An adaptive optimal-kernel time-frequency representation

第3卷第1期2004年2月 江南大学学报(自然科学版)Journal of Southern Yangtze U niversity(N atural Science Edition) V ol.3　N o.1Feb.　2004　文章编号:1671-7147(2004)01-0001-04 收稿日期:2003-05-22;　修订日期:2003-09-20. 基金项目:江苏省自然科学基金项目(BK 2002068)资助课题. 作者简介:于凤芹(1962-),女,辽宁北镇人,工学硕士,副教授,上海大学控制理论与控制工程专业在读博士研究生.曹家麟(1950-),男,上海人,教授,博士生导师.主要从事信号时频分析与编码的研究.多Chirp 成分信号双线性时频分布的交叉项分析于凤芹1,2,　曹家麟2(1.江南大学通信与控制学院,江苏无锡214036;2.上海大学机械电子与自动化学院,上海200072)摘　要:以广泛出现在许多工程应用领域和物理现象中的多Chirp 成分信号为对象,研究了双线性时频分布对这种信号时频分布的交叉项特点,推导了几种分布的交叉项的数学表示,从模糊平面分析了交叉项抑制的机理,提出了双线性时频分布对多Chirp 成分信号时频表示存在局限,仿真试验结果显示理论分析正确.关键词:多Chirp 成分信号;双线性时频分布;交叉项干扰;中图分类号:T N 911.7文献标识码:AAn Analysis of Cross 2Terms I nterference of Bilinear Time 2FrequencyDistributions for Multi 2Component Chirp SignalsY U Feng 2qin 1,2,　C AO Jia 2lin 2(1.School of C ommunication and C ontrol ,S outhern Y angtze University ,Wuxi 214036,China ;2.C ollege ofMachano 2electronics and Automation ,Shanghai University ,Shanghai 200072,China )Abstract :An analysis on cross 2terms interference of bilinear time 2frequency distributions for multi 2com ponent chirp signals ,which often appear in s ome engineering applications and many natural phenomena ,is given in this paper.The mathematic descriptions of cross 2terms interference of several bilinear time 2frequency distribu 2tions are presented.By com paring the shape of the kernel and the location of cross 2terms in the ambiguity function domain ,the limitations of cross 2terms interference reduce of the existing bilinear time 2frequency dis 2tributions for multi 2com ponent chirp signals are proposed.The simulation result corresponds with the theoretic analysis we have done.K ey w ords :multi 2com ponent chirp ;bilinear time 2frequency distributions ;cross 2terms interference Chirp 信号,又称线性调频信号(LFM :LinearFrequency M odulation ),是一种特殊的非平稳信号,它广泛出现在通信、雷达、声纳和地震勘探等系统中[1].此外,Chirp 信号在时频平面呈直线型,因而常作为衡量一种时频分析方法是否有效的试验信号[2].作者以含有多Chirp 成分信号为对象,研究了现有的几种双线性时频分布对这种信号时频表示中的交叉项的特点,推导了几种分布的交叉项的数学表示,分析了交叉项的结构和特点,通过对双线性时频分布的核函数在模糊平面的形状与多Chirp 成分信号的自项和交叉项在模糊平面分布特点分析,指出了现有的双线性时频分布对该类信号时频表示在交叉项抑制方面存在局限,仿真结果直观地验证了理论分析的正确性.1　几种主要的双线性时频分布及其交叉项 对于非平稳信号x(t),定义其瞬时相关函数为k x(t,τ)=x(t+τ2)x3(t-τ2)(1)对瞬时相关函数作关于t的傅立叶反变换,得到模糊函数(AF:Ambiguity Function)AF x(τ,ν)=∫∞-∞x(t+τ2)x3(t-τ2)e j2πtνd t(2)对瞬时相关函数作关于τ的傅立叶变换,得到Wigner2Ville分布(WVD)为WVD x(t,f)=∫∞-∞x(t+τ2)x3(t-τ2)e-j2πtf dτ(3) WVD将一维时间信号影射到二维时间———频率平面,表示非平稳信号的能量在时频平面同时随频率和时间变化.模糊函数将一维时间信号变换到二维时延———频偏平面,表示信号与其时间延迟和频率偏离信号在时频平面的相关性.如同平稳信号一样,非平稳信号的相关函数与能量表示也存在着二维傅立叶变换关系,由式(2)和式(3),可以直接得到WVD x(t,f)=∫∞-∞∫∞-∞AF x(τ,ν)e-j2π(tν+τf)dνdτ(4) AF和WVD都是信号的双线性函数,对于多分量信号或者具有复杂调制规律的信号,信号间的相互作用产生交叉项干扰,它降低了信号时频分布的分辨率,模糊了信号的原本特征,使时频分布难以解释.为了改善时频分布性能,人们对AF或WVD提出了各种平滑的改进方法,产生了种种时频分布.这种双线性时频分布可以用统一的形式表示为p(t,f)=∫∞-∞∫∞-∞AF x(τ,ν) (τ,ν)e-j2π(tν+τf)dνdτ=∫∞-∞∫∞-∞x(u+τ2)x3(u-τ2) (τ,ν)e-j2π(tν+τf-uν)d u dνdτ(5)在这种统一的表示形式里,不同的时频分布只是体现在核函数形式的选择上,作为能量型时频分布, p(t,f)应具有许多数学性质,因而对核函数产生了种种约束条件[3].不同的核函数,就产生了拥有不同数学性质的时频分布,由式(5)可见,当 (τ,ν) =h(τ),即仅对变量τ加窗函数h(τ)来达到减小交叉项的目的,得到的分布是伪Wigner2Ville分布(PWVD)PW VD x(t,f)=∫∞-∞x(t+τ2)x3(t-τ2)h(τ)e-i2πfτdτ=WVD x(t,f)f3H(f)(6)显然PWVD只能平滑掉变量τ方向的交叉项并且使该方向上的分辨率降低.在两个方向都平滑是平滑伪Wigner2Ville的分布如下(SPWV)SPWV x(t,f)=∫∞-∞x(t-u+τ2)x3(t-u-τ2)g(u)h(τ)e-i2πfτdτ(7)对WVD在时频两个方向滤波,得到平滑Wigner2Ville分布(SWVD)SWVD x(t,f)=WVD x t f33G(t,f)(8)这里t f33表示对时间和频率的二维卷积,G(t,f)是平滑滤波器.谱图可以看作是SWVD的特例,即SPEC x(t,f)=|STFT x(t,f)|2=∫∞-∞x(u)γ3(u-　t)e-i2πfu d u∫∞-∞x3(s)γ(s-t)e i2πfs d s=WVD x(t,f)t f33WVDγ(-t,f)(9)当核函数分别取以下形式:CWD(τ,ν)=exp[-(2πτν)2σ](10)BJD(τ,ν)=sin(πτν)(πτν)(11)MH D(τ,ν)=cos(πτν)(12)RI D(τ,ν)=H(τν)(13)CK D(τ,ν)=g(τ)・|τ|sin(πτν)(πτν)(14)依次得到Choi2Williams分布(CWD)、Born2Jordan分布(BJD)、Margenau2Hill分布(MHD)、减小交叉项分布(RID)和锥型核分布(CK D)[3].交叉项是双线性时频分布固有的,它们来自多分量信号中不同分量之间的交叉作用.时频分布的信号项(自项)产生于信号的每个分量本身,它们与时频分布具有的有限支撑的物理性质是一致的,而交叉项是时频分布里的干扰产物,与原信号的物理性质相矛盾,它模糊了时频分布的分辨率,降低了时频分布的可读性,掩盖了信号的本来特征,阻碍了时频分析的在实际中的进一步应用[4].为了定量地说明交叉项的位置和结构特点,以两个Chirp信号为例,为推导方便,令两个信号是同一个Chirp的时移和频移,即令2江南大学学报(自然科学版)第3卷　 x (t )=ej 2π(f 0t +1/2αt 2),x 1(t )=x (t -t 1)ej 2πf 1t, x 2(t )=x (t -t 2)ej 2πf 2t则WVD x 1+x 2(t ,f )=δ(f -(f 1+f 0)-α(t -t 1))+δ(f -(f 2+f 0)-α(t -t 2))+2δ(f -(f 1+f m )-α(t -t m ))+cos {[f d (t -t m )-t d (f -f m )+f d t m ]}(15)其中:t m =(t 1+t 2)/2,　f m =(f 1+f 2)/2,　t d =t 1-t 2,　f d =f 1-f 2由式(15)可见,WVD 的交叉项发生在两个信号的时间和频率的中点(几何中点处),在垂直于两个信号连接线的方向上振荡,振荡的频率正比于两信号的时间差与频率差,振荡的幅度是自项的两倍,对于含由N 个成分的信号,交叉项的个数是N (N 21)/2.对于该信号,谱图的表达式为SPEC x 1+x 2(t ,f )=|STFT x 1+x 2(t ,f )|2=2|STFT x 1(t ,f )||STFT x 2(t ,f )|×cos [πt (f 1-f 2)-πf (t 1-t 2)+π(f 1t 1-f 2t 2)+(Ψx ,h (t -t 12,f -f 12)-Ψx ,h (t -t 22,f -f 22))](16)从式(16)可以看出,不象W VD 的交叉项总是发生在两个自项的几何中点,谱图的交叉项发生在两个信号的短时傅立叶变换的重叠区域,如果两个信号的STFT 没有重叠,则谱图中不存在交叉项,交叉项的幅度的最大值是两个信号STFT 幅度乘积的两倍,交叉项被一个余弦函数所调制,这个余弦函数不仅与两个信号的中心时间和中心频率有关,还与两个信号交叉的W VD 的相位有关.2　仿真试验与交叉项抑制分析以2个或3个Chirp 信号为例,做出它们的几种常用的时频分布,仅给出时频平面分布以说明交叉项的特点.图1(a )是两个平行的Chirp 信号的W VD ,中间一条为交叉项,图1(b )是两个交叉的Chirp 信号W VD ,其交叉项位于两条直线的几何中点,振荡的方向和频率如以上的理论推导一致.图1(c )是相距较远的两个平行的Chirp 信号的谱图,没有交叉项存在,图1(d )是两个相距较近的信号的谱图,仍然存在交叉项.图1(e )是3个Chirp 信号CK D ,它能平滑掉大量的交叉项,但相距较近的信号成分之间仍存在少量的交叉项.图1(f )是两个chirp 信号的MH D ,它对信号不适当的平滑和分割,产生了大量的多余成分,所以MH D 不宜作Chirp 信号的时频分析工具.(a )两个平行Chirp 信号W VD(b )两个交叉Chirp 信号W VD(c )两个相距较远Chirp 信号的谱图(不存在交叉项)(d )两个相距较近Chirp 信号的谱图(存在交叉项)(e )3个Chirp 信号CK D3　第1期于凤芹等:多Chirp 成分信号双线性时频分布的交叉项分析(f)两个平行Chirp信号MH D图1　几种双线性时频分布的交叉项Fig.1　The cross2terms of some bilinear time2frequency distributions 每一种时频分布的核函数的形状与待分析信号自项与交叉项的位置分布导致了各种时频分布对交叉项抑制程度的差异.固定的核函数决定了每一种分布可能对某一类信号很理想而对另一类信号却不适合[5].几种常用的时频分布的核函数的形状如图2所示.由图2可以看出:W VD的核函数在整个模糊平面处处为1,不作任何平滑,所以交叉项最严重;PW VD仅在(方向上平滑,所以它能抑制在该方向的交叉项,并使该方向的分辨率降低;谱图的核函数与所选用的窗函数的模糊函数有关,如果分析窗与某一个信号成分匹配,则相应的匹配滤波器对给定成分的信号有完美的表示,但对不匹配的信号成分,给出的是一个畸变的表示.对于CW D,如果模糊函数的自项位于τ轴和v轴,与其核函数分布一致,则对于这样的信号表示性能好,而对于多成分的Chirp信号,其自项为过原点的直线,大量的能量既没有落在τ轴附近,也没有位于v轴周围,与CW D分布核函数的形状不匹配,显然对该类信号表示不合适.CK D在模糊平面的形状落在v轴上,同样道理,用CK D表示多成分Chirp信号也不合适.3　结　语通过理论推导和仿真试验结果分析,可以得到图2　几种时频分布的核函数在模糊平面的形状Fig.2　The sh ape of kernel of some bilinear time2frequen2 cy distributions in ambiguity function dom ain如下结论:作为双线性时频分布,W VD对单个分量Chirp信号的时频表示是最理想的,能量在时频平面的分布完全位于其瞬时频率曲线上;对于两个Chirp 信号,其交叉项位于自项的几何中点呈振荡的形式,振荡的幅度是自项的两倍,振荡的频率与两信号的时间差与频率差成正比;对含有多个Chirp成分的信号,各个成分之间可能平行也可能交叉,其两两之间的几何中点的位置和个数也在变化,交叉项的个数按N(N21)/2的规律增长,并且交叉项可能与信号的自项重叠,无法通过后续的平滑方法加以抑制.谱图可以抑制两个距离较远信号的交叉项,但相距较近的信号间的交叉项仍然存在. PW VD、SW VD、CW D及CK D这些分布对信号自项位于模糊平面的两个坐标轴附近、交叉项远离坐标原点的这样信号的时频分布效果好,而多Chirp成分的信号的自项位于模糊平面过原点的直线附近,所以,PW VD、SW VD、CW D及CK D等分布也不适合多Chirp成分的时频分布.实际应用中信号还有大量的噪声存在,会进一步加强交叉项的干扰作用,所以,必须寻找更适合多Chirp成分信号的时频分析工具.参考文献:[1]张贤达,保铮.非平稳信号分析与处理[M].北京:国防工业出版社,1998.[2]DOUG LAS L LONES,TH OM AS W PARK S.A res olution comparis on of several time2frequency representations[J].IEEE T rans SignalProcessing,1992,40(2):413-420.[3]H LAW ATSCH F,BOUDRE AUX2BARTE LS G F.Linear and quadratic time2frequency signal representations[J].IEEE SP MAG A2ZINE,1992,9(2):21-67.[4]SH UBH A K ADAM BE,BOUDRE AUX2BARTE LS G F.A comparis on of the existence of“cross terms”in the wigner distribution and thesquared magnitude of the wavelet trans form and the short time fourier trans form[J].IEEE T rans Signal Processing,1992,40(10): 2498-2517.[5]RICH ARD G,BARANI UK,DOUG LAS L JONES.A signal2dependent time2frequency representation:optimal kernel design[J].IEEET rans Signal Processing,1993,41(3):1589-1601.(责任编辑:戴陵江,彭守敏) 4江南大学学报(自然科学版)第3卷　。

VPPB - The Second Version:An Approach to Monitor Solaris Kernel ThreadsMagnus Broberg, Lars Lundberg, and Håkan GrahnDepartment of Computer Science, University of Karlskrona/RonnebySoft Center, S-372 25 Ronneby, Sweden{Magnus.Broberg, Lars.Lundberg, Hakan.Grahn}@ide.hk-r.se Abstract. Efficient performance tuning of parallel programs is often hard, as notedin i.e. [2]. We present a performance prediction and visualization tool called VPPB(Visualization of Parallel Program Behaviour). Based on a monitored uni- proc-essor execution, the VPPB shows the (predicted) behaviour of a multithreadedSolaris program using any number of processors, thus making the tool very flex-ible. The speed-up is predicted, and the program behaviour is visualized as a graph.The graph can be used in the performance tuning process. To the best of our know-ledge, VPPB is the only available tool that supports this kind of flexible perform-ance tuning of parallel programs developed for shared memory multiprocessorsusing a widely spread standardized parallel programming environment; C or C++programs that uses the built-in thread package in the Solaris 2.X operating system.In the previous version we were unable to trace I/O, by tracing the kernel threadsthis is now possible.1 IntoductionThe VPPB [1] consists of three major parts, Recorder, Simulator, and Visualizer. The user of VPPB starts the already compiled multithreaded program from the Recorder. The Recorder automatically wraps the thread primitives and records every call the mul-tithreaded program does to the primitives. At the end the recorded data is saved in a file. The Simulator is used to read the recorded data and re-schedule the information in order to simulate a multiprocessor. The simulator considers scheduling as well as hard-ware parameters. The scheduling parameters include the priority of a thread, the number of LWPs, and whether the thread should be bound to an LWP, CPU, or not bound at all. The hardware parameters include the number of CPUs on the simulated multiprocessor as well as the time required to synchronize threads between the CPUs.The output from the simulator is displayed in the Visualizer to show the user how the (simulated) execution on the multiprocessor went. The visualization part supply the developer with two graphs: one showing the threads' behaviour over time in a Gant dia-gram and the other the amount of parallelism over time. The second graph is a con-densed version of the first graph. The first graph has a facility which makes it possible to relate an event in the graph to the source code line causing the event. Our approach is based on the assumption that the behaviour of the multithreaded program is (more or less) independent of the scheduling policy and the number of processors used.2 ImprovementsOur first version of VPPB[1] could only monitor the threads' behaviour. Threads are non-preemtive and run in user-space. The threads are mapped on Light Weight Proc-esses (LWPs) that are preemtive, run in kernel-space, and scheduled by the operating system. Since the OS scheduling could not be monitored, only one LWP were allowed to execute during recording. This was a severe limitation, since, e.g., a thread blocked on I/0 will block its LWP and the OS will automatically create new LWPs in order to continue execute other threads.In this second version of VPPB we are incorporating possibilities to handle I/O. We will use the TNF kernel probes in the Solaris kernel to monitor the LWPs. These probes monitor, among other things, each time slice. We will use the same technique for wrap-ping the I/O primitives as for the threads primitives.We have made an initial implementation that allows us to monitor multithreaded pro-grams with each thread bound to an LWP. Further, we have only implemented monitoring of four basic I/O primitives: open; close; read; and write. Each I/O primi-tive is modelled as two distinct events. The first event represent the part of the primitive that take CPU time, the latter event represent the waiting time for the I/O to be (physi-cally) finished. The simulator does not yet dynamically create LWPs when needed, thus the simulation can only be used when binding threads to LWPs.One side effect of allowing several LWP is that programs that relied on spinning locks will not hang when using the Recorder. We are still not able to monitor the actual spin-ning variable. As a result, the prediction of spinning locks will not be very accurate.3 ValidationThe validation of the predictions was made using a subset of the SPLASH-2 benchmark suite. Table 1 givs the benchmarks used, their dataset and the predicted speed up on 2, 4 and 8 processors. The real (measured) speedup are the values in paranthesis.Table 1. BenchmarksName Dataset 2 Proc. 4 Proc.8 Proc. Ocean514-by-514-grid 1.96(1.97) 3.71(3.87) 6.70(6.65) Water-Spatial512 mol., 30 time steps 1.98(2.00) 3.92(3.95)7.59(7.64) FFT4M points 1.53(1.55) 2.08(2.14) 2.59(2.62) Radix16M keys, radix 1024 1.99(2.00) 3.96(3.99)7.88(7.79) LU (cont.)768x768 matrix, 16x16 blocks 1.82(1.79) 3.12(3.15) 4.77(4.82)Cholesky tk29.O 1.40(1.62) 1.98(2.31) 2.38(2.89) FMM 2048 particles 1.64(1.90) 2.21(3.50) 2.06(5.19)All executions were made on a Sun Enterprise 4000 with 8 processors and 512MByte memory. The SPLASH-2 programs are designed to create one thread per physical proc-essor. The time overhead for doing these recordings was less than 7.8% for all programs except Raytrace that had 20.8% overhead.4 DiscussionCholesky and FMM use spinning locks and this affects the exution time when executing several threads on a single processor. As soon as a thread runs into a spinning lock, it is bound to stay there for (in average) a half time slot until another thread can execute and possibly change the value of the lock. This effect made Cholesky's excution time increase by 40% with 8 threads and 220% with 32 threads. The values for FMM are 134% and 476%, repectively. All values are compared with the execution time using one thread.A test conducted on Cholesky, with only one LWP and letting each spinning lock execute a thr_yield for each iteration, gave an excution time increase by 8% and 18%, respectively. The error in our predictions for Cholesky to became half as large as before. Since the monitoring is done on a single processor with multiple threads, the recorded information will include the overhead stated above. This could be one of the reasons why the speed-up prediction for Cholesky and FMM are so bad.We have also constructed a limited validation of our I/O implementation. The small test program consists of two threads. Each thread opens a file, writes 20Mbytes of data in two chucks, and closes the file, this is repeated three times per thread. Running the program on 1 processor, not using threads and thus forcing a sequential behaviour, took 44.15 seconds. When binding the threads and executing them on the Enterprise with 8 processors we got an execution time of 39.86 seconds. Simulation, with 8 processors, shows that we would expect an execution time of 38.10 seconds, that is a 4.4% error.5 Future WorkWhen we get the I/O working we will use a large telecommunication application [2] as a case study. We will use this application since it uses a lot of I/O.References1. Magnus Broberg, Lars Lundberg, and Håkan Grahn, "VPPB - A Visualization andPerformance Prediction Tool for Multithreaded Solaris Programs", International Parallel Processing Symposium 19982. Lars Lundberg, Daniel Häggander, "Optimizing Dynamic Memory Management ina Multithreaded Application Executing on a Multiprocessor", 1998 InternationalConference on Parallel Processing。

浅海环境参数尤其是海底地声参数（包括海底的声速、密度、衰减系数和分层特征等）的获取，除采用海底采样、钻孔等方法进行局部测量外，利用声学方法进行海底参数遥感（地声反演），具有成本低、速度快、范围广等优点。

在深海，反演常通过多径传播的到达时间不同来进行，而在浅海，由于声信号与海水边界作用，使得传播变得十分复杂，通过多径到达结构来进行反演己不太合适。

比较可行的方法是通过阵列获取声信号在时域、频域、空域的幅度和相位信息，并通过有效的寻优过程，得到与接收数据匹配的环境信息。

因此，很多研究者将目光投向了匹配场处理研究。

1973年，Ingenito （1973）进行了模式分离实验，其在浅海中使用垂直阵对简正波模式进行分离和识别，同时利用模式衰减与海底沉积层衰减特性相联系的理论，通过简单的数据拟合确定了海底吸收系数，这是首次将匹配场处理理论应用于海底参数的反演。

而利用声场确定海洋声速的海洋声层析概念，首先是由Munk 和Wunsch（1979）提出的，他们考察了水声信号到达时间与传播路径声速分布的关系。

进入上世纪80年代以后，反演理论有了快速的发展，各种研究成果和实验结果不断涌现。

Rubano（1980）利用不同位置的爆炸声源测量了群速分布、模式形状和传播损失，并通过匹配方法得到了三层海底地声模型的参数。

Zhou （1985）采用与Rubano类似的实验情况，通过群速分布特性和简正波测量结果（80-120Hz）得到了远黄海局部海域的地声参数。

Rajan等（1987）和Lynch 1991）采用群速度分布曲线来反演海底地声属性，并采用线性扰动反演技术比较了窄带和宽带（20-120Hz）反演结果。

Tolsoty等（1991）利用模拟数据，考虑全三维海洋变化性，提出并设计了一种海洋声层析的新方法，即沿感兴趣的海洋体积周围从飞机上投放爆炸物（低频宽带声源），用傅里叶分量与波动方程的解相匹配进行反演。

Diachok等（1995）将宽带全场反演的其它方法和实验处理结果收集在关于海洋环境参数反演的专著中。

基于时频分析和2DNMF的局部放电模式识别廖瑞金;段炼;汪可;杨丽君【摘要】提出时频分析结合二维非负矩阵分解的混合特征提取算法识别不同局部放电类型.在实验室环境下采集了4种典型绝缘缺陷模型的局部放电超高频(UHF)波形,引入自适应最优径向高斯核时频分析挖掘局部放电UHF信号的时频信息,在对时频幅值矩阵进行二维非负矩阵分解提取降维特征后,采用模糊k-近邻分类器对4种不同类型的局部放电信号进行识别.对试验样本的识别结果表明:自适应最优径向高斯核时频分布能较好地表征局部放电单次波形的时频信息;二维非负矩阵分解降维后的特征矩阵能保存原始时频矩阵的大部分有用信息;模糊k-近邻分类器比k-近邻分类器和3层反向传播神经网络具有更高的识别率,并较反向传播神经网络具有容易拓展的优点.%A hybrid feature extraction algorithm based on TFA (Time-Frequency Analysis) combined with 2DNMF(Two-Dimensional Non-negative Matrix Factorization) is proposed to identify the defect types of PD (Partial Discharge). The UHF(Ultra High Frequency) signals of various defect models are measured in laboratory and AORGK(Adaptive Optimal Radially Gaussian Kernel) time-frequency analysis is then introduced to represent the UHF signals. The obtained time-frequency amplitude matrices are further compressed by 2DNMF and FkNN(Fuzzy k-Nearest Neighbor) classifier is then applied to recognize the four typical PD defects. Experimental results show that,TFA describes the time-frequency information of PD UHF signals effectively; the extracted features reserves most useful information of original time-frequency matrices;FkNN classifier has a higher recognition rate than those of kNN(k-Nearest Neighbor)classifier and BPNN(Back Propagation Neural Network),and is easier to expand than BPNN.【期刊名称】《电力自动化设备》【年(卷),期】2013(033)003【总页数】6页(P20-25)【关键词】绝缘;局部放电;模式识别;时频分析;二维非负矩阵分解;模糊k-近邻分类器【作者】廖瑞金;段炼;汪可;杨丽君【作者单位】重庆大学输配电装备及系统安全与新技术国家重点实验室,重庆400044【正文语种】中文【中图分类】TM8350 引言电力设备制造和运行中产生的尖端、气隙等绝缘缺陷会引起绝缘局部场强集中，进而导致绝缘局部击穿并引发局部放电PD（Partial Discharge）。

定孑科■执2020年第33卷第6期Electronic Sci.&Tech./Jun.15,2020一种基于LMS自适应滤波的互相关时延估计优化算法魏文亮，茅玉龙(中国船舶重工集团公司第七二四研究所，江苏南京211106)摘要在多站时差定位系统中使用基于LMS自适应滤波的互相关法进行时延估计时，若采用固定步长因子则会在收敛速度和稳态失调之间存在较大矛盾，从而影响时延估计精度。

针对这一问题，文中提出了一种基于分段变步长LMS自适应滤波和希尔伯特差值的互相关时延估计优化算法。

该方法首先采用分段变步长LMS自适应滤波对信号进行滤波处理，然后将滤波后的信号作互相关运算，最后通过希尔伯特差值法锐化相关函数的峰值，进一步提高时延估计精度。

在相同条件下，文中模拟分析了不同算法的时延估计精度。

实验结果表明，新的优化算法时延估计精度更高。

在不同信噪比下，新方法相较传统时延估计方法精度提高了 2.2%以上，具有良好的抗噪声性能。

关键词时差定位；自适应滤波；互相关；变步长LMS；时延估计；希尔伯特差值中图分类号TN95文献标识码A文章编号1007-7820(2020)06-029-06doi:10.16180/ki.imnl007-7820.2020.06.006Cross一correlation Time Delay Estimation Optimization Algorithm Basedon LMS Adaptive FilteringWEI Wenliany,MAO Yulony(724t h Research Institute,Chigg Shipbuilding Industre Coloration,Nanjing211106 ,China) AbstracC In multi一station time dmferenco positioning system,when using the cross一 correlation method based on LMS adaptive filtecny for time delay estimation,the fixed step factor has a laraa contradiction between conver-g'nc'sp''d and s i ady-s ia i o e s'i，whoch a e cis ih'a ccu ea cy o ed'eay's ioma ioon.A om ong a i ih os p eob em，aceo s -correlation time delay estimation optimization algorithm based on piecewise wCable step size LMS adaptive filtecny and Hoeb'eido e enc'waspeopos'd on ihossiudy.Foesiey，ih'p o c'w os'ea eoab e s i p soz'LMS adapioe'eoeieongwas us'd ioeoeieih'sognae，ih'n ih'eoeie'd sognaewasceo s-co e'eaid，and eona e yih'p'ak oeih'co e'eaioon euncioon was shaoened by the HilbeC dgterenco method to obtain higher delay estimation accuracy.Under the sama condi-ioons，iheiomedeeayesiomaioon accu acyoedo e eeniaegooihmswassomueaied and anaeyzed.The esueisshowed ihai tha proposed optimization alyoothm had higher delay estimation accuracy,and the prediction accuracy could ba im­proved by2.2%compared with the traditional method under diiecnt SNR,indicating proposed alyoCthm presented good anti一noise peiformanco.Keywords TDOA location%adaptive filtering%CC%VSS-LMS%time delay estimation%HilbeC digecncoTDOA定位是无源定位技术发展的主要方向之一⑴，前对TDOA.的研究 :的、的选择和精度的，其测量的精度是精度的一因素&因此，对测量相的研究十&收稿日期：2019-04-08基金项目：十三五装发重点预研项目(41413050304)Equipment Development Key Pre-research Project During13w5-Year Plan Period(41413050304)作者简介:魏文亮(1993-)，男，硕士研究生。

交通行业术语（中英文对照）Stop-line ----- 停车线A con gested link ---- 阻塞路段Weighti ng factor ----- 权重因子Con troller --- 控制器Emissio ns Model ---- 排气仿真the traffic pattern ----- 交通方式Con troller --- 信号机Amber ----- 黄灯Start-up delay ---- 启动延误Lost time ----- 损失时间Off-peak——非高峰期The morni ng peak -- 早高峰Pedestria n crossin ——人行横道Coord in ated con trol systems——协调控制系统On-I ine ---- 实时Two-way ----- 双向交通Absolute Offset ----- 绝对相位差Overlapp ing Phase——搭接相位Critical Phase ----- 关键相位Cha nge Interval --- 绿灯间隔时间Arterial In tersection Con trol 干线信号协调控制Fixed-time Con trol ----- 固定式信号控制Real-time Adaptive Traffic Con trol ---- 实时自适应信号控制Green Ratio ---- 绿信比Through movemen ----- 直行车流Congestion ----- 阻塞，拥挤The perce ntage congestion——阻塞率The degree of saturation——饱和度The effective gree n time --- 有效绿灯时间The maximum queue value——最大排队长度Flow Profiles ------ 车流图示Double cycli ng ----- 双周期Si ngle cycli ng --- 单周期Peak高峰期The eve ning peak periods——晚高峰Siemens --- 西门子Pelican ---- 人行横道Fixed time plans ---- 固定配时方案On e-way traffic ---- 单向交通Green Ratio ---- 绿信比Relative Offset ----- 相对相位差Non-o verlapp ing Phase——非搭接相位Saturatio n Flow Rate -- 饱和流率Isolated In tersecti on Contro -- 单点信号控制（点控）Area-wide Con trol ---- 区域信号协调控制Vehicle Actuated （\A）-- 感应式信号控制The Mi nimum Green Time ---- 最小绿灯时间Unit Exte nsion Time --- 单位绿灯延长时间The Maximum Green Time ---- 最大绿灯时间Oppos ing traffic --- 对向交通（车流）Actuati on ---- Con trol ----- 感应控制方式Pre-timed Control ------ 定周期控制方式Remote Contro ----- 有缆线控方式Self —I nductfa ns --- 环形线圈检测器Signal ----- s pacin -------- 信号间距Though-traffic lane ----- 直行车道Inbound ---- 正向Outbound ---- 反向第一章交通工程--- Traffic Engin eeri ng运输工程--- Tran sportati on Engin eeri ng航空交通--- Air Tran sportati on水上交通--- Water Tran sportati on管道交通--- Pipeli ne Tran sportati on交通系统--- Traffic System交通特性--- Traffic Characteristics人的特性--- Huma n Characteristics车辆特性--- Vehicular Characteristics交通流特性--- Traffic Flow Characteristics道路特性--- Roadway Characteristics交通调查--- Traffic Survey交通流理论--- Traffic Flow Theory交通管理--- Traffic Man ageme nt交通环境保护---- T raffic En vir onment Protecti on 交通设计--- Traffic Desig n交通统计学--- Traffic Statistics交通心理学--- Traffic Psychology汽车力学--- Automobile Mecha nics交通经济学--- Traffic Econo mics汽车工程--- Automobile Engin eeri ng人类工程--- Huma n Engin eeri ng环境工程--- En vir onment Engin eeri ng自动控制--- Automatic Con trol电子计算机Electric Computer第一章公共汽车一Bus无轨电车Trolley Bus有轨电车Tram Car大客车Coach小轿车Seda n载货卡车Truck拖挂车Trailer平板车Flat-bed Truck动力特性一Drivi ng Force Characteristics 牵引力Tractive Force空气阻力Air Resista nee滚动阻力Rolli ng Resista nee坡度阻力Grade Resista nee加速阻力Accelerati on Resista nee附着力一一 Adhesive Force汽车的制动力Braki ng of Motor Vehicle 自行车流特性Bicycle flow Characteristics驾驶员特性Driver Characteristics刺激Stimulation感觉--- Sense判断--- Judgme nt行动--- Action视觉--- Visual Sense听觉--- Heari ng Sense嗅觉--- Se nse of Smell味觉--- Sense of Touch视觉特性--- Visual Characteristics视力--- Visi on视野--- Field of Visio n色彩感觉--- Color Sense眩目时的视力--- Glare Visio n视力恢复--- Retur n Time of Visio n动视力--- Visual in Motion亮度--- Luminance照度--- Luminance反应特性--- Reactive Characteristics刺激信息--- Stimula nt In formati on驾驶员疲劳与兴奋---- Drivi ng Fati ng and Excitability交通量--- Traffic Volume地点车速Spot Speed瞬时车速In sta ntan eous Speed时间平均车速Time mean Speed空间平均车速Space mean speed车头时距Time headway车头间距一一Space headway。

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一种在线时间序列预测的核自适应滤波器向量处理器庞业勇;王少军;彭宇;彭喜元【摘要】针对信息物理融合系统中的在线时间序列预测问题,该文选择计算复杂度低且具有自适应特点的核自适应滤波器(Kernel Adaptive Filter,KAF)方法与FPGA 计算系统相结合,提出一种基于FPGA的KAF向量处理器解决思路.通过多路并行、多级流水线技术提高了处理器的计算速度,降低了功耗和计算延迟,并采用微码编程提高了设计的通用性和可扩展性.该文基于该向量处理器实现了经典的KAF方法,实验表明,在满足计算精度要求的前提下,该向量处理器与CPU相比,最高可获得22倍计算速度提升,功耗降为1/139,计算延迟降为1/26.【期刊名称】《电子与信息学报》【年(卷),期】2016(038)001【总页数】10页(P53-62)【关键词】核自适应滤波器;现场可编程逻辑门阵列;向量处理器;微码【作者】庞业勇;王少军;彭宇;彭喜元【作者单位】哈尔滨工业大学自动化测试与控制研究所哈尔滨150080;哈尔滨工业大学自动化测试与控制研究所哈尔滨150080;哈尔滨工业大学自动化测试与控制研究所哈尔滨150080;哈尔滨工业大学自动化测试与控制研究所哈尔滨150080【正文语种】中文【中图分类】TP3911 引言信息物理融合系统(Cyber-Physical System, CPS)是将计算、通信和控制能力深度融合的网络化物理系统，数据的在线实时处理是CPS的核心问题之一[1]。

而实际物理系统产生的数据往往具有时间序列特性，因此时间序列预测广受工业界和研究机构的关注，越来越多的嵌入式在线时间序列预测系统被广泛地应用到变电站无线监测与预警，可穿戴机器人运动控制以及嵌入式环境监测等领域。

然而，对于在线应用，非线性时间序列预测方法需要不断地加入新样本并且对模型进行更新，导致所需要的计算量大大增加，从而限制了其在CPS等先进智能信息处理系统中的应用。