双/多基地制导雷达定位精度分析

介绍了双/多基地制导雷达五种定位方法,首先对T-R型雷达系统定位精度进行了系统仿真,然后对五种定位算法的优缺点进行分析比较。最后通过多站定位对定位性能好的两种算法进行数据融合。仿真结果表明,利用多基地制导雷达定位改善了基线附近的定位精度,使距离解算精度得到进一步提高。     

双基地两坐标雷达对三维目标的最优化定位算法

利用非线性最小二乘方法的最优性质,本文提出了双基地雷达的两种最优化算法:LMF(Leenberg-Marquardt-Fletcher)定位算法和拟牛顿定位算法。仿真结果表明,与目前的定位优化算法相比,这两种新的定位算法不但具有更好的定位精度,而且在不增加观测量的条件下避免了定位模糊问题。     

基于多模融合的室内定位算法研究

针对现有单模定位算法精度较低的问题,基于融合技术的思想,通过分析各种典型的室内定位技术及现有的融合定位系统,提出了一种WiFi与蓝牙两种技术进行融合的室内定位方案,旨在提高定位精度。提出的算法采用指纹法得到两种模式的定位结果,并在决策级上利用权重分析的方法进行多模融合,最终得到目标的坐标定位。     

自主移动机器人即时定位与地图构建方法研究

自主移动机器人的即时定位与地图构建是机器人领域中的关键问题。文中对自主移动机器人即时定位与地图构建研究的两个主要问题进行了分析,研究了不确定信息的描述和处理方法,以及数据关联的方法。综述了现阶段即时定位与地图构建的实现方法,其中包括两种卡尔曼滤波算法和粒子滤波算法,以及基于视觉的SLAM算法。通过分析现有算法的特点,提出了自主移动机器人即时定位与地图构建未来研究的发展方向。     

基于最小平方算法的被动浮标目标定位

对多被动浮标的目标定位问题进行了研究,在给定各枚浮标与目标之间距离的差值的条件下,给出一种基于最小平方意义下的被动目标定位算法,即在保证定位误差平方最小的条件下,实现目标定位的方法。在对算法定位原理及定位误差研究分析的基础上,针对近场目标和远场目标两种情况,采用Monte-Carlo方法对该算法的定位性能进行了仿真;在一定假设条件下,仿真分析了不同距离差值量测噪声对目标位置误差、位置均方根误差及方位误差的影响,并且对近场目标和远场目标两种情况下目标定位的性能进行了分析比较。     

无线定位中的接收信号强度值匹配算法

提出两种在WLAN环境下利用RSSI值与距离之间关系进行定位的匹配算法。分别对两种算法的原理进行理论分析,建立算法实现的模型,使之在不同情况下都能够精确地计算匹配结果。提出实现算法的基本流程,并对算法本身的特点进行了理论分析。分析表明通过该方法的修正可以明显改善定位的精度。     

声源定位在Micaz节点上的实现

对无线传感器网络中声源定位的两大关键技术时间同步和定位算法进行了研究,针对声源定位关键问题,提出了一种改进的无线传感器定位方法。通过修改时间同步消息标记时间戳,对时钟漂移进行线性回归分析,有效降低了系统能耗。利用改进的TPSN时间同步算法与声源定位算法在Micaz节点上实现了定位功能。实验结果证明了本文方法的有效性和可靠性。     

采用两种被动定位法的声纳浮标接收器基阵设计

以矢量水听器的三元纯方位被动定位和测时差被动定位两种方法为基础,针对算法特点和声纳浮标的使用特点,分别设计了两种方法所采用的水下换能器基阵结构。两种基阵结构相似,部分重合,可根据实际工作条件任意选择算法,也可两种算法相互间自行切换。此种相互补偿的设计可以提高水下目标被动定位的精度和可靠性。     

基于Chan氏算法和最近邻居算法的协同定位方法

在对定位算法中的测距和非测距算法研究的基础上,本文提出了在改进阅读器排布的定位空间中,将最近邻居算法与Chan算法结合,进行协同定位的方法。在设定的两种小范围仿真空间中,通过均方误差(RMSE)和误差累计分布曲线(CDF)两个定位精度评价指标对改进前后的算法进行比较,在噪声较小且误差均匀分布的环境下,改进算法的定位误差可90%控制在0.4m以内。     

基于Chan氏算法和Taylor级数展开法的协同定位方法

该文分析讨论了Chan和泰勒两种TDOA定位算法的优缺点,并在此基础上提出了基于 Chan氏算法(1994)和Taylor级数展开法(1976)的协同定位方法。通过在不同信道环境下对协同定位 方法进行仿真,表明该方法在信道环境恶劣时能够有效地提高定位精度。     

Sparse LMS/F algorithms with application to adaptive system identification

Standard least mean square/fourth (LMS/F) is a classical adaptive algorithm that combined the advantages of both least mean square (LMS) and least mean fourth (LMF). The advantage of LMS is fast convergence speed while its shortcoming is suboptimal solution in low signal‐to‐noise ratio (SNR) environment. On the contrary, the advantage of LMF algorithm is robust in low SNR while its drawback is slow convergence speed in high SNR case. Many finite impulse response systems are modeled as sparse rather than traditionally dense. To take advantage of system sparsity, different sparse LMS algorithms with l_p‐LMS and l₀‐LMS have been proposed to improve adaptive identification performance. However, sparse LMS algorithms have the same drawback as standard LMS. Different from LMS filter, standard LMS/F filter can achieve better performance. Hence, the aim of this paper is to introduce sparse penalties to the LMS/F algorithm so that it can further improve identification performance. We propose two sparse LMS/F algorithms using two sparse constraints to improve adaptive identification performance. Two experiments are performed to show the effectiveness of the proposed algorithms by computer simulation. In the first experiment, the number of nonzero coefficients is changing, and the proposed algorithms can achieve better mean square deviation performance than sparse LMS algorithms. In the second experiment, the number of nonzero coefficient is fixed, and mean square deviation performance of sparse LMS/F algorithms is still better than that of sparse LMS algorithms. Copyright © 2013 John Wiley & Sons, Ltd.     

自适应算法在干扰抵消器应用中的比较研究

文中介绍了自适应滤波算法的原理和干扰抵消器工作原理，并将LMS算法、NLMS算法和变步长LMS算法分别应用在了干扰抵消器中进行了仿真。仿真的结果表明，三种自适应算法运用到了干扰抵消器中，都可以很好地滤除干扰，提取有用信号。其中运用了变步长LMS算法的干扰抵消器无论在收敛速度和滤波性能上，都要强于其他两种算法。     

Performance of LMS-based adaptive filters in tracking atime-varying plant

The article gives a general analysis of the LMS-based adaptive filters when used for tracking a class of time varying plants. The algorithms covered are the conventional LMS, the transform-domain normalized LMS, and the LMS/Newton algorithm. An important fact that we observe is that in comparing these algorithms, better initial convergence does not necessarily mean better tracking. A few special cases are presented that show the contrary     

单站外辐射源定位误差分析与仿真优化

建立了外辐射源单站定位的几何模型,给出了测量目标参数(方位角、到达时间差、基线距离、多普勒频移)的主要方法,分析了影响目标参数观测精度的相关因素。比较两种典型定位算法的几何定位精度,并利用子集优选策略改善算法性能,在不同观测精度和位置关系的条件下,进行仿真实验。实验结果表明,各算法的定位精度与目标观测精度和几何位置有关,而对于改善后的定位算法,使得大部分区域内的目标定位精度提高了50%,并减少了目标定位盲区。     

A modified LMS algorithm for fast processing — analysis and realization

Fast convergence and simplicity of the real-time implementation of the adaptive filter algorithm are desirable in several applications. In this paper, a fast modified least mean square (LMS) algorithm is presented and analysed. The performance of the LMS and the modified LMS algorithm is compared with the help of both simulation and experimental results. Once the algorithms reach the track period (i.e. steady-state conditions), their performance is found to be essentially the same. The tracking performance of the modified algorithm is better as it operates twice as fast as the LMS algorithm.     

3种改进型LMS算法在噪声抵消中的仿真比较

介绍了噪声抵消的原理和从强背景噪声中采用自适应滤波提取有用信号的方法;对基于Sigmind函数的变步长算法、基于箕舌线和基于抽样函数的变步长算法进行了对比研究。计算机仿真结果表明,这3种算法都能通过有效抑制各种干扰来提高强噪声背景中的信号检测特性;输出均方误差曲线和信噪比表明:基于抽样函数的变步长LMS算法具有良好的收敛性能、更小的权噪声和更大的抑噪能力。     

运动TDMA系统目标时差定位算法

根据时分多址(TDMA)系统的同步特征,利用TDMA运动目标准周期性信号的到达时间,提出了3种在三站时差定位系统中实现目标定位的算法.采用目标运动分析的方法,对TDMA目标位置的可观测性进行分析,提出了目标运动分析时差定位算法,利用目标航迹上多个位置的时差实现目标的定位.运用目标运动分析测距算法,提出了测距与传统时差定...     

Sparse least mean fourth algorithm for adaptive channel estimation in low signal‐to‐noise ratio region

Both least mean square (LMS) and least mean fourth (LMF) are popular adaptive algorithms with application to adaptive channel estimation. Because the wireless channel vector is often sparse, sparse LMS‐based approaches have been proposed with different sparse penalties, for example, zero‐attracting LMS and L_p‐norm LMS. However, these proposed methods lead to suboptimal solutions in low signal‐to‐noise ratio (SNR) region, and the suboptimal solutions are caused by LMS‐based algorithms that are sensitive to the scaling of input signal and strong noise. Comparatively, LMF can achieve better solution in low SNR region. However, LMF cannot exploit the sparse information because the algorithm depends only on its adaptive updating error but neglects the inherent sparse channel structure. In this paper, we propose several sparse LMF algorithms with different sparse penalties to achieve better solution in low SNR region and take the advantage of channel sparsity at the same time. The contribution of this paper is briefly summarized as follows: (1) construct the cost functions of the LMF algorithm with different sparse penalties; (2) derive their lower bounds; and (3) provide experiment results to show the performance advantage of the propose method in low SNR region. Copyright © 2013 John Wiley & Sons, Ltd.     

Generalized Robust Multichannel Frequency‐Domain LMS Algorithms for Blind Channel Identification

Recently, several noise‐robust adaptive multichannel LMS algorithms have been proposed based on the spectral flatness of the estimated channel coefficients in the presence of additive noise. In this work, we propose a general form for the algorithms that integrates the existing algorithms into a common framework. Computer simulation results are presented and demonstrate that a new proposed algorithm gives better performance compared to existing algorithms in noisy environments.     

一种改进的变步长LMS自适应算法

传统的最小均方误差(LMS)算法难以同时获取较快的收敛速度和较小的稳态误差,而变步长LMS算法可获得二者之间的平衡。对已有的一些变步长LMS算法进行了分析,在变系数步长(VFSS)算法的基础上,引入输入信号因子,并建立步长因子与误差信号之间新的非线性函数关系,提出一种改进的变步长LMS算法,该算法不仅继承了VFSS算法在低信噪比环境下抗噪声性能好的特点,而且能够快速跟踪系统的变化,仿真结果表明改进算法的性能优于现有算法。