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声源定位属于典型的被动定位,基于声源定位与跟踪在实际中具有广泛的应用。文中利用声音随距离的能量衰减模型,提出了最小二乘的增量式声源跟踪算法形式,并在二维空间分别给出了声源的定位,位置式及增量式声源跟踪算法的实验结果。位置式及增量式声源跟踪算法是跟踪算法的两种表现形式,实际中可根据跟踪算法的复杂度和具体的控制对象进行选择。由于只需计算声源移动增量,与传统的位置式算法相比,增量式声源位置跟踪算法具有控制器调整方便、容错性强等特点。 相似文献
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对传统的MUSIC算法时域特征分析方法改进到频域,并将其在GSMMA麦克风阵列模型上进行声源定位仿真计算,获得了良好的定位效果,同时对改进算法进行了矩阵理论分析。 相似文献
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为了提高相位变换加权的可控响应功率SRP-PHAT(Steered Response Power-Phase Transform)声源定位算法的性能,提出一种基于分布式麦克风阵列的改进算法。根据分布式麦克风阵列的特点,使用麦克风对接收信号的广义互相关GCC-PHAT(Generalized Cross-Correlation with Phase Transform weighting)函数的最大值来评价接收信号的质量。在传统SRP-PHAT算法的基础上,以该最大值为权重乘以每对麦克风接收信号的GCC-PHAT函数。该算法质量较高的麦克风对接收信号赋予了较大的权重,因而能提高定位性能。仿真结果表明,在信噪比低于10 dB,混响时间大于300 ms的条件下,改进算法的定位成功率比传统算法提高了2%~4%。 相似文献
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基于声源能量的无线传感器网络( WSNs)最大似然定位算法抗噪声干扰能力强,定位精度高,同时适用于多个目标定位,但是计算量大,不适用于实时定位。针对现有算法的缺点,提出了一种基于自适应迭代的最大似然定位算法。该算法将代价函数作为目标函数,在给定的梯度误差范围内自适应地搜索目标位置。为了提高算法的收敛速度和定位精度,提出了基于Sigmoid函数的变步长的搜索算法。仿真实验结果表明:与最大似然定位算法相比,自适应迭代算法运算量小,定位精度高,能满足对目标定位精度和速度要求较高的场合,具有一定的实际应用意义。 相似文献
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《计算机应用与软件》2016,(6)
针对传统的SRP-PHAT(Steered Response Power with Phase Transform)声源定位算法容易受噪声影响而导致定位性能降低的问题,提出一种频域补零且保留部分镜像分量的改进算法。该算法首先通过傅里叶变换将接收信号变换到频域,然后在高频端补零至20倍帧长,同时保留部分镜像分量。在此基础上计算麦克风对接收信号的互功率谱密度函数,作傅里叶逆变换得到相位变换加权的广义互相关(GCC-PHAT)函数。保留的镜像分量拓宽了信号频域,使GCC-PHAT函数的峰更为尖锐,累加后得到的SRPPHAT函数的空间谱峰也就更加尖锐,从而提高定位性能。实验表明,相比于传统算法,改进算法能显著提高定位成功率。 相似文献
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针对现代战场中对狙击手实现快速打击的需求,本文设计了一种基于TDOA声源定位算法的激光武器狙击手攻击系统,该系统以STM32单片机为主控制器,以驻极体麦克风为声音检测传感器,利用TDOA算法实现音源的坐标测量。为了提高测量速度,本文采用查表法代替传统的最小二乘拟合的方法求解非线性方程组,使得.整机可以采用单片机作为核心处理器,降低了系统的功耗和体积。最后,本文通过实验测量所设计的枪声定位系统,距离测量误差小于±01m,角度测量误差小于±1°,测量时间不超过2s。此外,本系统可以用激光指示出声源所在位置,模拟使用激光武器打击目标。 相似文献
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该文提出一种使用声压幅度比进行声源定位的方法。该方法从接收阵列各拾音器所接收之电压信号的幅度与相应拾音器到待测声源的距离两者之间存在的关系出发,给出了以声压幅度比为参量的约束条件的表达式,建立了利用这些约束条件进行声源定位的算法。为了验证方法的有效性,进行了计算机仿真实验。结果表明本文提出的定位方法简单、快捷,具有较高的定位精度。 相似文献
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A method based on the generalized cross-correlation (GCC) method weighted by the phase transform (PHAT) has been developed for binaural sound source localization (SSL) and tracking of multiple sound sources. Accurate binaural audition is important for applying inexpensive and widely applicable auditory capabilities to robots and systems. Conventional SSL based on the GCC-PHAT method is degraded by low resolution of the time difference of arrival estimation, by the interference created when the sound waves arrive at a microphone from two directions around the robot head, and by impaired performance when there are multiple speakers. The low-resolution problem is solved by using a maximum-likelihood-based SSL method in the frequency domain. The multipath interference problem is avoided by incorporating a new time delay factor into the GCC-PHAT method with assuming a spherical robot head. The performance when there are multiple speakers was improved by using a multisource speech tracking method consisting of voice activity detection (VAD) and K-means clustering. The standard K-means clustering algorithm was extended to enable tracking of an unknown time-varying number of speakers by adding two additional steps that increase the number of clusters automatically and eliminate clusters containing incorrect direction estimations. Experiments conducted on the SIG-2 humanoid robot show that this method outperforms the conventional SSL method; it reduces localization errors by 18.1° on average and by over 37° in the side directions. It also tracks multiple speakers in real time with tracking errors below 4.35°. 相似文献
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机器人听觉声源定位研究综述 总被引:4,自引:0,他引:4
声源定位技术定位出外界声源相对于机器人的方向和位置,机器人听觉声源定位系统可以极大地提高机器人与外界交互的能力.总结和分析面向机器人听觉的声源定位技术对智能机器人技术的发展有着重要的意义.首先总结了面向机器人听觉的声源定位系统的特点,综述了机器人听觉声源定位的关键技术,包括到达时间差、可控波束形成、高分辨率谱估计、双耳听觉、主动听觉和视听融合技术.其次对麦克风阵列模型进行了分类,比较了基于三维麦克风阵列、二维麦克风阵列和双耳的7个典型系统的性能.最后总结了机器人听觉声源定位系统的应用,并分析了存在的问题和未来的发展趋势. 相似文献
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提出了一种在嘈杂环境下利用麦克风阵列对声音信号定位的方法。该方法对每个麦克风采集的声音信号进行经验模态分解,然后根据各个IMF信号的归一化能量挑选出主要的IMF分量进行信号重构,从而实现对信号进行降噪处理。将降噪后的信号使用互功率谱相位法进行相关运算,计算出不同麦克风声音信号出现的时间差异。根据信号时延和麦克风之间的几何位置关系计算出声音信号的位置。为了验证本文所提出的定位算法,进行了语音信号定位实验,通过实验实测的数据分析对比分析,验证了本文提出的方法比传统的定位算法要优越。 相似文献
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For passive source localization based on both TDOA and GROA, this paper proposes two bias reduction methods for the well-known Weighted-Least-Squares (WLS) estimator. We first derive the passive source localization bias from the two-step algebraic closed-form solution. This bias is found to be considerably larger than the Maximum Likelihood Estimator (MLE) and limits the WLS estimator’s practical applications. In this paper, We develop two methods to reduce the bias. The first one called Bias-Subtraction-Method (BSM) directly subtracts the expected bias from the solution of the WLS estimator, and the second one called Bias-Reduction-Method (BRM) imposes a constraint to the equation error formulation to improve the source location estimate. The noise covariance matrix must be known exactly in calculating the expected bias in BSM, and we only need to know the structure of it in BRM. For far-field sources localization when the noise is Gaussian and not too large, both of the two proposed methods can reduce the localization bias effectively and achieve the Cramér-Rao Lower Bound (CRLB) performance very well, and the BRM almost has the same performance as the MLE estimator. Simulations corroborate the performance of the two proposed methods. 相似文献
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头佩式麦克风阵列在单兵便携反狙击声探测定位系统和机器人声定位系统中具有实际的应用价值。一般的声源定位方法是基于无遮挡的线性或非线性麦克风阵列。采用头佩式麦克风阵列,考虑到背向声源麦克风的低频声波由于头盔遮挡而发生的衍射作用,针对低频波段的声音信号进行定位算法的设计和研究。该算法利用低频声波的绕射路径计算时延,采用联合可控功率响应(SRP-PHAT)框架进行时延补偿搜索定位。实验表明,相比于普通的无遮挡定位算法,基于绕射路径的头佩式麦克风阵列定位方法通过综合利用背向声源的麦克风数据,明显地提高了定位的精度,这种精度的提升在选择1 kHz以内的信号频率窗口时达到最佳效果。 相似文献
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Fakheredine Keyrouz Klaus Diepold 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2008,12(7):721-729
This paper presents a novel real-time robotic binaural sound localization method based on hierarchical fuzzy artificial neural
networks and a generic set of head related transfer functions. The robot is a humanoid equipped with the KEMAR artificial
head and torso. Inside the ear canals two small microphones play the role of the eardrums in collecting the impinging sound
waves. The neural networks are trained using synthesized sound sources placed every 5° from 0° to 255° in azimuth, and every
5° from − 45° to 80° in elevation. To improve generalization, the training data was corrupted with noise. Thanks to fuzzy
logic, the method is able to interpolate at its output, locating with high accuracy sound sources at positions which were
not used for training, even in presence of strong distortion. In order to achieve high localization accuracy, two different
binaural cues are combined, namely the interaural intensity differences and interaural time differences. As opposed to microphone-array
methods, the presented technique, uses only two microphones to localize sound sources in a real-time 3D environment.
This work is fully supported by the German Research Foundation (DFG) within the collaborative research center SFB453 “High-Fidelity
Telepresence and Teleaction”. 相似文献