A low complexity coherent integration method for maneuvering target detection

This paper considers the coherent integration problem for detecting a maneuvering target with complex motions, where the velocity, acceleration and jerk result in respectively the range migration (RM), linear Doppler frequency migration (LDFM) and quadratic Doppler frequency migration (QDFM) within the coherent pulse interval. A new coherent integration algorithm based on keystone transform (KT) and generalized dechirp process (GDP), i.e., KTGDP, is proposed. In this method, KT and fold factor searching are first employed to correct the RM, and then GDP is applied to estimate the target's radial acceleration and jerk. With the estimated motion parameters, LDFM and QDFM can be compensated and the coherent integration can be achieved via Fourier transform. In addition, at the cost of some performance loss, a fast coherent integration method combing KT and cubic phase function (CPF), i.e., KTCPF, is also introduced to further reduce the computational complexity. Compared with the generalized Radon–Fourier transform (GRFT) method, the proposed algorithms can avoid the blind speed side lobe (BSSL) effect and have much lower computational burden. Finally, we evaluate the performance via some numerical simulations.     

Fast implementation of ground maneuvering target focusing

Ground maneuvering target detection is a hot topic in the applications of synthetic aperture radar (SAR), whereas its focusing performance is severely deteriorated by range migration and Doppler frequency migration during a long integration time. This paper proposes a novel method to image the target and estimate its parameters via performing two independent 2-dimensional (2-D) searches after a parameter separation operation. In order to improve the search speed, we set the limited search ranges and propose local mapping sparse Fourier transform (LMSFT) to replace fast Fourier transform (FFT). Compared with the traditional algorithms, the proposed method can realize fast coherent integration of multiple maneuvering targets via compensating the high-order range migration and Doppler frequency migration. In addition, the proposed method is stable under noise. Several simulation results have validated the effectiveness of the proposed method.     

A new coherent integration method for weak maneuvering target with jerk motion

In radar systems, prolonging the integration time helps to improve the detection and estimation performance of weak target. However, in a long integration time, target's high-order motion is non-ignorable, which causes high-order range migration (RM) and Doppler frequency migration (DFM) problems. The RM and DFM defocus the integration result and lead to detection and estimation performance loss. In this paper, a new coherent integration method is proposed for maneuvering radar target with jerk motion. First, we define a new cross correlation function basing on non-uniformly resampling technique. By using this function, we eliminate the range migration (RM) and Doppler frequency migration (DFM) caused by target's velocity and acceleration. Then, matched filtering process is performed to compensate the phase error caused by target's jerk motion. Finally, the integration is achieved by summing signal data in different range frequency cells and performing a two-dimensional fast Fourier transform (FFT). The proposed method is applicable for low signal-to-noise ratio (SNR) environment. Compared with the GRFT algorithm, the proposed method has a close integration performance and a lower computational complexity. The effectiveness is validated by simulations and real-data processing results.     

Parameter estimation for maneuvering targets with complex motion via scaled double-autocorrelation transform

In this paper, a novel parameter estimation method is proposed for maneuvering targets with complex motion. In the proposed method, the second-order keystone transform (SOKT) and modified range cell migration correction (RCMC)/integration are jointly applied to overcome the velocity ambiguity and eliminate the envelope migration. Then, since the azimuth echoes of maneuvering targets with complex motion can be modeled as cubic phase (CP) signals after motion compensation, a new transform, namely, scaled double-autocorrelation transform (SCDCT), is defined. This transform can be essentially interpreted as the two-dimensional (2-D) Fourier transform (FT) of a scaled parametric instantaneous double-autocorrelation (PIDAC) function. By employing this derived transform, the estimated chirp rates and derivative of chirp rates of CP signals can be obtained simultaneously without searching operation and thus the computational burden can be reduced significantly. Furthermore, the characteristics of cross terms and anti-noise performance of SCDCT are theoretically analyzed. Compared with three other popular methods, product high-order match phase transform, TC-dechirp Clean and modified discrete chirp Fourier transform, the proposed SCDCT-based method is more computationally efficient and has better estimation performance in low signal-to-noise ratio (SNR) circumstance. Simulation results verify the effectiveness of the proposed SCDCT-based method.     

A novel STAP method for the detection of fast air moving targets from high speed platform

Space-time adaptive processing (STAP) is an effective method for detecting moving target in airborne/spaceborne radar.However,the detection of fast air moving targets from high speed platform is challenged by the range walk of both the clutter and moving targets.It is well known that keystone formatting can be used to compensate for the range walk of multiple moving targets simultaneously without using the knowledge of the motion parameters.However,in the presence of serious Doppler ambiguity of fast air moving targets,distribution of the clutter will be affected by the keystone formatting matched to the ambiguity number of targets,and as a result the STAP performance will degrade.In this paper,a novel STAP method is proposed for the detection of fast air moving targets from high speed platform,which can deal with the range walk of both clutter and targets.Effectiveness of the new method is verified via simulation examples.     

基于分数阶傅里叶变换的LMS自适应滤波

分数阶傅里叶变换是一种线性变换,在多分量情况下不像Wigner-Ville分布那样受到交叉项的影响。但是当信号的信噪比比较小时,检测的效果就比较差,文中提出了一种基于分数阶傅里叶变换的LMS自适应滤波算法。实验结果表明,这种方法在低信噪比的情况下能够有效地检测出信号。另外,如果在自适应过程中采用变步长,可以加快收敛速度,可以显著地减少运算量。     

Time–frequency analysis for moving ship targets in GEO spaceborne/airborne bistatic SAR imaging based on a GEO satellite transmitter

The track of a synthetic aperture radar (SAR) system mounted on a geosynchronous orbit (GEO) satellite can be curvilinear. GEO spaceborne/airborne bistatic (SA-Bi) SAR, which employs a GEO satellite as a transmitter and an aeroplane that works on the ‘receive-only’ mode as a receiver, is distinct from general bistatic SAR systems. During coherent integration time, the rotation of ship targets results in a time-varying Doppler frequency shift that produces a smeared Doppler spectrum and a blurred image after Fourier transform. This article mainly studies the time–frequency characteristics of ship targets in a GEO SA-Bi SAR system. First, the geometric model of a GEO SA-Bi SAR in ship target imaging is built, and the Doppler frequency shift function induced by target rotation is derived in detail. Then, the instantaneous frequency characteristics of moving targets are analytically calculated based on the time–frequency analysis method. Moreover, a rotation target imaging strategy of GEO SA-Bi SAR based on the time–frequency analysis method is proposed and tested with simulation data, which demonstrates the correctness and effectiveness of the proposed method.     

时变单音干扰下GPS系统的性能分析及干扰抑制方法

针对实际系统中干扰源的移动特性,将干扰信道建模为时变瑞利衰落信道,分析了时变单音干扰下多普勒频移对GPS系统性能的影响并推导了误码率公式,提出了一种基于短时傅里叶的自适应时频域干扰抑制算法。该算法首先对接收到的GPS信号进行时频分析,检测出受到干扰的频点,然后设置合理的干扰剔除门限,最后通过自适应二阶格型陷波器将所受干扰的频点剔除。并与基于lms的时域自适应滤波算法进行了对比,实验证明,所提算法对时变单音干扰具有较好的干扰抑制能力。     

基于信息总线服务的智能数据流自适应集成方法

为了提高信息服务中心的智能数据处理能力,提出一种基于信息总线服务的智能数据流自适应集成方法,构建智能数据流自适应信息处理模型,采用统计时间序列分析方法进行信息总线服务的大数据处理和信号集成,采用同步与匹配滤波方法进行信息总线服务数据信息流的滤波检测,采用快速傅里叶变换进行智能数据流自适应集成信号滤波处理,提高智能数据流的纯度。采用重叠保留法实现信息总线服务数据流的混频处理,构建相关匹配检测器实现信息总线服务数据流的分路、插零、取反等运算,根据运算结果实现智能数据流自适应集成。仿真结果表明,采用该方法进行信息总线服务数据流集成的混频性能较好,提高了信息总线服务数据流的检测和信息融合能力,提高了输出信噪比。     

小波分析在中巴资源一号卫星图像预处理中的应用

中巴资源一号卫星图像中存在比较明显的条带干扰现象。本文利用小波分析的多分辨和局部化性质，根据卫星探测头中探测元的个数，精确地选择小波分解中相应的子图进行处理，针对性强，效果理想。在处理反射层尾现象和保持图像信息方面，该方法优于基于Fourier分析的频域滤波方法。实验表明该方法效果良好。     

A statistical and Wiener filtering algorithm based on the curvelet transform for SAR images

Synthetic aperture radar (SAR) images contain many kinds of noise. Speckle noise is multiplicative noise generated by the coherent imaging processes involved in SAR images and brings a great hindrance to the interpretation and application of SAR images, so it is considered the first major kind of noise in SAR images. SAR images also contain other incoherent additive noises generated by other factors, such as Gaussian noise, which are all considered the second major kind of noise. In order to reduce the impact of noise as much as possible, after an in-depth study of SAR imaging and noise-generating mechanism, curvelet transform principle, and Wiener filtering characteristic, a novel filtering method, here called the statistical and Wiener based on curvelet transform (SWCT) method is proposed. The SWCT algorithm processes two different kinds noise based on their properties. Specifically, it establishes a two-tiered filtering framework. For the first kind of noise, the algorithm uses the curvelet transform to decompose the SAR image and uses the statistical characteristics of the SAR image to generate an adaptive filtering threshold of the coefficients of decomposition to recover the original image. Then it filters every sub-band image at each decomposed scale and performs the inverse curvelet transform. The second kind of noise is directly filtered using the Wiener filter in the SWCT algorithm. Using the two-tiered filtering model and fully exploiting statistical characteristics, the SWCT algorithm not only reduces the amount of coherent speckle noise and incoherent noise effectively but also retains the edges and geometric details of the original SAR image. This is very good for target detection, classification, and recognition. Qualitative and quantitative tests were performed using simulated speckle noise, Gaussian noise, and real SAR images. The proposed SWCT algorithm was found to remove noise effectively and the performance of the algorithm was tested and compared to the mean filter, enhanced gamma-MAP (maximum a posterior probability) filter, wavelet transform filter, Wiener filter, and curvelet transform filter. Experiments carried out on real SAR images confirmed that the new method has a good filtering effect and can be used on different SAR images.     

An approach for refocusing of ground fast-moving target and high-order motion parameter estimation using Radon-high-order time-chirp rate transform

Long synthetic aperture time can improve the imaging quality of a ground moving target, whereas a moving target may be severely smeared in the cross-range image due to the range migration and the Doppler frequency migration. In this paper, the effects of the third-order Doppler broadening and Doppler ambiguity of a fast-moving target are considered. To address these issues, a novel motion parameter estimation method named high-order time-chirp rate transform (HTRT) is proposed, and then a new synthetic aperture radar (SAR) imaging method based on Radon-HTRT (RHTRT) for a ground moving target is developed. The major contributions are as follows: 1) The proposed SAR imaging method can eliminate the Doppler ambiguity effect. 2) The proposed method can realize longer time coherent integration than Radon–Fourier transform (RFT) and Radon–fractional Fourier transform (RFRFT) methods. 3) The proposed method is computationally efficient since HTRT can obtain the motion parameters of a moving target via performing the 2-dimensional (2-D) fast Fourier transform (FFT). Both the simulated and real data processing results show that the proposed method can finely image a ground moving target in a high signal-to-clutter and noise ratio (SCNR) environment.     

Frequency division multiplexing technique for composite ambiguity function approximation

In this paper, we propose a frequency division multiplexing (FDM) diversity waveform technique to achieve a delay-Doppler response that approximates the composite ambiguity function (CAF) in a multiple target scenario. First, a channel estimate based on maximum likelihood estimation (MLE) for each subband is provided, and the signal to noise power ratio (SNR) needed to achieve the specific variance requirement of the MLE is derived. Next, using the Doppler transformation, we show how to combine the output response of each subband undergoing a different Doppler shift. Then the CAF approximation of each detected target is derived based on the coherent CLEAN algorithm. Finally, the CAF approximation of all the targets is attained, having the improved delay-Doppler resolution and thus providing a way to resolve initially undetected targets using a recursive procedure.     

基于DFRFT的脉压方法及与匹配滤波性能对比

针对线性调频体制雷达的目标检测与测距,提出采用离散分数阶Fourier变换实现脉压,推导基于采样型离散分数阶Fourier变换脉压方法的理论模型,对采用离散分数阶Fourier变换实现脉压时出现的时延模糊进行分析,提出用与谱峰相邻数据的相位差估计真实雷达目标的时延解决模糊问题。在雷达目标检测与测距中,基于离散分数阶Fourier变换与匹配滤波的脉压方法相比,距离分辨力相同,运算量降低近一半。     

An Extension of Fourier-Wavelet Volume Rendering by View Interpolation

This paper describes an extension to Fourier-wavelet volume rendering (FWVR), which is a Fourier domain implementation of the wavelet X-ray transform. This transform combines integration along the line of sight with a simultaneous 2-D wavelet transform in the view plane perpendicular to this line. During user interaction, only low resolution images are computed based on wavelet approximation coefficients. When user interaction ceases, the images are refined incrementally with the wavelet detail coefficients. The extension proposed in this paper is similar to a technique called view interpolation, which originates from the field of computer graphics. View interpolation is used to speed up rendering of complex scenes by precomputing images from a number of selected viewpoints. For intermediate viewpoints, rendering is performed by interpolating the precomputed images. In this paper, we show that for FWVR the speed of rendering low resolution images is increased by interpolation of precomputed sets of wavelet approximation coefficients in the Fourier domain. The differences with traditional view interpolation are that (i) interpolation is performed on the wavelet approximation coefficients in the Fourier domain and not on images, and (ii) interpolation is performed during user interaction only. When interaction ceases, ordinary FWVR progressively renders an image at high quality. Medical CT data are used to assess the accuracy and performance of the method. We use regular angular sampling of spherical coordinates which determine the viewing direction. The results show that angle increments as large as 10 degrees result in only a small degradation of image quality.     

视频文字大小自适应提取算法*

视频文字大小自适应提取算法基于离散傅里叶变换(discrete Fourier transform, DFT)特征、多分辨率处理及支持向量机分类技术。算法在不同分辨率下结合梯度信息、文字边界定位技术提取出文字候选区域,然后用支持向量机对于候选图像块DFT特征作进一步分类。结果表明,该算法能提取出视频图像中不同大小的文字,识别率优于以小波、灰度、离散余弦变换系数(discrete cosine transform, DCT)等为纹理特征的算法。     

斜视SAR分数阶距离多普勒成像算法

针对传统距离多普勒(RD)算法在斜视合成孔径雷达(SAR)成像时运算量不足和边缘存在干扰的问题,将传统的RD算法中的匹配滤波用分数阶Fourier变换替代,提出基于分数阶Fourier变换SAR斜视距离多普勒成像(FrRD)算法。理论分析斜视SAR回波信号分数阶Fourier变换域模型,徙动校正在距离分数阶Fourier域方位频域完成,给出FrRD算法仿真流程。仿真结果表明,与传统RD算法相比,FrRD算法成像副瓣更低,成像边界更加清晰,成像时间缩短近一半。     

强杂波背景下LPI信号的模糊检测方法

研究了强杂波干扰背景下运用模糊集合理论解决低截获概率信号（LPI）雷达信号的检测问题,分析了在无源雷达体制下获取有效目标信号的方法,并指出了传统匹配滤波方法的局限性.针对该问题,给出了四种模糊集合相似性测度,在借鉴了传统的匹配滤波器基础上提出了构造模糊匹配滤波器,并利用模糊相似性测度为准则进行滤波运算,以解决强干扰背景中信号检测的问题.基于定义的相似性测度准则对LPI信号采用模糊匹配滤波,仿真结果表明该方法具有在强干扰背景下检测目标的良好能力,其性能优于传统匹配滤波方法.     

Scale cross-ambiguity and target resolution

We derive the correct formulation of the Doppler problem in which the received signal is scaled in time by a scale factor dependent on the relative radial velocity of the transmitter and the receiver, normalized by the propagation speed in the transmission medium. For sine waves, the conventional Doppler model in which the received signal is translated in frequency is equivalent to the scale model. However, for signals with non zero bandwidth, the difference can be quite significant, as we demonstrate. We further demonstrate simple methods, based on the discrete Fourier transform (DFT) and phase differentiation of the scale CAF, by which delay and scale may be accurately estimated.     

Speech transmission with COFDM based on different discrete transforms

Recently, the multimedia and cellular technologies have spread dramatically. Therefore, the demand for digital information has increased. Speech compression is one of the most effective forms of communication. This paper presents three approaches for the transmission of compressed speech signals over convolutional Coded Orthogonal Frequency Division Multiplexing (COFDM) system with a chaotic interleavering technique. The speech signal has is compressed using the Set Partitioning In Hierarchical trees (SPIHT) algorithm, which is an improved version of EZW and which is characterized by a simple and effective method for further compression. For mitigation of the fading due to multipath wireless channels, this paper proposes a COFDM system based on fractional Fourier transform (FrFT), a COFDM system based on discrete Cosine transform (DCT), and a COFDM system based on discrete wavelet transform (DWT). The FrFT has the ability of solving the frequency offset problem, which causes the received frequency-domain sub-carriers to be shifted, and therefore, the orthogonality between subcarriers deteriorates even with equalization. The DCT has an advantage of increased computational speed as only real calculations are required. The DWT is spectrally efficient since it does not utilize cyclic prefix (CP). These systems have been designed under the assumption that corruptive background noises are absent. Therefore, denoising techniques, namely wavelet denoising and Wiener filtering methods are suggested at the receiver to achieve enhancement in the speech quality. The simulation experiments shows that the proposed COFDM–DWT with Wiener filtering at the receiver has a better trade-off between BER, spectral efficiency and signal distortion. Hence, the BER performance is improved with small bandwidth occupancy. Moreover, due to the denoising stage, the speech quality is improved to achieve good intelligibility.