基于高斯拟合与切比雪夫不等式的标签数量二次估计算法

针对射频识别技术(RFID)系统中现有标签数量估计算法存在的估计误差大、识别时延长、时间复杂度高的问题,该文提出一种基于高斯拟合与切比雪夫不等式的标签数量2次估计算法(TLNEGC).首先根据碰撞因子与碰撞时隙比例的关系建立碰撞模型,采用高斯函数对碰撞模型中的离散数据点进行拟合逼近获得高斯估计模型;然后利用高斯估计模型初次估计标签的数量,根据初次估计的结果判断是否需要进行2次估计,2次估计是利用切比雪夫不等式对估计区间进行2次搜索以获得最佳估计值.MATLAB仿真分析表明,该文所提TLNEGC算法的平均估计误差和总时间消耗明显低于现有的高精度标签估计算法,同时具有较低的时间复杂度和较高的稳定性.     

一种基于不等长时隙的射频识别防碰撞算法

该文提出了一种基于不等长时隙的射频识别(Radio Frequency Identification, RFID)动态帧时隙ALOHA (Dynamic Framed-Slotted ALOHA, DFSA)防碰撞算法。算法考虑到大量碰撞时隙和空闲时隙对系统效率的影响,采用帧内时隙长度不等的优化策略,由时隙优化参数和未读标签数确定帧长,通过优化的切比雪夫不等式法进行标签估计,并基于马尔科夫链分析标签识别过程,来实现读取周期的控制。分析和仿真结果表明,该算法比时隙优化前的DFSA算法效率更高,平均识别时间更短,标签数估计比下限值法、Schoute法和碰撞率法更准确。     

一种新型的快速标签估计算法

标签估计是RFID系统中的关键技术之一.常规的标签估计算法通常要查询所有时隙的标签响应情况.在标签数量较大时会极大地增加通信负荷和时间损耗.提出一种快速标签估计算法,判断每一帧前4个时隙的标签碰撞情况,就可以对Q参数取值做快速调整,随后通过查询少量时隙就可以得到标签估计数目.仿真结果表明,与传统的标签估计算法相比,本文算法具有估计速度快、估计误差小等优点,非常适用于需要快速做出估计的场合.     

基于TD-SCDMA系统的快速频率估计算法

提出了一种应用于TD-SCDMA系统中的快速频率估计算法.这种算法利用训练序列估计信道冲击响应,恢复不受信道干扰的发送信号;然后利用最大似然估计算法和二分数值估计算法,估计出较为准确的频率偏移值.仿真表明,该算法比常用的频率估计算法具有更低的复杂度,而且在高斯白噪(AWGN)和多径衰落环境下,均能够对TD-SCDMA系统中的频率偏移获得较为准确的估计结果.     

基于拟合的莱斯因子估计算法

针对现有的莱斯因子矩估计算法适用性低和复杂度大的问题，提出一种基于拟合的改进矩估计算法，以提高莱斯因子的估计效率。改进的算法首先将矩估计模型进行有理函数近似，然后对近似式求逆，得到两个关于莱斯因子的闭合式。仿真结果表明，改进后的算法在样本为50000和1000时，估计效率提高至少2.5倍；在保证估计精度的情况下，拥有更小的时间复杂度，可用在终端资源有限的无线通信场景中。     

基于标签识别码分组的连续识别防碰撞算法研究

标签碰撞增加了射频识别(RFID)系统的时间开销和无源标签的能量消耗,降低了识别速率。该文提出了一种适用于标签识别码连续的防碰撞算法UIG算法,该算法首先根据公司编码和产品编码将所有标签分组,再由产品序列号的碰撞信息生成每组的两个初始标签识别码。最后,通过对初始标签识别码分别连续减1和加1识别出所有标签。性能分析和仿真结果显示,该算法在时间复杂度和通信复杂度上都有很大改善,吞吐率得到了大大的提高。     

一种新的OFDM采样频偏盲估计算法

针对OFDM技术对系统的时钟同步要求非常高的问题,提出了一种新的采样频偏盲估计算法,该算法可较好地解决估计精度和估计复杂度的折中问题。通过仿真结果表明,在高斯白噪声信道下,与同类算法相比,该算法不仅估计精度高,而且估计复杂度低,符合实际工程应用。     

一种有效的MIMO-OFDM系统MAP信道估计

对于MIMO-OFDM系统,最大后验概率(MAP)信道估计算法可通过期望最大化(EM)算法降低计算复杂度,但将产生误差平底(error floor)现象。并且,系统的数据传输效率受限于发送端天线的数目。针对这些问题,该文提出了一种有效的MAP信道估计算法,并分析了算法的性能。所提算法在利用EM算法减小MAP 算法计算复杂度的基础上,利用角域内信道间的独立性降低估计误差。为改善系统数据传输效率及估计性能,通过多个OFDM符号进行联合的信道估计。仿真实验验证了所提算法拥有更好的估计性能和数据传输效率。     

改进型帧时隙ALOHA防碰撞算法研究

为进一步提高RFID系统中电子标签防碰撞算法的识别效率,对帧时隙ALOHA防碰撞算法的性能进行分析,提出一种结合精确标签估计和二进制搜索的改进型帧时隙ALOHA算法.将识别过程分为标签估计和标签识别两个阶段,在标签估计算法中引入碰撞概率上、下限参数,并精确估计标签数量对初始帧时隙大小进行优化;在标签识别阶段,利用二进制搜索算法对时隙内的碰撞标签进行快速识别.通过对识别过程进行仿真结果表明:改进的算法改善了防碰撞性能,提高了RFID系统的标签识别效率.     

利用DFT和迭代校正的正弦信号频率估计算法

对电磁信号的频率估计广泛应用于通信、雷达、导航和电子对抗等领域。针对正弦信号的频率精确估计,本文提出了一种利用DFT和迭代校正的频率估计算法,并与现有三种基于DFT的频率估计算法进行了性能仿真对比,分析结果表明,新算法的频率估计性能明显优于其他三种算法,可以得到非常逼近CRLB的频率估计值,迭代校正5次时,频率估计的RMSE距离CRLB不到0.07dB,而且没有估计误差平层,算法估计精度高,对频率的取值范围不敏感,性能稳定,迭代校正的复杂度较低,具有很好的应用价值。      

基于高斯拟合的相干激光雷达风速估计算法

分别利用高斯拟合估计算法（Gaussian fitting estimation algorithm,以下简称Gauss估计算法）和最大似然（Maximum Likelihood,ML）离散谱峰值（Discrete Spectral Peak,DSP）估计算法（ML DSP）处理实测回波信号,计算得到风速扰动的功率谱密度（Power Spectral Density,PSD）。根据Kolmogorov湍流理论中PSD与频率的-5/3关系,比较不同距离门下的PSD,采用高频区域的风速误差作为风速估计性能参数,分析比较不同距离情况下风速误差,并利用自相关系数分析风速时间变化的相关性。结果表明:在距离较低的探测区域Gauss估计算法的风速误差微弱小于对应的ML DSP估计算法,二者之间的风速误差差值最多不超过0.05 m/s。而在距离较高的区域,两种算法的风速误差差值从820 m处的0.06 m/s增加至1 200 m的0.16 m/s。在风速的时间相关性分析上,Gauss估计算法的风速时间自相关系数明显大于对应的ML DSP估计算法,说明Gauss估计算法处理的风速数据更具有稳定性。     

Time discretization of continuous-time filters and smoothers forHMM parameter estimation

In this paper we propose algorithms for parameter estimation of fast-sampled homogeneous Markov chains observed in white Gaussian noise. Our algorithms are obtained by the robust discretization of stochastic differential equations involved in the estimation of continuous-time hidden Markov models (HMM's) via the EM algorithm. We present two algorithms: the first is based on the robust discretization of continuous-time filters that were recently obtained by Elliott to estimate quantities used in the EM algorithm; the second is based on the discretization of continuous-time smoothers, yielding essentially the well-known Baum-Welch re-estimation equations. The smoothing formulas for continuous-time HMM's are new, and their derivation involves two-sided stochastic integrals. The choice of discretization results in equations which are identical to those obtained by deriving the results directly in discrete time. The filter-based EM algorithm has negligible memory requirements; indeed, independent of the number of observations. In comparison the smoother-based discrete-time EM algorithm requires the use of the forward-backward algorithm, which is a fixed-interval smoothing algorithm and has memory requirements proportional to the number of observations. On the other hand, the computational complexity of the filter-based EM algorithm is greater than that of the smoother-based scheme. However, the filters may be suitable for parallel implementation. Using computer simulations we compare the smoother-based and filter-based EM algorithms for HMM estimation. We provide also estimates for the discretization error     

An empirical Bayes estimator for in-scale adaptive filtering

A scale-adaptive filtering scheme is developed for underspread channels based on a model of the linear time-varying channel operator as a process in scale. Recursions serve the purpose of adding detail to the filter estimate until a suitable measure of fidelity and complexity is met. Resolution of the channel impulse response associated with its coherence time is naturally modeled over the observation time via a Gaussian mixture assignment on wavelet coefficients. Maximum likelihood, approximate maximum a posteriori (MAP) and posterior mean estimators, as well as associated variances, are derived. Doppler spread estimation associated with the coherence time of the filter is synonymous with model order selection and a MAP estimate is presented and compared with Laplace's approximation and the popular AIC. The algorithm is implemented with conjugate-gradient iterations at each scale, and as the coherence time is recursively decreased, the lower scale estimate serves as a starting point for successive reduced-coherence time estimates. The algorithm is applied to a set of simulated sparse multipath Doppler spread channels, demonstrating the superior MSE performance of the posterior mean filter estimator and the superiority of the MAP Doppler spread stopping rule.     

基于Golay互补序列的压缩感知稀疏信道估计算法

该文针对现有基于压缩感知的信道估计方法峰均功率比高、计算量大等问题,使用确定性格雷(Golay)序列作为训练序列对稀疏信道进行信道估计,在接收端实现了对信道冲激响应的估计,给出了估计模型和具体的算法推演,推导了该方法的峰均功率比,并与基于随机高斯序列的压缩感知信道估计方法的性能、峰均功率比和计算量进行了比较。仿真实验表明:格雷序列以及随机高斯序列两种序列都可以重构出稀疏信道非零抽头系数,但是格雷序列对稀疏信道冲激响应的确定性观测估计值的均方误差(MSE)和匹配度性能(Match Rate, MR)均优于随机高斯序列,计算量降低了许多,且在OFDM系统中峰均功率比大大降低。     

Collision and Fade Localization within Packets for Wireless LANs

In this paper, we derive an algorithm for estimating the location of events which cause burst errors, such as packet collisions and multipath fades in wireless packets. Effective localization algorithms have a range of applications, and could lead to network performance improvements through techniques such as error control coding and cross-layer design between the physical and medium access control layers. Given that a collision or fade occurs in a packet, the algorithm finds the maximum-likelihood estimate of the start time and duration of the collision or fade. Due to the computational complexity of this optimal estimate, we also derive an efficient sub-optimal algorithm which decouples the estimation of the collision or fade duration from the estimation of its starting position within the packet of interest. Both algorithms are able to isolate collision locations within the packet when the signal-to-noise ratio is high enough for the link to operate at a BER of ~10^?6 in the absence of collision. An extension to M-QAM demonstrates similar effectiveness for constellation sizes up to 128.     

A Technique for Frequency Synchronization in Multi-Band OFDM in Realistic UWB Channel

This paper presents a highly accurate frequency offset estimation algorithm for multi-band orthogonal frequency division multiplexing (MB-OFDM) systems effective for realistic ultra-wideband (UWB) environment. The proposed algorithm derives its estimates based on phase differences in the received subcarrier signals of several successive OFDM symbols in the preamble. We consider different carrier frequency offsets and different channel responses in different bands to keep the analysis and simulation compatible for practical multi-band UWB scenario. Performance of the proposed algorithm is studied by means of bit error rate (BER) performance of MB-OFDM system. In order to compare the variance of the synchronizer to that of the theoretical optimum, we derive the Cramer–Rao lower bound (CRLB) of the estimation error variance and compare it with the simulated error variance both in additive white Gaussian noise and UWB channel model (CM) environments, CM1–CM4. Next, we modify the estimation algorithm by proposing a multi-band averaging frequency offset synchronization (MBAFS) scheme. We establish superior BER performance with MBAFS compared to our first scheme. We calculate modified CRLB for MBAFS and compare it with simulation results for CM1–CM4. Both analysis and simulation show that MBAFS algorithm can estimate the carrier frequency offset effectively and precisely in UWB fading channels for MB-OFDM applications. We also analyze the computational complexity of both the proposed algorithms in order to verify their feasibility of implementation in practical UWB receiver design.     

Joint estimation of symbol timing and carrier frequency offset of OFDM signals over fast time-varying multipath channels

In this paper, we present a novel joint algorithm to estimate the symbol timing and carrier frequency offsets of wireless orthogonal frequency division multiplexing (OFDM) signals. To jointly estimate synchronization parameters using the maximum likelihood (ML) criterion, researchers have derived conventional models only from additive white Gaussian noise (AWGN) or single-path fading channels. We develop a general ML estimation algorithm that can accurately calculate symbol timing and carrier frequency offsets over a fast time-varying multipath channel. To reduce overall estimation complexity, the proposed scheme consists of two estimation stages: coarse and fine synchronizations. A low complexity coarse synchronization based on the least-squares (LS) method can rapidly estimate the rough symbol timing and carrier frequency offsets over a fast time-varying multipath channel. The subsequent ML fine synchronization can then obtain accurate final results based on the previous coarse synchronization. Simulations demonstrate that the coarse-to-fine method provides a good tradeoff between estimation accuracy and computational complexity.     

Joint Frequency and Symbol Synchronization Schemes for an OFDM System

This paper proposes two multi-stage joint symbol timing and carrier frequency synchronization schemes for an orthogonal frequency division multiplex (OFDM) system. Simultaneous estimation of symbol timing and frequency offset is derived from the maximum likelihood (ML) principle, assuming a cyclic prefix (guard interval) is inserted in each OFDM symbol. One of the proposed algorithms derives an initial frequency estimate in the first stage that reduces the frequency uncertainty to only two or three sub-carrier spacings. The timing information and a finer frequency estimate that has a resolution of a sub-carrier spacing are obtained in the second stage. The third stage provides an estimation for the residual fractional frequency error. The other algorithm bypasses the first stage for one can use the second stage alone to search for the timing and frequency offsets. However, the computing complexity of the second stage is higher than that of the first stage, thus the three-stage algorithm is a preferred choice unless the frequency uncertainty is small. Simulation results show that both algorithms yield excellent performance not only in white Gaussian channels but also in multipath fading channels.     

一种残差模糊匹配的非线性目标跟踪改进算法

为解决目标跟踪中因系统滤波初值不准确和噪声统计特性未知引起标准非线性卡尔曼算法估计误差变大问题,该文提出一种基于残差的模糊自适应(RTSFA)非线性目标跟踪算法。在确定采样型滤波基本框架的基础上,给出了在线性化误差约束条件下高斯权值的积分一般形式,并利用李雅普诺夫第二方法证明了该算法估计误差有界收敛的充分条件。进一步构建自适应噪声协方差矩阵在线估计噪声特性,并引入Takagi-Sugeno模型和量测椭球界限规则选择噪声估计器调节因子,有效提高了算法的收敛速度和滤波精度。通过滤波初值信息不明和量测噪声时变的纯方位目标跟踪模型,验证了非线性目标跟踪算法具有更好的跟踪精度和更强的鲁棒性。     

The Gaussian Transform of Distributions: Definition, Computation and Application

This paper introduces the general-purpose Gaussian transform of distributions, which aims at representing a generic symmetric distribution as an infinite mixture of Gaussian distributions. We start by the mathematical formulation of the problem and continue with the investigation of the conditions of existence of such a transform. Our analysis leads to the derivation of analytical and numerical tools for the computation of the Gaussian transform, mainly based on the Laplace and Fourier transforms, as well as of the afferent properties set (e.g., the transform of sums of independent variables). The Gaussian transform of distributions is then analytically derived for the Gaussian and Laplacian distributions, and obtained numerically for the generalized Gaussian and the generalized Cauchy distribution families. In order to illustrate the usage of the proposed transform we further show how an infinite mixture of Gaussians model can be used to estimate/denoise non-Gaussian data with linear estimators based on the Wiener filter. The decomposition of the data into Gaussian components is straightforwardly computed with the Gaussian transform, previously derived. The estimation is then based on a two-step procedure: the first step consists of variance estimation, and the second step consists of data estimation through Wiener filtering. To this purpose, we propose new generic variance estimators based on the infinite mixture of Gaussians prior. It is shown that the proposed estimators compare favorably in terms of distortion with the shrinkage denoising technique and that the distortion lower bound under this framework is lower than the classical minimum mean-square error bound.