信号强度与运动传感器融合的智能手机室内定位算法

利用智能手机传感器实现高精度跟踪定位已经成为一个研究热点,本文针对室内定位中由于手机的运动引起采集信号强度不稳定造成的定位误差大的问题,提出了基于信号强度与加速度梯度融合综合的新的测距算法,结合手机方向信息、地图信息、信号强度的分布信息,利用测距信息与地图匹配算法,实现对智能手机的精确定位。在实验测试中,该算法平均定位精度为1.2m, 3.5m以下定位精度达95%。本算法有效的提高了智能手机的室内定位精度,并且相比指纹库定位算法减少了搜索次数,提高了定位速度。     

基于多模光纤强度调制的扰动定位传感器

介绍了一种采用模式耦合原理的强度调制型扰动定位传感器.传感光纤为多模光纤.光在传感光路中沿相反的方向传播,分别由两个探测器接收,通过计算扰动信号到达两个探测器的时间差,可以得到扰动的位置.本文分析了其传感原理、定位原理及影响定位精度的因素,通过采用宽谱光源、数字滤波等方法使定位精度提高到约±100 m.所述方案定位精度高、结构简单、传感距离长,适用于长距离管道监测、安全防卫等各种扰动传感领域.     

基于稀疏表示的雷达辐射源信号级融合识别算法

针对现有融合识别算法难以兼顾信息完备性和节点通信数据量的问题,提出一种基于信号稀疏表示的雷达辐射源信号级融合识别算法。该方法将接收信号投影到稀疏域并进行压缩,从而在稀疏域完成融合,最后利用融合后的稀疏系数进行识别。该方法既降低了通信数据量,又较好地保证了信息的完整性。仿真实验表明,相对于单一传感器和决策级融合,所提出的方法可有效提高信号识别性能。     

基于信息熵的D-S证据理论及其在传感器融合中的应用

从证据本身和证据之间的相互关系两个方面分析了证据的可信度及相应在融合过程中获得的权重.引入了证据信息熵的概念,并给出了从证据本身确定可信度的方法.为了从证据之间的相互关系考察证据的可信度,给出了描述证据间相互支持的模糊关系矩阵,并依此来影响各传感器对于融合数据的重要性.实验验证了所提方法的有效性和鲁棒性.     

A new metric based on extended spatial frequency and its application to DWT based fusion algorithms

A new quantitative metric is proposed to objectively evaluate the quality of fused imagery. The measured value of the proposed metric is used as feedback to a fusion algorithm such that the image quality of the fused image can potentially be improved. This new metric, called the ratio of spatial frequency error (rSFe), is derived from the definition of a previous measure termed “spatial frequency” (SF) that reflects local intensity variation. In this work, (1) the concept of SF is first extended by adding two diagonal SFs, then, (2) a reference SF (SF_R) is computed from the input images, and finally, (3) the error SF (SF_E) (subtracting the fusion SF from the reference SF), or the ratio of SF error (rSFe = SF_E/SF_R), is used as a fusion quality metric. The rSFe (which can be positive or negative) indicates the direction of fusion error—over-fused (if rSFe > 0) or under-fused (if rSFe < 0). Thus, the rSFe value can be back propagated to the fusion algorithm (BP fusion), thereby directing further parameter adjustments in order to achieve a better-fused image. The accuracy of the rSFe is verified with other quantitative measurements such as the root mean square error (RMSE) and the image quality index (IQI), as well as with a qualitative perceptual evaluation based on a standard psychophysical paradigm. An advanced wavelet transform (aDWT) method that incorporates principal component analysis (PCA) and morphological processing into a regular DWT fusion algorithm is implemented with two adjustable parameters—the number of levels of DWT decompositions and the length of the selected wavelet. Results with aDWT were compared to those with a regular DWT and with a Laplacian pyramid. After analyzing several inhomogeneous image groups, experimental results showed that the proposed metric, rSFe, is consistent with RMSE and IQI, and is especially powerful and efficient for realizing the iterative BP fusion in order to achieve a better image quality. Human perceptual assessment was measured and found to strongly support the assertion that the aDWT offers a significant improvement over the DWT and pyramid methods.     

An upper bound for the recursive least squares estimation error

A new upper bound for the convergence rate of recursive least squares (RLS) errors is presented. The bound is free of some deficiencies of a cell-known RLS upper bound and allows a realistic assessment of factors influencing convergence rate, such as input-output data scaling, disturbances, signal-to-noise ratio, number of estimated parameters, data discounting, and excitation properties of plant inputs. Some of the properties of the new bound are discussed     

无线传感器网络中协作误差传播感知的机会可靠传输机制

针对无线传感器网络协作通信过程中误差传播对系统性能的影响,提出了一种基于机会式误差修正的可靠传输机制。首先,在多节点协作无线传感器网络中建立三种协作传输方案,基于信道质量和误符号率建立协作误差传播模型。接着,针对协作误差中的合并、调度和干扰等类型,通过在信道、信源和协作网络物理层提出机会式误差修正算法。最后,在上述结论基础上,综合感知用户的性能要求、调度复杂度和协作传播模型,提出了机会式可靠传输机制。数学分析结果不仅验证了机会式传输机制比静态机制具有高可靠性,而且证明了所提机制在端到端通信的实时性、可靠性、吞吐率和能效等方面有优越性。     

Simulation and sensor data fusion for machine learning application

The performance of machine learning algorithms depends to a large extent on the amount and the quality of data available for training. Simulations are most often used as test-beds for assessing the performance of trained models on simulated environment before deployment in real-world. They can also be used for data annotation, i.e, assigning labels to observed data, providing thus background knowledge for domain experts. We want to integrate this knowledge into the machine learning process and, at the same time, use the simulation as an additional data source. Therefore, we present a framework that allows for the combination of real-world observations and simulation data at two levels, namely the data or the model level. At the data level, observations and simulation data are integrated to form an enriched data set for learning. At the model level, the models learned from observed and simulated data separately are combined using an ensemble technique. Based on the trade-off between model bias and variance, an automatic selection of the appropriate fusion level is proposed. Our framework is validated using two case studies of very different types. The first is an industry 4.0 use case consisting of monitoring a milling process in real-time. The second is an application in astroparticle physics for background suppression.     

An error bound for approximate solutions of two-point boundary value problems

A new error bound for any approximate solutionu of the two-point boundary value problemAy:=?(py′)′+qy=f,y(0)=0, y(1)=0, is proposed. This error bound depends on the deviationAu?fjust like the one which is proportional to ‖Au?f‖₂, but in the case of Ritz-Galerkin approximations by cubic splines it behaves asymptotically likeh ³, whereh is the knot distance, i.e., it is by one order of magnitude better. An important advantage of this error bound is that it can be used even in the case of generalized solutions and of piecewise linear approximations. An error bound for the approximation of the derivative results also from these considerations. This error bound behaves in the above case asymptotically also likeh ³, i.e. it has the same asymptotic behaviour as the actual approximation error of the derivative.     

An efficient sensor quantization algorithm for decentralized estimation fusion

In this paper, we consider the design problem of optimal sensor quantization rules (quantizers) and an optimal linear estimation fusion rule in bandwidth-constrained decentralized random signal estimation fusion systems. First, we derive a fixed-point-type necessary condition for both optimal sensor quantization rules and an optimal linear estimation fusion rule: a fixed point of an integral operation. Then, we can motivate an iterative Gauss–Seidel algorithm to simultaneously search for both optimal sensor quantization rules and an optimal linear estimation fusion rule without Gaussian assumptions on the joint probability density function (pdf) of the estimated parameter and observations. Moreover, we prove that the algorithm converges to a person-by-person optimal solution in the discretized scheme after a finite number of iterations. It is worth noting that the new method can be applied to vector quantization without any modification. Finally, several numerical examples demonstrate the efficiency of our method, and provide some reasonable and meaningful observations how the estimation performance is influenced by the observation noise power and numbers of sensors or quantization levels.     

基于传感器最优配置的等价空间故障检测方法

提出一种基于传感器最优配置的等价空间故障检测方法．针对线性时不变动态系统,给出了传感器最优配置问题的描述,以及基于传感器最优配置的故障检测多目标优化问题的描述．在系统可供测量的N个变量中,选择能获得关于系统故障尽可能多信息的m个变量作为测量变量,在满足对故障具有尽可能高的灵敏度,同时对扰动等未知信号具有尽可能强的鲁棒性的条件下,使整个系统测量成本达到最低．仿真算例说明了所提出设计方法及算法的有效性．     

基于ARMA的微惯性传感器随机误差建模方法

针对微惯性传感器随机误差建模效果不理想,影响微惯性组合导航系统性能的问题,提出了采用自回归滑动平均(ARMA)对微惯性传感器随机误差进行建模的方法。通过对随机误差模型应用于微惯性器件误差建模的深入分析,将Yule-Walker方程引入线性预测问题中,实现AR功率谱密度的估计,建立了基于随机过程有理功率谱密度的ARMA模型建立方法,并给出了ARMA建模准确性的LDA验证准则。通过微惯性传感器实测数据,对随机误差建模方法进行了有效性验证。该方法为微惯性器件的随机误差建模和分析提供了一种新的途径。     

An error bound for infinite approximations of queueing networks with large finite stations under RSRD protocol

Queueing networks with stations having finite but relatively large buffers are studied. The protocol is the repetitive service random destination (RSRD) protocol. A simple infinite product approximation is suggested to evaluate the system. An analytic error bound is established for the accuracy of this approximation. This error bound is shown to be of the order of the steady state probability to exceed the buffer limits computed by product form expressions for the infinite system. This probability can often be thought as being quite small and thus provides a practical error bound. Some numerical support is provided. To establish the error bound, a new method consisting of introducing an intermediate model is used. This approach seems of interest for wider application to obtain error bounds.     

基于传感器技术的可穿戴式设备信号频率测量

利用信号需求分析结果,选取待处理的规范指标信号,评价算子的计算说明,完成信号质量评价。在此基础上,通过调整传感器偏振信号、计算测量标准具的方式,对可穿戴式设备信号的频率间隔进行调整分析,完成可穿戴式设备信号频率测量方案搭建。相同物理平台上进行实验,与QT方法相比,应用新型信号频率测量方案后,抗电磁干扰免疫水平得到有效提升,检测带宽占比最大值不超过55%。     

An application adaptation layer for wireless sensor networks

In wireless sensor networks, poor performance or unexpected behavior may be experienced for several reasons, such as trivial deterioration of sensing hardware, unsatisfactory implementation of application logic, or mutated network conditions. This leads to the necessity of changing the application behavior after the network has been deployed. Such flexibility is still an open issue as it can be achieved either at the expense of significant energy consumption or through software complexity. This paper describes an approach to adapt the behavior of running applications by intercepting the calls made to the operating system services and changing their effects at run-time. Customization is obtained through small fragments of interpreted bytecode, called adaptlets, injected into the network by the base station. Differently from other approaches, where the entire application is interpreted, adaptlets are tied only to specific services, while the bulk of the application is still written in native code. This makes our system able to preserve the compactness and efficiency of native code and to have little impact on the overall application performance. Also, applications must not be rewritten because the operating system interfaces are unaffected. The adaptation layer has been implemented in the context of TinyOS using an instruction set inspired to the Java bytecode. Examples that illustrate the programming of the adaptation layer are presented together with their experimental validation.     

基于扫频激振技术的单线圈振弦式传感器

介绍一种用扫频激振技术实现激振单线圈振弦式传感器的新方法 ,它与传统的激振方法相比 ,具有硬件电路简单、激振可靠、激振频率可控等优点。     

一种基于地理位置的无线传感器网络分簇路由算法

针对大规模无线传感器网络,提出了一种基于地理位置的双基站分簇路由算法。该算法在网络覆盖区域边缘设置两个基站,按照地理位置将区域划分为若干均匀分布网格。每个网格根据节点剩余能量和到网格内其它节点平均距离远近选择簇头。通过仿真分析,证明该算法能减少网络能耗,延长网络生存时间。     

An upper bound on the mean-square error in the sampling expansion for non-band-limited processes

An upper bound is obtained on the mean-square error involved when a real-valued non-band-limited nonstationary random process x(t) is approximated by the sampling expansion

$\text{∑}\text{n=?∞}\text{∞}\text{x(nT)}\text{sin}\text{π(t?nT)/T}\text{π(t?nT)/T}$

for some T > 0. When the process x(t) is band-limited over [$\text{?}\text{1}\text{2T}\text{,}\text{1}\text{2T}$], this error bound reduces to zero.