Joint time-frequency analysis for radar signal and image processing

The Fourier transform has been widely used in radar signal and image processing. When the radar signals exhibit time- or frequency-varying behavior, an analysis that can represent the intensity or energy distribution of signals in the joint time-frequency (JTF) domain is most desirable. In this article, we showed that JTF analysis is a useful tool for improving radar signal and image processing for time- and frequency-varying cases. We applied JTF analysis to radar backscattering and feature extraction; we also examined its application to radar imaging of moving targets. Most methods of JTF analysis are non-parametric. However, parametric or model-based methods of time-frequency analysis, such as adaptive Gaussian and chirplets, are more suitable for radar signals and images     

基于时频分析的ISAR成像

传统的逆合成孔径雷达(ISAR)成像算法用傅里叶变换进行频谱分析,对机动目标的成像会导致成像模糊。本文应用短时傅里叶变换(STFT)、魏格纳分布(WVD)、平滑伪魏格纳分布(SPWVD)几种时频分析方法对机动目标进行瞬时成像。仿真和实验结果表明,通过该方法能得到清晰的目标的距离-瞬时多普勒像。     

Joint time-frequency analysis

It has been well understood that a given signal can be represented in an infinite number of different ways. Different signal representations can be used for different applications. For example, signals obtained from most engineering applications are usually functions of time. But when studying or designing the system, we often like to study signals and systems in the frequency domain. Although the frequency content of the majority of signals in the real world evolves over time, the classical power spectrum does not reveal such important information. In order to overcome this problem, many alternatives, such as the Gabor (1946) expansion, wavelets, and time-dependent spectra, have been developed and widely studied. In contrast to the classical time and frequency analysis, we name these new techniques joint time-frequency analysis. We introduce the basic concepts and well-tested algorithms for joint time-frequency analysis. Analogous to the classical Fourier analysis, we roughly partition this article into two parts: the linear (e.g., short-time Fourier transform, Gabor expansion) and the quadratic transforms (e.g., Wigner-Ville (1932, 1948) distribution). Finally, we introduce the so-called model-based (or parametric) time-frequency analysis method     

Direction of arrival estimation using adaptive directional time-frequency distributions

Time-frequency distributions (TFDs) allow direction of arrival (DOA) estimation algorithms to be used in scenarios when the total number of sources are more than the number of sensors. The performance of such time–frequency (t–f) based DOA estimation algorithms depends on the resolution of the underlying TFD as a higher resolution TFD leads to better separation of sources in the t–f domain. This paper presents a novel DOA estimation algorithm that uses the adaptive directional t–f distribution (ADTFD) for the analysis of close signal components. The ADTFD optimizes the direction of kernel at each point in the t–f domain to obtain a clear t–f representation, which is then exploited for DOA estimation. Moreover, the proposed methodology can also be applied for DOA estimation of sparse signals. Experimental results indicate that the proposed DOA algorithm based on the ADTFD outperforms other fixed and adaptive kernel based DOA algorithms.     

Ultrafast optical distortion equalizer using time-frequency domain processing

An ultrafast optical distortion equalizer using time-frequency domain processing that allowed bitwise adaptive compensation of ad hoc optical distortions is described. In the time-frequency domain, because all distortions are distilled to variations in the arrival times of each multiplexed spectral component, they can be treated as one time-frequency distribution. Spatial channels are used to separate spectral components in a distorted bit pulse into plural channels for time-frequency demultiplexing (TF-DEMUX). After TF-DEMUX, temporal and spectral adjustments are achieved through a hard-wired optical delay line for each spatial channel so that each fixed time difference can be canceled. The operation of the proposed ultrafast optical distortion equalizer for uncharacterized optical distortion of chromatic dispersion and timing jitter ranging from several picoseconds to subpicoseconds was demonstrated.     

Transient interference excision in over-the-horizon radar using adaptive time-frequency analysis

The performance of over-the-horizon radars (OTHRs) degrades significantly due to transient interferences such as lightning, meteor echoes, and man-made impulse bursts. The conventional techniques used to suppress these transient interferences generally follow a simple time-domain procedure which first localizes the transient, sets the corrupted time samples to zero, and follows by data reconstruction via interpolations. However, for an OTHR operation with a short coherent integration time (CIT) of a few seconds, the transient interference may corrupt a significant portion of the received data such that data interpolation can become erroneous. In this paper, a new transient interference excision technique is proposed. It utilizes the adaptive time-frequency analysis technique to parameterize the radar signal. The transient interference can then be identified and subsequently removed via their characteristic parameters. No data interpolation is needed. The performance of the proposed technique is illustrated by simulated and experimental OTHR data.     

An adaptive optimal-kernel time-frequency representation

Time-frequency representations with fixed windows or kernels figure prominently in many applications, but perform well only for limited classes of signals. Representations with signal-dependent kernels can overcome this limitation. However, while they often perform well, most existing schemes are block-oriented techniques unsuitable for on-line implementation or for tracking signal components with characteristics that change with time. The time-frequency representation developed in the present paper, based on a signal-dependent radially Gaussian kernel that adapts over time, surmounts these difficulties. The method employs a short-time ambiguity function both for kernel optimization and as an intermediate step in computing constant-time slices of the representation. Careful algorithm design provides reasonably efficient computation and allows on-line implementation. Certain enhancements, such as cone-kernel constraints and approximate retention of marginals, are easily incorporated with little additional computation. While somewhat more expensive than fixed kernel representations, this new technique often provides much better performance. Several examples illustrate its behavior on synthetic and real-world signals     

Space-time adaptive processing using circular arrays

A direct data-domain (D³) least-squares space-time adaptive-processing (STAP) approach is presented for adaptively enhancing radar signals in a non-homogeneous environment of jammers, clutter, and thermal noise, utilizing a circular antenna array. The non-homogeneous environment may consist of non-stationary clutter. The D ³ approach is applied directly to the data collected by a circular antenna array (utilizing space), and in time (Doppler) diversity. Conventional STAP generally utilizes statistical methodologies, based on estimating a covariance matrix of the interference, using the data from various range cells of the circular array and assuming that it is a uniform linear array. However, for highly transient and inhomogeneous environments, the conventional statistical methodology may be difficult to apply. Moreover, the error in forming the covariance matrix by assuming that the data collected by the circular array is assumed to be a uniform linear array is highly problem dependent. Hence the D³ method is presented, as it analyzes the data in space and time over each range cell separately. However, it treats the antenna array as circular, i.e., it deals with the antenna structure in its proper form. Limited examples are presented to illustrate the application of this approach     

Joint time-frequency analysis for investigation of layered masonry structures using penetrating radar

Joint time-frequency analysis of penetrating radar response has been tested using simulated and experimental data as a processing tool for detecting stratified features inside a masonry structure. The radar response has been simulated exploiting the formalism of the propagators inside a finite number of dielectric layers. Experimental testing has been carried out by varying the thickness of the hollow space between two walls.     

Color image processing using adaptive multichannel filters

New adaptive filters for color image processing are introduced and analyzed. The proposed adaptive methodology constitutes a unifying and powerful framework for multichannel signal processing. Using the proposed methodology, color image filtering problems are treated from a global viewpoint that readily yields and unifies previous, seemingly unrelated, results. The new filters utilize Bayesian techniques and nonparametric methodologies to adapt to local data in the color image. The principles behind the new filters are explained in detail. Simulation studies indicate that the new filters are computationally attractive and have excellent performance.     

Joint time-frequency measurements of modelocked semiconductor diodelasers and dynamics using frequency-resolved optical gating

Joint time-frequency ultrafast measurements using frequency-resolved optical gating (FROG) have been used to provide a fundamental understanding of: (1) ultrashort pulse propagation in semiconductor optical amplifiers; (2) the modelocking dynamics in external cavity semiconductor diode lasers; and (3) correlated multiple-wavelength generation from mode locked semiconductor lasers. The pulse shaping and chirping effects measured by FROG are shown to be attributed to intracavity gain and saturable absorbing dynamics, as well as group velocity dispersion. In addition, the intracavity gain dynamics show a regime of transient unsaturated gain, which can be exploited to allow phase-correlated multiple-wavelength modelocked operation from a single-stripe external-cavity semiconductor diode laser. In this case, FROG techniques are used to understand the underlying mechanisms involved in the phase correlation process     

Translational motion compensation in ISAR image processing

In inverse synthetic aperture radar (ISAR) imaging, the target rotational motion with respect to the radar line of sight contributes to the imaging ability, whereas the translational motion must be compensated out. This paper presents a novel two-step approach to translational motion compensation using an adaptive range tracking method for range bin alignment and a recursive multiple-scatterer algorithm (RMSA) for signal phase compensation. The initial step of RMSA is equivalent to the dominant-scatterer algorithm (DSA). An error-compensating point source is then recursively synthesized from the selected range bins, where each contains a prominent scatterer. Since the clutter-induced phase errors are reduced by phase averaging, the image speckle noise can be reduced significantly. Experimental data processing for a commercial aircraft and computer simulations confirm the validity of the approach.     

Efficient radar target classification using adaptive jointtime-frequency processing

This paper presents a new target recognition scheme via adaptive Gaussian representation, which uses adaptive joint time-frequency processing techniques. The feature extraction stage of the proposed scheme utilizes the geometrical moments of the adaptivity spectrogram. For this purpose, we have derived exact and closed form expressions of geometrical moments of the adaptive spectrogram in the time, frequency, and joint time-frequency domains. Features obtained by this method can provide substantial savings of computational resources, preserving as much essential information for classifying targets as possible. Next, a principal component analysis is used to further reduce the dimension of feature space, and the resulting feature vectors are passed to the classifier stage based on the multilayer perceptron neural network. To demonstrate the performance of the proposed scheme, various thin-wire targets are identified. The results show that the proposed technique has a significant potential for use in target recognition     

Wide-band adaptive array processing using pole-zero digital filters

Conventional broad-band array processing is accomplished by linearly combining the outputs of tapped-delay lines attached to each sensor of an array. This type of processing can be interpreted as using an all-zero digital filter at each sensor to generate a frequency-dependent magnitude and phase shift (weighting) over the array operating bandwidth. A new array processing method is presented which uses digital filters having both poles and zeros to perform the frequency-dependent array weighting. Several algorithms for adapting the pole-zero array filters are introduced. Computer simulations are then presented demonstrating the potential for substantial improvement in broad-band interference nulling provided by pole-zero array processing.     

Signal processing in adaptive arrays using power basis

The development of the theory of adaptive arrays (AAs) is proposed based on the representation of the inverse covariance matrix (CM) of a noise in the AA channels as a finite power series expansion using the direct CM and by representation of a weight vector of the AA as a finite series expansion of the power vectors. The dimension of the power CM basis is equal to the power of the minimum polynomial of the CM. In the case when the number of external interference sources is less than the number of AA channels, such polynomials have the same fundamental role as the characteristic polynomial of the CM in an opposite case. Proofs for the existence of the above mentioned polynomials of the CM are given. A new method for the calculation of the polynomial coefficients is presented, and the physical properties of the power vector basis are studied. It is shown that the power vectors are correlated and that there are two stages of AA signal processing.     

逆合成孔径雷达图像仿真研究

实现了从目标建模、散射计算到雷达图像仿真的全过程.首先使用AutoCAD软件对目标建模并进行三角面元离散,采用反方向光学射线追踪技术进行消隐,以物理光学近似理论计算目标的后向散射,最后利用逆合成孔径雷达成像方法得到了目标的雷达仿真图像.球体的计算结果验证了物理光学的正确性,坦克和飞机模型的仿真成像结果与实验结果和CAD模型的对比进一步证实了目标仿真成像系统的有效性.     

Joint time-frequency interpretation of scattering phenomenology indielectric-coated wires

The scattering phenomenology in dielectric-coated wire structure is investigated using the joint time-frequency processing of simulated and measurement data. The method of moments is applied to carry out the simulation. The computed results are compared to measured data in both the frequency and time domains. The scattering data are next analyzed in the joint time-frequency plane by using the short-time Fourier transform (STFT) technique to provide further insight into the scattering phenomenology. The dispersive Goubau mode excited along the coated wire can be clearly observed in the joint time-frequency plane. In addition, the time-frequency distribution series, which improves the resolution of the STFT while overcoming the cross-term interference problem of the Wigner-Ville distribution, is applied to better identify backscattering returns that are difficult to resolve in the joint time-frequency plane     

ISAR成像实时处理系统设计

根据ISAR成像的特点研究并设计了ISAR实时成像处理系统,主要包括:信号处理模块的硬件采用分布式并行的设计方式;系统的数据通信采用串并转换及链路口和交换机相结合的方法;根据外场实验的参数和目标特征,选择适于ISAR实时成像的算法,并对算法的运算量进行了分析,在此基础上进行了实时处理在信号处理板卡上的任务分配。最后的试验结果和分析验证了该系统的有效性。