基于最小熵的自适应加权包络对齐算法

在逆合成孔径雷达（ISAR）成像及运动补偿原理的基础上提出了一种基于最小熵的自适应加权包络对齐算法,并通过仿真试验验证了该算法对提高运动补偿精度和改善最终成像质量的有效性。     

一种基于场强差的移动台定位改进算法

为了解决传统的移动台定位过程中信号的传播误差对定位精度的影响,提出了一种基于移动台与服务基站之间的场强差来确定移动台位置的改进算法。小波变换在信号消噪处理中可以取得良好的效果,通过选择合适的小波函数和分解层数,对测量得到的场强差进行分解和重构,然后用经典Chan算法确定移动台位置,大大降低了传播误差对定位精度的影响。仿真表明,该算法能很好地抑制传播误差,其定位效果比直接使用Chan算法更加稳定。     

基于遗传算法的二维最小交叉熵的动态图像分割

为解决动态图像的分割问题,本文提出了一种遗传算法的二维最小交叉熵的分割算法。首先给出二维交叉熵的定义,然后运用遗传算法,以二维交叉熵为评价函数,搜索能使二维交叉熵最小的参数向量,并以该参数向量为分割阈值对飞机图像进行图像分割处理。实验结果表明,二维飞机交叉熵图像分割精度比一维飞机熵图像分割精度高,且比传统的二维熵分割速度快。     

基于朗伯模型参数估计的VLC室内定位优化算法

为实现高精度室内定位,本文设计了一种可见光 通信(VLC)室内定位系统,并通过 结合优化的朗伯模型、码分多址技术(CDMA)、三边定位算法而有效提升了定位精度和系统 扩展性。首先,每个发光二极管(LED)的ID信息经过直接序列调制后加载到LED驱动电路上 ,LED发出带有自身ID信息的灯光信号。在接收端通过光电探测器(PD)接收灯光信号,并 根据扩频码的正交性恢复出ID信息及接收信号强度(RSS),以此提高信道容量并增强系统 抗干扰能力。然后,根据朗伯光源模型,由三边定位算法得出待定位点的定位估计坐标。为 进一步提高精度,引入k最近邻(KNN)思想,采集适当的指纹点并由指纹点信息对每盏灯在 定位估计坐标处的朗伯光源模型参数进行估计,由优化后的朗伯模型计算出精度更高的定位 坐标。在1m×1m×1.35 m的空间区域中,进行本VLC室内定位系统 的实验测试。结果表明,提 出的高精度VLC室内定位系统的平均定位误差降低至2cm左右,其定位精度相比于传统三边 定 位算法提升了30%。此外,该系统方案所采用基于指纹点信息优化朗 伯模型参数的方法具备良好的实用扩展性,可实现广阔的应用场景。     

基于分形理论的移动单基站定位参数估计算法

针对第三方用户对移动台定位的难点，设计了一种单基站定位算法。分形插值算法和三天线场强差分算法用于获取高精度的单基站定位参数，同时介绍了抑制多径效应的选通相关算法。系统的仿真结果表明该定位算法可以提供高精度的定位结果。     

A minimum entropy based switched adaptive predictor for lossless compression of images

The gradient adjusted predictor (GAP) uses seven fixed slope quantization bins and a predictor is associated with each bin, for prediction of pixels. The slope bin boundary in the same appears to be fixed without employing a criterion function. This paper presents a technique for slope classification that results in slope bins which are optimum for a given set of images. It also presents two techniques that find predictors which are statistically optimal for each of the slope bins. Slope classification and the predictors associated with the slope bins are obtained off-line. To find a representative predictor for a bin, a set of least-squares (LS) based predictors are obtained for all the pixels belonging to that bin. A predictor, from the set of predictors, that results in the minimum prediction error energy is chosen to represent the bin. Alternatively, the predictor is chosen, from the same set, based on minimum entropy as the criterion. Simulation results, of the proposed method have shown a significant improvement in the compression performance as compared to the GAP. Computational complexity of the proposed method , excluding the training process, is of the same order as that of GAP.     

基于局部熵的点云精简算法

针对目前流行的三维物体激光扫描仪获取的点云数据量大,冗余度高等问题,提出一种基于信息熵的点云精简算法。首先,定义数据点的曲率、点到邻域点重心的距离、点到邻域点的平均距离的倒数,三者乘积为权值积;然后,使用K-means聚类算法划分点云数据,根据类内估计曲率差值区分特征区域与非特征区域;最后,针对特征区域,利用提出的精简方法精简点云。实验结果表明,该方法计算相对简单,能够有效避免孔洞现象,同时,更好地保留了点云数据的原始物理特征。     

一种基于圆环模型的网格搜索定位算法

为了降低非视距(Non-Line-of-Sight,NLOS)误差对定位精度的不利影响,提出了一种基于圆环模型的到达时间(Time of Arrival,TOA)/到达角(Angle of Arrival,AOA)混合定位算法。假设各基站(Base Station,BS)接收的多径信号都经历了散射体的单次散射,首先利用圆环模型的几何特征搜索圆环的半径,再根据圆环的半径、TOA信息和AOA信息确定散射体的位置;然后将散射体作为虚拟基站规划移动台(Mobile Station,MS)的可能位置范围,并使用网格搜索和约束判断来获得多个MS可能的估计位置;最后根据误差目标函数的大小进行选择性平均。在不同NLOS场景下的仿真结果表明,与现有算法相比,所提算法取得了更好的定位性能。     

A hop-count based positioning algorithm for wireless ad-hoc networks

We propose a range-free localization algorithm for a wireless ad-hoc network utilizing the hop-count metric’s ability to indicate proximity to anchors (i.e., nodes with known positions). In traditional sense, hop-count generally means the number of intermediate routers a datagram has to go through between its source and the destination node. We analytically show that hop-count could be used to indicate proximity relative to an anchor node. Our proposed algorithm is computationally feasible for resource constrained wireless ad-hoc nodes, and gives reasonable accuracy. We perform both real experiments and simulations to evaluate the algorithm’s performance. Experimental results show that our algorithm outperforms similar proximity based algorithms utilizing received signal strength and expected transmission count. We also analyze the impact of various parameters like the number of anchor nodes, placements of anchor nodes and varying transmission powers of the nodes on the hop-count based localization algorithm’s performance through simulation.     

基于手机的声学超宽带室内定位

针对室内定位技术部署复杂、成本高的问题,提出了一种利用手机接收声学信号通过脉冲压缩进行室内定位的方法。通过借鉴雷达系统中的脉冲压缩技术,将信号和噪声分离,并提取出信号到达时延估计。为了减小定位误差,研究了手机的声学特性,设计了声学超宽带信号的信道模型,将应答节点时延回传,进一步减小信号传播的时延估计。在停车场的试验结果表明：定位结果和实际位置相符,平均定位误差在30 cm以内。     

Discrete minimum entropy quantization

We present a simple lower bound on the entropy of a quantized signal and a method for determining the minimum entropy quantization, subject to a maximum distortion, for a discrete memoryless random process. This quantization allows more efficient use of variable-order models for compression of images and sound.     

Linear minimum free energy estimation: a computationally efficientnoise suppression spectral estimation algorithm

Simplified linear versions of the nonlinear minimum free energy (MFE) noise suppression algorithms are introduced. The LMFE algorithms result from the addition of a smoothness penalty function to the linear prediction cost function. The autoregressive (AR) parameters are chosen commensurate with a global minimum of the modified cost function. It is shown that this constrained optimization procedure reduces to a form of matrix regularization. The LMFE algorithms are calculable in real time as they require a negligible increase in complexity over the conventional autoregressive algorithms, including fast computational versions thereof. Linear MFE extensions are applicable to all conventional AR algorithms, and should in each case substantially increase the useful SNR range of these algorithms. Simulation results illustrating the single snapshot performance of these algorithms are given for both narrowband sources and combinations of narrowband and broadband sources subjected to various levels of Gaussian white noise     

The research on channel estimation and signal-noise ratio estimation based on minimum error entropy kalman filter for single carrier frequency domain equalization system

In this paper, the channel estimation and signal-to-noise ratio (SNR) estimation technique of single-carrier frequency domain equalization (SC-FDE) system under low SNR in aeronautical multipath channel are studied, a SNR estimation algorithm which is easy to implement in engineering and an improved LS channel estimation algorithm based on Kalman filter using minimum error entropy (MEE-KF) are proposed. This paper first introduces the SC-FDE system and introduces the principle of MEE-KF, and then, the channel estimation flow based on MEE-KF is obtained by combining it with the traditional LS channel estimation algorithm, which makes the estimation results perform better. Simulation results show that after getting more accurate noise variance, the channel estimation results can better follow the changes of the channel after MEE-KF processing, so as to resist the doppler frequency offset effect and make the channel estimation results more accurate, that is the channel response results of the data part can be closer to the real situation, so that the communication performance of SC-FDE system has also been greatly improved.     

基于MT-BCS的可分离DOA估计算法

压缩感知理论的不断发展,为二维DOA估计问题提供了新的思路。然而传统的二维DOA估计方法,只是对一维估计的建模方法别无二致,这导致求解时存在计算复杂度高、分辨率低等问题。文中通过对二维DOA模型的重新建模,将多任务贝叶斯压缩感知理论应用于二维DOA估计问题中,从而提出基于多任务贝叶斯压缩感知的可分离二维DOA低的优点。     

基于时间序列的竞技水平估计算法

体育竞技中,选手水平随时间变化,True Skill-T算法能估计选手时间序列上的竞技水平。文中针对围棋比赛的特点对其进行简化和修改,实现了True Skill Go-Year算法。为研究算法在实际排名应用上的可行性,分别基于真实的围棋比赛数据和仿真数据,考察了算法对数据的适应能力。实验结果表明True Skill-T算法排名结果符合领域常识,具备实际应用的性能。     

Information entropy based match field cutting algorithm

With the increasing diversity of network functions,packet classification had a higher demand on the number of match fields and depth of match table,which placed a severe burden on the storage capacity of hardware.To ensure the efficiency of matching process while at the same time improve the usage of storage devices,an information entropy based cutting algorithm on match fields was proposed.By the analysis on the redundancy of match fields and distribution pattern in a rule set,a match field cutting model was proposed.With the mapping of matching process to the process of entropy reduction,the complexity of optimal match field cutting was reduced from NP-hard to linear complexity.Experiment results show that compared to existing schemes,this scheme can need 40% less TCAM storage space,and on the other side,with the growing of table size,the time complexity of this algorithm is also far less than other algorithms.     

基于多天线到达相位差的室内定位算法

针对测距式射频识别(RFID)室内定位算法的精确度会受到测距误差严重影响的问题,提出一种基于多天线到达相位差(M-PDOA)的室内定位算法。通过M-PDOA测距方法进行测距,采用三边测量算法对目标标签进行定位。仿真结果显示,本算法的测距平均误差为0.102 7 m,性能提升65.76%;定位平均误差为0.180 7 m,性能提升56.67%,说明本文算法能够有效减少测距误差,并且定位精确度较高。     

一种基于可移动锚节点的DV-HOP改进定位算法

当节点不均匀分布时,DV-Hop的定位精度较差。针对DV-Hop定位算法的缺陷,提出一种基于移动锚节点的改进DV-Hop定位算法。在网络中引入具有一定移动能力的锚节点,并构建锚节点之间的虚拟力模型,锚节点受到虚拟力作用发生移动,从而均匀的分布于整个网络,修正了DV-Hop对不均与分布网络适应性差的特点。仿真实验表明,与原始算法相比改进后的算法定位精度有较大提高。     

The minimum error entropy based robust wireless channel tracking inimpulsive noise

This article concerns the problem of adaptive wireless channel tracking in the non-Gaussian α-stable noise. By assuming a primitive Cauchy distribution for the estimate error and minimizing the entropy of error, we develop the least entropy of error (LEE) based wireless channel tracking algorithm and the second-order LEE (SOLEE) algorithm. Simulation results show that both algorithms are robust to impulsive noise and such robustness can be achieved without any performance loss in the Gaussian noise