影响,能实现相位差的动态检测;与传统算法相比,该算法测量精度更高,抗噪性能更强,更具鲁棒性和普适性。

基于相关原理的相位差测量改进算法及应用

为提高相关法在非整周期采样情况下的相位差测量精度,增强算法的动态响应能力,提出了一种基于相关原理的相位差测量改进算法。该算法利用原两路同频信号产生具有90°相移的两个参考信号,经零相位滤波后,将原两路信号分别与两路参考信号进行相关运算,再利用相关和正弦函数的一些性质得到相位差的计算公式。理论分析和实验结果表明,该算法具有不必预先知道信号频率的特点,测量精度不受采样是否整周期的影响,且能实现相位差的动态检测,与传统算法相比,该算法测量精度更高,更具鲁棒性和普适性。

锁相倍频和准同步采样法在谐波测量中的应用

介绍了一种新的微机谐波测量分析装置 ,该装置给出准同步和锁相倍频两种方案供选择使用 .对电网参数进行采样时 ,可分别用锁相倍频方案或准同步方案 ,以达到互补 ,从而使测量误差变得最小 .同时 ,还介绍了该装置的硬件和软件 ,并结合实际对系统的误差进行了分析 .

适用于非同步采样的相位差准确测量方法

提出了一种基于相关原理的电信号相位差准确测量方法,在同步采样条件下,对电压、电流信号作归一化处理后得到的2信号的互相关函数在初始时刻的值直接反映了它们之间的相位差。电信号频率发生波动时,同步采样变成非同步采样,根据相关函数的原理式进行推导得到改进的相位差测量算法。并对其在实际测量中的主要不确定源进行了分析,定量地给出它们对改进算法测量质量的影响程度。该方法可同时测量出相位差和信号的频率,具有较高的准确度, 适用于同步采样和非同步采样两种情况。实验及仿真结果均验证了该方法的有效性。