多重频解模糊中降低频道量化误差的新方法

针对工程应用中采用重频参差法解脉冲多普勒雷达的速度模糊存在频道量化误差问题,提出了一种提高频率估计精度的综合方法。该方法首先将目标所在距离单元相参积累后的频域数据,通过傅里叶反变换为原始数据,然后补零FFT,在此基础上运用二次函数拟合,进一步提高频率估计精度。它有效结合了补零FFT和二次函数拟合两种方法的优点,弥补了二者的不足。仿真结果表明,该方法在运算量不大的情况下,即可很好地降低频道量化误差。     

Power scheduling of universal decentralized estimation in sensor networks

We consider the optimal power scheduling problem for the decentralized estimation of a noise-corrupted deterministic signal in an inhomogeneous sensor network. Sensor observations are first quantized into discrete messages, then transmitted to a fusion center where a final estimate is generated. Supposing that the sensors use a universal decentralized quantization/estimation scheme and an uncoded quadrature amplitude modulated (QAM) transmission strategy, we determine the optimal quantization and transmit power levels at local sensors so as to minimize the total transmit power, while ensuring a given mean squared error (mse) performance. The proposed power scheduling scheme suggests that the sensors with bad channels or poor observation qualities should decrease their quantization resolutions or simply become inactive in order to save power. For the remaining active sensors, their optimal quantization and transmit power levels are determined jointly by individual channel path losses, local observation noise variance, and the targeted mse performance. Numerical examples show that in inhomogeneous sensing environment, significant energy savings is possible when compared to the uniform quantization strategy.     

LFMCW雷达测距中的自适应频率校正

信号的频率落在两个量化频点之间时会产生估计误差,所以需要校正快速傅里叶变换(FFT)后估计的频率.结合Rife算法和抛物线插值法的特点,提出了一种自适应频率校正算法.该算法依据不同的频率自适应地选择不同的频率校正算法,克服了Rife算法在信号的频率落在FFT量化频点周围的情况下容易产生插值方向错误的缺点,同时避免了抛物线插值法在信号频率落在相邻两个量化频点的中心附近时估计均方误差会急剧增加的缺点.仿真结果验证了所提算法的有效性.此外,相比于单一的Rife算法和抛物线插值法,所提算法在频率估计精度以及抗噪声能力方面都有显著提升.     

基于分簇结构无线传感器网络的高效无源定位方法

针对基于节点通信能力和能量受限的无线传感器网络实现高精度无源定位的问题,首先,在分簇结构网络中,通过折中单个簇的TDOA定位精度和运算复杂度,确定了簇规模;其次,基于直达波环境中TDOA定位误差是按布站GDOP对测量误差放大的原理,提出第一轮定位先使用网内所有节点以RSSI定位方法粗估计目标辐射源位置,并根据各簇在该粗估计位置处的布站GDOP和测量误差估算TDOA定位标准差,第二轮定位选择具有较低TDOA定位估计标准差的部分簇参与TDOA定位,最后将这些簇的TDOA定位结果按估算的定位估计标准差加权平均,求得最终定位解。仿真结果证明该方法有效的去除了冗余节点,实现以半数节点接近使用全部节点的定位精度。      

Kalman Filtering with Optimally Scheduled Measurements in Bandwidth Limited Communication Media

A method is proposed for scheduling sensor accesses to the shared network in a networked control system. The proposed method determines the access order in which the sensors are granted medium access through minimization of the state estimation error covariance. Solving the problem by evaluating the error covariance for each possible ordered set of sensors is not practical for large systems. Therefore, a convex optimization problem is proposed, which yields approximate yet acceptable results. A state estimator is designed for the augmented system resulting from the incorporation of the optimally chosen communication sequence in the plant dynamics. A car suspension system simulation is conducted to test the proposed method. The results show promising improvement in the state estimation performance by reducing the estimation error norm compared to round‐robin scheduling.     

A Motion Picture Coding Algorithm Using Adaptive DCT Encoding Based on Coefficient Power Distribution Classification

A motion picture coding algorithm using motion-compensated interframe prediction and the adaptive discrete cosine transform (DCT) encoding technique is proposed. High coding efficiency is obtained by the adaptive DCT encoding technique in which encoding parameters are fitted to widely varying characteristics of the interframe differential signal. Segmented DCT subblocks of interframe prediction error are classified into categories based on their coefficient power distribution characteristics. The adaptation gain results from using a suitable variable word length code set designated by the above classification for encoding each quantization index of DCT coefficients. In addition, a new coding parameter control method is introduced based on the information rate estimation of the current frame. This classification promotes high stability because good estimation accuracy of bits consumption for each DCT subblock is obtained by utilizing the category indexes. Simulation results show that the proposed algorithm has enough coding efficiency to transmit videoconferencing motion pictures through a 384 kbit/s channel.     

Distributed Estimation in Energy-Constrained Wireless Sensor Networks

In this paper, we consider distributed estimation of a noise-corrupted deterministic parameter in energy-constrained wireless sensor networks from energy-distortion perspective. Given a total energy budget allowable to be used by all sensors, there exists a tradeoff between the subset of active sensors and the energy used by each active sensor in order to minimize the estimation MSE. To determine the optimal quantization bit rate and transmission energy of each sensor, a concept of equivalent unit-energy MSE function is introduced. Based on this concept, an optimal energy-constrained distributed estimation algorithm for homogeneous sensor networks and a quasi-optimal energy-constrained distributed estimation algorithm for heterogeneous sensor networks are proposed. Moreover, the theoretical energy-distortion performance bound for distributed estimation is addressed and it is shown that the proposed algorithm is quasi-optimal within a factor 2 of the theoretical lower bound. Simulation results also show that the proposed method can achieve a significant reduction in the estimation MSE when compared with other uniform schemes. Finally, the proposed algorithm is easy to implement in a distributed manner and it adapts well to the dynamic sensor environments.     

Asymptotic design of quantization and bit allocation for distributed estimation in wireless sensor networks

This paper investigates the schemes of asymptotically optimal quantization and bit allocation for distributed estimation in wireless sensor networks in which a total bit rate constraint is imposed. Because there is no communication among all sensors, quantizers for observations need to be designed separately. The Lloyd‐Max quantizer is shown to be asymptotically optimal for each sensor. Moreover, it is shown that the level number of asymptotically optimal quantization for sensors is proportional to the signal‐to‐noise ratio, which is determined by the variances of observation and noise. Because the original quantized minimum mean‐square error estimator often corresponds to a high computational cost when a large number of sensors are active, in our work, an asymptotically equivalent algorithm of iterative quantized estimator (IQE), which enjoys a low computational cost, is proposed. In addition, an IQE algorithm can be applied to wireless sensor networks with delay or packet loss. Simulation results of this work indicate that the effectiveness of our IQE algorithm is obvious; that is, a significant improvement of estimation performance is achieved by using the optimal bit allocation when comparing with the uniform bit allocation. Copyright © 2016 John Wiley & Sons, Ltd.     

An isotropic universal decentralized estimation scheme for a bandwidth constrained ad hoc sensor network

Consider a decentralized estimation problem whereby an ad hoc network of K distributed sensors wish to cooperate to estimate an unknown parameter over a bounded interval [-U,U]. Each sensor collects one noise-corrupted sample, performs a local data quantization according to a fixed (but possibly probabilistic) rule, and transmits the resulting discrete message to its neighbors. These discrete messages are then percolated in the network and used by each sensor to form its own minimum mean squared error (MMSE) estimate of the unknown parameter according to a fixed fusion rule. In this paper, we propose a simple probabilistic local quantization rule: each sensor quantizes its observation to the first most significant bit (MSB) with probability 1/2, the second MSB with probability 1/4, and so on. Assuming the noises are uncorrelated and identically distributed across sensors and are bounded to [-U,U], we show that this local quantization strategy together with a fusion rule can guarantee a MSE of 4U/sup 2//K, and that the average length of local messages is bounded (no more than 2.5 bits). Compared with the worst case Cramer-Rao lower bound of U/sup 2//K (even for the centralized counterpart), this is within a factor of at most 4 to the minimum achievable MSE. Moreover, the proposed scheme is isotropic and universal in the sense that the local quantization rules and the final fusion rules are independent of sensor index, noise distribution, network size, or topology. In fact, the proposed scheme allows sensors in the network to operate identically and autonomously even when the network undergoes changes in size or topology.     

Practical robust uniform channel decomposition scheme for CSI mismatch in limited feedback MIMO systems

Uniform channel decomposition (UCD) has been proven to be optimal in bit error rate (BER) performance and strictly capacity lossless when perfect channel state information (CSI) is assumed to be available at both the transmitter and receiver side. In practice, CSI can be obtained by channel estimation at receiver and conveyed to transmitter via a limited-rate feedback channel. In such case, the implementation of traditional UCD by treating the imperfect CSI as perfect CSI cause significant performance degradation due to inevitable channel estimation error and vector quantization error. To overcome this problem, a practical robust UCD scheme was proposed in this paper, which includes two steps, firstly, a matching architecture was proposed to eliminate the mismatch between CSI at receiver (CSIR) and CSI at transmitter (CSIT), secondly, an MMSE based robust UCD scheme considering channel estimation error and vector quantization error as an integral part of the design was derived. Simulation results show that the proposed practical robust UCD scheme is capable of improving the BER performance greatly in the context of channel estimation error and vector quantization error compared with the traditional UCD scheme.     

双模态信息融合的飞行目标位姿估计方法

针对在飞行目标位姿估计中背景复杂、目标提取及位姿解算精度低、速度实时性差等问题,提出了一种激光与图像信息融合的飞行目标位姿估计方法。首先,建立彩色相机和激光雷达间的坐标变换模型实现两传感器像素级匹配,对同一时刻的图像和点云进行融合处理;其次,采用Vi Be算法与深度信息融合对图像中运动目标进行提取,并根据图像目标的位置框选出对应的点云;最后,利用PnP算法进行特征点粗配准,获取点云间初始旋转平移矩阵,并采用迭代最近点算法进行精配准,利用IK-D Tree加速临近点搜索,提高配准速度。使用仿真试验和半实物仿真试验对方法准确性和稳定性进行了验证,结果显示：二维图像目标检测算法正确率为97%,错误分类比为0.011 2%;位姿估计算法与传统迭代最近点算法相比精度提高了53.2%,单次耗时从261 ms降低至132 ms,效率提升约49.4%,与其他算法相比也具有一定优势。为飞行目标落点精准预测和制导控制提供解决思路。     

Genetic Algorithm-based Adaptive Optimization for Target Tracking in Wireless Sensor Networks

In this paper, we address the problem of genetic algorithm optimization for jointly selecting the best group of candidate sensors and optimizing the quantization for target tracking in wireless sensor networks. We focus on a more challenging problem of how to effectively utilize quantized sensor measurement for target tracking in sensor networks by considering best group of candidate sensors selection problem. The main objective of this paper is twofold. Firstly, the quantization level and the group of candidate sensors selection are to be optimized in order to provide the required data of the target and to balance the energy dissipation in the wireless sensor network. Secondly, the target position is to be estimated using quantized variational filtering (QVF) algorithm. The optimization of quantization and sensor selection are based on the Fast and Elitist Multi-objective Genetic Algorithm (NSGA-II). The proposed multi-objective (MO) function defines the main parameters that may influence the relevance of the participation in cooperation for target tracking and the transmitting power between one sensor and the cluster head (CH). The proposed algorithm is designed to: i) avoid the problem lot of computing times and operation counts, and ii) reduce the communication cost and the estimation error, which leads to a significant reduction of energy consumption and an accurate target tracking. The computation of these criteria is based on the predictive information provided by the QVF algorithm. The simulation results show that the NSGA-II -based QVF algorithm outperforms the standard quantized variational filtering algorithm and the centralized quantized particle filter.     

Rejection of Limit Cycles Induced From Disturbance Observers in Motion Control

A disturbance observer (DOB) is generally introduced into motion control systems to eliminate the unwanted disturbance and plant uncertainty. Due to the quantization error from the sensors and D/A converter, DOB-based controller might generate limit cycles which severely decreases positioning accuracy as the system reaches steady-state positioning. This paper presents a sufficient condition for removing limit cycles and explores the condition to design controller parameters. In addition, since the parameters based on the condition make the system sluggish, an adaptive mechanism is introduced not only to maintain the system performance but also to eliminate limit cycles. The experimental results validate the analytical results and also illustrate the effectiveness of the proposed method     

基于SOM聚类的台风云型模式发现

Dvorak等人提出的基于卫星云图的云型和云系特征的热带气旋强度估计方法已被世界气象组织推荐给全球使用.本文尝试从历史数据中自动发现典型的云型模式,实现Dvorak模板图像的自动选取、匹配和识别.采用SOM对12000多幅红外云图进行聚类,采用提出的局部统计信息等特征,分析了某些距离作为相似性度量存在而只能发现球形簇的缺点;对不同的特征和相似性度量进行了对比试验,并分析了SOM拓扑误差和量化误差.从实验结果可以看出,局部熵特征有着最小的量化误差,但聚类准确度较低.原始图像作为输入特征时,有着较高的聚类准确度及拓扑保持度.局部统计信息特征比局部熵特征量化误差大,但有着更高的聚类准确度.这些结论为采用无监督聚类方法来发现云型模式并找到最佳的特征和较好的相似性度量以取得更好的结果提供了重要的参考,也有助于避免目前云图自动化分析研究中对特征和度量选取的随意性.     

智能天线中一种新的三维波束形成方法

提出了用分布于 x?y?z 三轴上的三个阵元估计扩频信号各条多径的来波方向(DOA)的方法,以及用由 x?y 均匀平面圆阵和 z 轴直线阵组成的阵列天线形成 3 维波束的方法。根据 CDMA 信号模型和多径信道模型,用 最大似然(ML)法分别估计出信号到达 3 个阵元的复信道响应,并利用复信道响应的幅角来估计各条多径信号 的 DOA;然后在已有最小均方误差(MMSE)法和最小二乘(LS)法的基础上生成带限制条件的扩展 MMSE(EMMSE)法形成 3 维波束。模拟表明,给出的 DOA 估计方法具有很高的精度,而 EMMSE 方法形成的波束主 瓣窄,旁瓣被抑制,有很高的输出信干噪比。     

Statistical Leakage Estimation of Double Gate FinFET Devices Considering the Width Quantization Property

This paper presents a statistical leakage estimation method for FinFET devices considering the unique width quantization property. Monte Carlo simulations show that the conventional approach underestimates the average leakage current of FinFET devices by as much as 43% while the proposed approach gives a precise estimation with an error less than 5%. Design example on subthreshold circuits shows the effectiveness of the proposed method.     

基于24位置的MEMS惯性传感器快速标定方法

针对微惯性测量单元原始输出信息受零偏、标度因数、非正交误差等误差项干扰影响测量精度的问题,提出一种无需借助高精度转台的MEMS IMU快速原位标定方案。在分析MEMS惯性传感器输出特性的基础上建立传感器误差模型,利用六面体夹具设计IMU 24位置连续转停标定方案,以重力及各次旋转角度为参考信息完成传感器误差标定。针对加速度计零偏、标度因数、非正交误差9个参数构造标定模型,采用牛顿法估计误差参数最优值,考虑陀螺仪零偏与标度因数6个误差参数,利用最小二乘法计算误差参数最优估值。分别进行加速度计、陀螺标定补偿实验,实验结果表明,提出的MEMS IMU快速原位标定方法能快速得到传感器误差参数,提高了输出数据精度。     

矿井旋转机组碰摩声发射源监测系统的设计

研究提出了一种基于声发射源特征识别的矿井旋转机组碰摩故障检测方法。为了能对矿井旋转机组实时远程监控并实现分布式网络化管理,设计了一种基于ARM嵌入式系统的矿井旋转机组振动监测系统。针对高斯混合模型在建模时需要较多的训练数据的缺陷,提出了一种基于模糊矢量量化混合模型的声发射识别方法,该方法综合考虑了模糊集理论、矢量量化和高斯混合模型的优点,通过用模糊矢量量化误差尺度取代传统高斯混合模型的输出概率函数,减少了建模时对训练数据量的要求,提高了模型精度和识别速度。通过实验观察上位机输出结果,验证了监测数据的实时性和准确性,达到了对旋转机组运行的状态信息实时监测和故障诊断的要求。     

Accurate and time efficient estimation of the probability of error in bursty channels

A method and a technique for the probability of error estimation in digital channels with memory are developed and demonstrated. The expressions for the mean and variance of a random variable, representing a block of bits transmission in a bursty channel (channel with memory), are derived. The influence of the memory is expressed by a parameter called the memory factor. It is shown that the traditional Monte Carlo method can be applied for the probability of error estimation. In order to control the accuracy and increase the time efficiency of estimation this method is modified and a new method, called the modified Monte Carlo method, is proposed. Based on this modified method a technique of estimation with controlled accuracy is developed and demonstrated using data obtained by simulation. According to this technique the sample size is adjusted in the course of estimating procedure to give an accurate estimate of the probability of error for a minimum required time of estimation. Copyright © 2003 John Wiley & Sons, Ltd.     

基于微气压传感器的室内行人高度估计方法

针对当前气压传感器解算高度存在误差随时间累积的问题,提出了一种基于微气压传感器的室内行人高度估计新方法。该方法结合微气压传感器的输出高度值,对多个相邻时间内的高度值进行合理性判断,并结合室内行人运动状态对高度变化进行约束,使得解算的高度符合室内行人运动规律。通过电脑采集MS5611微气压传感器数据进行仿真验证,结果表明:该方法能够有效地对高度误差进行修正,相较于微气压传感器直接解算的高度,该算法解算的高度更贴合实际情况,解算轨迹也更接近真实轨迹,轨迹的平均闭环误差从1.9%D下降至0.07%D。该算法有效地提高了室内行人高度估计的定位精度,在室内行人三维定位领域具有一定的工程应用价值。