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题名:
基于小波分析的机动平台光电跟踪系统的信号处理与研究
作者: 蒋行国
学位类别: 博士
答辩日期: 2007-06-11
授予单位: 中国科学院光电技术研究所
授予地点: 光电技术研究所
导师: 杨文淑
关键词: 惯性传感器 ; 小波变换 ; 提升小波 ; Kalman滤波 ; 实时信号处理 ; 信号融合
其他题名: Processing and Study Based on Wavelet for Signal of the Electro-Optical Tracting System on Moving Bed
学位专业: 信号与信息处理
中文摘要: 对机动平台光电跟踪系统惯性传感器信号进行处理一直以来都是光电跟踪系统的关键技术之一。本论文围绕机动平台光电跟踪系统惯性传感器信号处理技术,展开深入细致的研究,旨在提出一些有效的技术路线和方法,以解决当前该领域中一些难点和关键问题,如实时性问题。 论文在分析了机动平台光电跟踪系统惯性传感器信号噪声特点的基础上,对系统噪声在小波域的特性进行了深入研究,描述了噪声特性与其小波变换系数之间的关系,以及该小波变换系数在各分解层的传播特性。并提出在小波域对系统随机噪声参数进行估计,为后续信号滤波提供条件。同时,对小波分析在光电跟踪系统中的应用进行了深入的研究和分析。 在对机动平台光电跟踪系统惯性传感器信号滤波处理方面,提出了几项新的技术和方法。提出了基于小波分析的Kalman滤波方法,首先改进了AR模型,使其能对非零均值的信号进行处理,然后提出了信号的AR1和AR2模型,最后通过小波分析的方式对AR模型进行参数估计;通过对小波滤波方法进行深入的研究,提出采用Donoho 等提出的阈值滤波法,并将噪声的参数估计与信号滤波分别在两个模块并行处理,提高了处理的速度,同时,推导出了可以直接把噪声各尺度下的小波分解系数方差作为对应尺度下噪声的方差,代入到阈值公式中,得到对应尺度下的阈值,进行滤波。该方法只需进行各尺度下的小波分解系数方差计算,而不必对噪声参数的具体值进行估计,所以进一步简化了算法,提高了信号处理的速度。而且,为了能进行实时处理,对信号在滤波时的输入方式和信号的延拓方式作了重大的改进,通过这样的改进,能够对信号进行逐点滤波处理。同时,在具体的实现上通过提升小波变换,并在具有一定的滤波效果的基础上,选用滤波器短的小波基,使得计算简单、速度更快。该算法为光电跟踪系统的实时信号处理提供了一种新的方法。 对于机动平台光电跟踪系统中不同传感器信号的融合,根据小波变换系数的物理意义,提出引入小波系数方差作为低频信号的特征量,在小波滤波之后,构造低频系数的权值,实现机动平台光电跟踪系统两种不同带宽的惯性传感器信号的融合。由于该算法是在信号的小波去噪基础上,即在对信号小波系数进行阈值处理后进行,因此,只增加少量的计算便可实现信号的融合。所以,该算法可与小波实时滤波方法有效结合起来,实现光电跟踪系统惯性传感器信号的实时滤波和融合处理。 论文还介绍了硬件实验平台的系统组成与结构特点,对本文算法的硬件实现进行了研究,通过对系统硬件和软件上的合理分配、算法的简化和优化措施,实现了机动平台光电跟踪系统惯性传感器信号的实时滤波处理。 本论文针对低信噪比的机动平台光电跟踪系统惯性传感器信号的难点和关键技术,结合工程实际,提出了一些针对性很强的思路和方案,为提高光电跟踪系统的水平提供了有力的保证。
英文摘要: The technique of processing for inertial sensor’s signal has been the key technique of the electro-optical tracting system on moving bed. Aiming at the technique, this Ph D dissertation developed some effective methods and algorithms to solve some difficulties in this area. Based on the analysis of the characteristics of inertial sensor’s noises, the characteristics of the noise in the wavelet field are studied. The relationship of the characteristics and it’s wavelet coefficient, and the spread property of the wavelet coefficient are described. Some new technologies and methods are presented in the processing of inertial sensor’s signal. A Kalman filtering method are presented, which mend the AR model,and then can process the non-zero mean data. Based on it, the models of signal for AR1 and AR2 are presented.And it’s parameteres are estimated by wavelet method. Based on the study for wavelet filtering methods, the threshold method by Donoho is presented, and parameters estimation of noises and filtering is made respectively by two models in order to be fast in processing signal, and deduce that wavelet coefficients’ variance can directly compute the thresholds every decompose layer by threshold formula. This method only need to compute wavelet coefficients’ variance every decompose layer, need not to estimate parameters of noises. Therefore, the algorithm is more simplied and the processing is fast. Moreover, in order to be real-time in processing the extension is mended, and then the signal can be filtered with one point by one point. And that it is implemented by the lifting wavelet transform, and choose wavelets with shorter filter in length when obtain stated efficiency, so the calculation is more simple and more fast. This Algorithm offer a new method for real-time filtering to the electro-optical tracting system. For fusion of two inertial sensors’ signal in electro-optical tracking system on moving bed that operate in different frequency ranges, coefficients’ variance is introduced and based on it to construct the weighed coefficients of the low frequency signal for lts fusion of sensors after filtering by wavelet. Based on wavelet filtering, namely that the fusion is after threshoding the wavelet coefficients, so that the calculation is simple in the fusion. Therefore, the fusion and wavelet filtering can integrate effectivly to carry out the real-time filtering and fusion for inertial sensors’ signal in electro-optical tracking system on moving bed. This dissertation also introduced component and configurable characteristic of the hardware platform based on PC/104, and studied the algorithm’s implement by hardware. By reasonably configuring hardware and software, and simplying the algorithm, and empolying optimizing measure, real-time filtering has been realized for inertial sensors’ signal in electro-optical tracking system. In conclusion, some innovated methods are developed to solve the difficulties of processing for the electro-optical tracking system inertial sensors’ signal in low SNR. The work of this dissertation has important reference to the future development of the electro-optical tracking system on moving bed.
语种: 中文
内容类型: 学位论文
URI标识: http://ir.ioe.ac.cn/handle/181551/224
Appears in Collections:光电技术研究所博硕士论文_学位论文

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Recommended Citation:
蒋行国. 基于小波分析的机动平台光电跟踪系统的信号处理与研究[D]. 光电技术研究所. 中国科学院光电技术研究所. 2007.
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