On the nonlinear integral transform of an ocean wave spectrum into an along-track interferometric synthetic aperture radar image spectrum

We present a new nonlinear integral transform relating the ocean wave spectrum to the along-track interferometric synthetic aperture radar (AT-INSAR) image spectrum. The AT-INSAR, which is a synthetic aperture radar (SAR) employing two antennas displaced along the platform's flight direction, is considered to be a better instrument for imaging ocean waves than the SAR. This is because the AT-INSAR yields the phase spectrum and not only the amplitude spectrum as with the conventional SAR. While the SAR and AT-INSAR amplitude spectra depend strongly on the modulation of the normalized radar cross section (NRCS) by the long ocean waves, which is poorly known, the phase spectrum depends only weakly on this modulation. By measuring the phase difference between the signals received by both antennas, AT-INSAR measures the radial component of the orbital velocity associated with the ocean waves, which is related to the ocean wave height field by a well-known transfer function. The nonlinear integral transform derived in this paper differs from the one previously derived by Bao et al. [1999] by an additional term containing the derivative of the radial component of the orbital velocity associated with the long ocean waves. By carrying out numerical simulations, we show that, in general, this additional term cannot be neglected. Furthermore, we present two new quasi-linear approximations to the nonlinear integral transform relating the ocean wave spectrum to the AT-INSAR phase spectrum.

小波多尺度跟踪技术研究

本论文研究的主要内容为基于小波多尺度特性的序列图像目标跟踪技术。目标跟踪作为一个在军事、工业和科学研究方面有着广泛应用背景的研究领域，一直以来吸引了大批国内外学者。由于小波变换具有多分辨率分析的特点，而且在时频两域都具有表征信号局部特征的能力，使得基于小波变换的目标跟踪算法具有传统算法无法比拟的优势。针对目标跟踪技术的研究现状和存在问题，本文着重从目标分割和特征检测与匹配两个角度对基于小波变换的几种新的目标跟踪方法进行了研究。 1. 采用基于多尺度Gabor小波的特征点检测算法对序列图像进行跟踪。借助图像的金字塔变换得到多尺度的Gabor小波特征图像，并对特征图像进行特征点检测，提取对图像变换具有鲁棒性的特征。针对两种特征检测方案，提出不同的特征匹配准则，按照分层匹配的策略由粗到精逐步定位目标的准确位置，具有较快的搜索速度。 2. 采用多尺度小波函数所提取的相位一致性特征进行基于目标分割和基于角点特征的跟踪。 对目标图像进行相位一致性检测，得到一个具有光照不变性的无量纲特征量—相位一致系数。利用相位一致性检测的这种特性，针对孤立目标的情况，提出了两种自适应目标分割和跟踪的算法。基于区域增长的目标分割算法利用从相位一致图像中找到的对比度最大点及其法线方向两边的灰度分布确定目标和背景的种子像素，进行自适应目标分割。基于相位一致性检测的目标分割算法只需确定一个阈值即可利用相位一致特征图像的方向性，依据目标在不同方向响应的不同将目标和背景区分开，适应于复杂纹理背景中的目标分割。最后，分别将两种算法所得的分割结果向水平和垂直方向投影即可确定各自的质心位置，实现自适应的质心跟踪。 进一步提取相位一致性图像的最小矩特征就能得到目标的角点信息。文中用实验验证了此方法检测到角点的综合性能。在此基础上，提出了利用单演相位差进行角点匹配跟踪的算法，并将其同基于灰度相关的匹配算法进行了对比，证明了本算法能够检测出更多准确匹配的角点、减少误匹配，同时具有较小的匹配运算量。 对以上提出的几种目标跟踪算法进行了大量的仿真实验，实验结果表明，这几种方法均取得了较好的跟踪效果，能够实现稳定、精确的跟踪。

基于匹配追踪方法的地震信号时频分析研究

Seismic signal is a typical non-stationary signal, whose frequency is continuously changing with time and is determined by the bandwidth of seismic source and the absorption characteristic of the media underground. The most interesting target of seismic signal’s processing and explaining is to know about the local frequency’s abrupt changing with the time, since this kind of abrupt changing is indicating the changing of the physical attributes of the media underground. As to the seismic signal’s instantaneous attributes taken from time-frequency domain, the key target is to search a effective, non-negative and fast algorithm time-frequency distribution, and transform the seismic signal into this time-frequency domain to get its instantaneous power spectrum density, and then use the process of weighted adding and average etc. to get the instantaneous attributes of seismic signal. Time-frequency analysis as a powerful tool to deal with time variant non-stationary signal is becoming a hot researching spot of modern signal processing, and also is an important method to make seismic signal’s attributes analysis. This kind of method provides joint distribution message about time domain and frequency domain, and it clearly plots the correlation of signal’s frequency changing with the time. The spectrum decomposition technique makes seismic signal’s resolving rate reach its theoretical level, and by the method of all frequency scanning and imaging the three dimensional seismic data in frequency domain, it improves and promotes the resolving abilities of seismic signal vs. geological abnormal objects. Matching pursuits method is an important way to realize signal’s self-adaptive decomposition. Its main thought is that any signal can be expressed by a series of time-frequency atoms’ linear composition. By decomposition the signal within an over completed library, the time-frequency atoms which stand for the signal itself are selected neatly and self-adaptively according to the signal’s characteristics. This method has excellent sparse decomposition characteristics, and is widely used in signal de-noising, signal coding and pattern recognizing processing and is also adaptive to seismic signal’s decomposition and attributes analysis. This paper takes matching pursuits method as the key research object. As introducing the principle and implementation techniques of matching pursuits method systematically, it researches deeply the pivotal problems of atom type’s selection, the atom dictionary’s discrete, and the most matching atom’s searching algorithm, and at the same time, applying this matching pursuits method into seismic signal’s processing by picking-up correlative instantaneous messages from time-frequency analysis and spectrum decomposition to the seismic signal. Based on the research of the theory and its correlative model examination of the adaptively signal decomposition with matching pursuit method, this paper proposes a fast optimal matching time-frequency atom’s searching algorithm aimed at seismic signal’s decomposition by frequency-dominated pursuit method and this makes the MP method pertinence to seismic signal’s processing. Upon the research of optimal Gabor atom’s fast searching and matching algorithm, this paper proposes global optimal searching method using Simulated Annealing Algorithm, Genetic Algorithm and composed Simulated Annealing and Genetic Algorithm, so as to provide another way to implement fast matching pursuit method. At the same time, aimed at the characteristics of seismic signal, this paper proposes a fast matching atom’s searching algorithm by means of designating the max energy points of complex seismic signal, searching for the most optimal atom in the neighbor area of these points according to its instantaneous frequency and instantaneous phase, and this promotes the calculating efficiency of seismic signal’s matching pursuit algorithm. According to these methods proposed above, this paper implements them by programmed calculation, compares them with some open algorithm and proves this paper’s conclusions. It also testifies the active results of various methods by the processing of actual signals. The problems need to be solved further and the aftertime researching targets are as follows: continuously seeking for more efficient fast matching pursuit algorithm and expanding its application range, and also study the actual usage of matching pursuit method.