A Novel Generalized Simulation Technology of Aerospace TT＆C Channel

A novel generalized simulation method is proposed to simulate the dynamic transmission delay of wideband and arbitrary signal in aerospace Tracking, telemetry and command （TT＆C） channel. This method orthogonally demodulates the wideband and arbitrary Radio-frequency （RF） signal into complex baseband by a Local oscillator （LO） signal. Then the method of dynamic interpolation and delay reconstruction is proposed to ob- tain the delay reconstruction signal of complex baseband signal based on the variation rules of satellite-to-earth loca- tion. Meanwhile, the method of satellite-to-earth distance subsection and polynomial fitting is applied to obtain the delay reconstruction signal of LO signal. The simulated output signal is achieved through the synthesis of two de- lay reconstruction signals mentioned above. The proposed method can accurately simulate the variation characteris- tics of time delay and Doppler when wideband and arbi- trary RF signal transmits in channel, without knowing any priori knowledge, such as signal form, signal parameters, and so on.     

Novel phase difference extraction method of FPP system based on DWT and OMP algorithm

In this paper, to reduce the non-linear phase error caused by gamma effect of the digital projector, a novel phase difference extraction method is proposed. First, through multi-layer discrete wavelet decomposition(DWT), the components of the original phase difference at different frequencies are extracted. Subsequently, the effective components are taken by the orthogonal matching pursuit(OMP) algorithm, and the phase difference is reconstructed. According to the upper and lower envelopes of the original phase difference extracted by cubic spline interpolation, the residual threshold of OMP is set. According to the results of three-dimensional(3-D) reconstruction experiment on a standard sphere, the proposed method is capable of reducing the errors in the original phase difference and achieving a better imaging result.     

A fast search method of steered response power with small-aperture microphone array for sound source localization

The Steered Response Power （SRP） method works well for sound source localization in noisy and reverberant environment. However, the large computation complexity limits its practical application. In this paper, a fast SRP search method is proposed to reduce the computational complexity using small-aperture microphone array. The proposed method inspired by the SRP spatial spectrum includes two steps： first, the proposed method estimates the azimuth of the sound source roughly and determines whether the sound source is in far field or near field; then, different fine searching operations are performed according to the sound source being in far field or near field. Ex- periments both in simulation environments and real environments have been performed to compare the localization accuracy and computation complexity of the proposed method with those of the conven- tional SRP-PHAT algorithm. The results show that, the proposed method has a comparative accuracy with the conventional SRP algorithm, and achieves a reduction of 93.62% in computation complexity compared to the conventional SRP algorithm.     

EFFICIENT STRIP-MODE SAR RAW SIGNAL SIMULATION OF MIXED TARGETS BASED ON ACCURATE 2-D SPECTRUM简

An accurate and efficient Synthetic Aperture Radar （SAR） raw data generator is of con- siderable value for testing system parameters and verifying imaging algorithms. Nevertheless, the ex- isting simulator cannot exactly handle the case of the fast moving targets in high squint geometry. As for the issue, the analytical expression for the two Dimensional （2-D） signal spectrum of moving targets is derived and a fast raw echo simulation method is proposed in this study. The proposed simulator can accommodate the moving targets in tile high squint geometry, whose processing steps of the simulation are given in detail and its computational complexity is analyzed. The simulation data for static and moving targets are processed and analyzed, and the results are given to validate the effectiveness of the proposed approach.     

SELECTION OF PROPER EMBEDDING DIMENSION IN PHASE SPACE RECONSTRUCTION OF SPEECH SIGNALS

In phase space reconstruction of time series, the selection of embedding dimension is important. Based on the idea of checking the behavior of near neighbors in the reconstruction dimension, a new method to determine proper minimum embedding dimension is constructed. This method has a sound theoretical basis and can lead to good result. It can indicate the noise level in the data to be reconstructed, and estimate the reconstruction quality. It is applied to speech signal reconstruction and the generic embedding dimension of speech signals is deduced.     

LLE-based classification algorithm for MMW radar target recognition

In this paper, a new multiclass classification algorithm is proposed based on the idea of Locally Linear Embedding (LLE), to avoid the defect of traditional manifold learning algorithms, which can not deal with new sample points. The algorithm defines an error as a criterion by computing a sample’s reconstruc-tion weight using LLE. Furthermore, the existence and characteristics of low dimensional manifold in range-profile time-frequency information are explored using manifold learning algorithm, aiming at the problem of target recognition about high range resolution MilliMeter-Wave (MMW) radar. The new algo-rithm is applied to radar target recognition. The experiment results show the algorithm is efficient. Com-pared with other classification algorithms, our method improves the recognition precision and the result is not sensitive to input parameters.     

A WEIGHTED ITERATIVE METHOD FOR ROBUST SELF-CALIBRATION

A robust self-calibration method is presented, which can efficiently discard the outliers based on a Weighted Iteration Method （WIM）. The method is an iterative process in which the projective reconstruction is obtained based on the weights of all the points, whereas the weights are defined in inverse proportion to the reciprocal of the re-projective errors. The weights of outliers trend to zero after several iterations, and the accurate projective reconstruction is determined. The location of the absolute conic and the camera intrinsic parameters are obtained after the projective reconstruction. The theory and experiments with both simulate and real data demonstrate that the proposed method is very efficient and robust.     

NON-STATIONARY SIGNAL DENOISING USING TIME-FREQUENCY CURVE SURFACE FITTING

Based on the theory of adaptive time-frequency decomposition and Time-Frequency Dis- tribution Series (TFDS), this paper presents a novel denoising method for non-stationary signal. Ac- cording to the input signal features, an appropriate kind of elementary functions with great concen- tration in the Time-Frequency (TF) plane is selected. Then the input signal is decomposed into a linear combination of these functions. The elementary function parameters are determined by using ele- mentary function TF curve surface to fit the input signal’s TFDS. The process of curved surface fitting corresponds to the signal structure matching process. The input signal’s dominating component whose structure has the resemblance with elementary function is fitted out firstly. Repeating the fitting process, the residue can be regarded as noises, which are greatly different from the function. Selecting the functions fitted out initially for reconstruction, the denoised signal is obtained. The performance of the proposed method is assessed by means of several tests on an emulated signal and a gearbox vi- brating signal.     

IMAGING ANALYSIS OF FREQUENCY MODULATION CONTINUOUS WAVE SYNTHETIC APERTURE RADAR IN NEAR RANGE MODE简

Frequency-Modulation Continuous-Wave Synthetic Aperture Radar （FMCW SAR） has shown great potential in the applications of civil and military fields because of its easy deployment and low cost. However, most of these work and analysis are concentrated on airborne FMCW SAR, where the characteristics of the ilnaging geometry and signal are much similar to that of traditional pulsed-SAR. As a result, a series of test campaigns of automobile-based FMCW SAR were sponsored by Institute of Electronics, Chinese Academy of Sciences （IECAS） in the autumn of 2012. In this paper, we analyze the imaging issues of FMCW SAR in automobile mode （named as near range mode）, where a vehicle is used as moving platform and a large looking angle is configured. The imaging geometry and signal properties are analyzed in detail. We emphasize the difference of the near range mode from the traditional airborne SAR mode. Based on the analysis, a focusing approach is proposed in the paper to handle the data focusing in the case. Simulation experiment and real data of automobile FMCW SAR are used to validate the analysis.     

NEW APPROACH TO EMULATE SEU FAULTS ON SRAM BASED FPGAS简

Field Programmable Gate Arrays （FPGAs） offer high capability in implementing of com- plex systems, and currently are an attractive solution for space system electronics. However, FPGAs are susceptible to radiation induced Single-Event Upsets （SEUs）. To insure reliable operation of FPGA based systems in a harsh radiation environment, various SEU mitigation techniques have been provided In this paper we propose a system based on dynamic partial reconfiguration capability of the modern devices to evaluate the SEU fault effect in FPGA. The proposed approach combines the fault injection controller with the host FPGA, and therefore the hardware complexity is minimized. All of the SEU injection and evaluation requirements are performed by a soft-core which realized inside the host FPGA Experimental results on some standard benchmark circuits reveal that the proposed system is able to speed up the fault injection campaign 50 times in compared to conventional method.     

基于压缩感知OMP改进算法的图像重构

正交匹配追踪(OMP)算法中迭代次数严格依赖信号的稀疏度K值,迭代次数选取适当会重构出高精确的图像,反之则会对图像重构质量造成严重影响.针对这一问题,提出了一种根据残差值的相对极差来确定最佳迭代次数的新方法.该方法要求在同一次迭代中对一幅图像的所有列同时进行迭代计算,根据极差的相对差值与门限值比较来确定最佳迭代次数,从而达到提高重构精度,消除对稀疏度K值依赖的目的.理论分析和仿真结果表明,改进的OMP算法比原有算法有更理想的重构效果,有更高的重构精度.     

基于线性预测分析和差分变换的语音信号压缩感知

在压缩感知研究中,信号在不同变换下的稀疏域好坏是影响信号重构性能的重要因素。该文基于语音信号的线性预测分析(LPC),提出一种结合了LPC分析和差分变换的语音稀疏化联合变换方法,通过正交匹配追踪算法(OMP)优化算法重构语音信号,与FFT和LPC两种稀疏化方法进行了对比分析。实验表明,在压缩比大于0.4时,联合变换法重构的语音信号性能明显优于另外两种方法。也即在相同重构性能并兼顾语音质量的情况下,联合变换法具有较小的压缩比,因而具有较好的压缩性能。采用PESQ语音质量评测方法对3种稀疏化算法重构的语音进行平均意见值(MOS)对比,联合变换法也具有较好的性能。     

基于压缩感知的超宽带信道估计方法的研究

压缩感知(Compressed Sensing, CS)理论可以从较少的观测样本中恢复稀疏信号。针对超宽带(Ultra- WideBand, UWB)信道的稀疏特性,将压缩感知理论应用于UWB系统的信道估计中,能够有效地降低系统的采样速率。该文针对UWB信道的特点对过完备字典库和观测矩阵进行设计,提出了一种滤波矩阵估计算法。然后,分别利用丹茨格选择器(Dantzig Selector, DS),基追踪降噪(Basis Pursuit De-Noising, BPDN)算法和正交匹配跟踪(Orthogonal Matching Pursuit, OMP)算法实现信号检测,进一步给出UWB信道估计中CS重建算法的选择建议。基于IEEE 802.15.4a信道模型的仿真结果表明,该算法同随机观测算法的检测结果相比,能够在较低的采样速率下获得更好的误码率性能。     

用概率推导和加权迭代L1范数实现信号重构

在对信号稀疏性统计分析的基础上,将具有稀疏描述能力的拉普拉斯分布用于描述信号的先验分布,基于贝叶斯法,利用信号采样值、拉普拉斯先验分布和高斯似然模型,推导信号的后验概率密度估计;最后将最大后验概率(MAP)估计过程转化为加权迭代L1范数的最小化问题。在求解过程中,与非加权的L1范数法进行对比表明,信号重构性能明显提高;通过实验计算,详细讨论了其中一些参数的取值原则和范围;针对稀疏度不同的信号,随着信号非零点数的增加,本文算法重构结果明显优于基追踪(BP)和(OMP)法;与同类的IRL1算法相比较,本文算法更具普遍性和理论意义。     

基于OMP-SVD的多分量单频信号频率估计

压缩感知(CS)是稀疏信号处理的有力工具。研究了多分量单频信号经过压缩采样以及信号重构后的频率估计问题。首先以一个随机高斯矩阵对信号进行观测测量,得到观测值;然后基于正交匹配追踪(OMP)和奇异值分解(SVD)算法,利用这些观测值对原信号进行高精度重构;最后利用重构中得到的非零元素的位置信息估计了原信号频率,并与基于Levinson-Durbin的AR参数模型频率估计算法进行了性能对比。仿真证明了算法能够高效重构原信号,并精确估计原信号频率。     

基于块A*正交匹配追踪的多传感器数据联合重构算法

 针对A*正交匹配追踪(A*OMP)算法计算复杂高,且不能利用信号的结构稀疏性这一缺陷,该文提出了块A*OMP算法并将其用于解决分布式压缩感知中的信号联合重构问题。该算法用原子块取代单个原子作为搜索树中的节点,在计算路径代价时用搜索树中所有路径的最大长度取代信号的稀疏度。然后在块A*OMP算法的基础上,选择与残差矩阵投影误差最小的原子块作为新的节点,得到了一种用于解决MMV(Multiple Measurement Vector, MMV)问题的块A*OMP算法,并利用该算法对相邻区域内的多个传感器所测的温度信号进行了联合重构。实验结果表明,该算法的重构性能优于MMV正交匹配追踪(OMPMMV)算法。     

High‐throughput and low‐area implementation of orthogonal matching pursuit algorithm for compressive sensing reconstruction

Massive computation of the reconstruction algorithm for compressive sensing (CS) has been a major concern for its real‐time application. In this paper, we propose a novel high‐speed architecture for the orthogonal matching pursuit (OMP) algorithm, which is the most frequently used to reconstruct compressively sensed signals. The proposed design offers a very high throughput and includes an innovative pipeline architecture and scheduling algorithm. Least‐squares problem solving, which requires a huge amount of computations in the OMP, is implemented by using systolic arrays with four new processing elements. In addition, a distributed‐arithmetic‐based circuit for matrix multiplication is proposed to counterbalance the area overhead caused by the multi‐stage pipelining. The results of logic synthesis show that the proposed design reconstructs signals nearly 19 times faster while occupying an only 1.06 times larger area than the existing designs for N = 256, M = 64, and m = 16, where N is the number of the original samples, M is the length of the measurement vector, and m is the sparsity level of the signal.     

一种基于改进ROMP的MIMO-OFDM信道估计方法

本文根据信道响应的时域稀疏性,引入压缩感知理论,针对正则化正交匹配追踪（ROMP）需已知稀疏度和原子一旦选入无法删除两大缺点,提出一种基于改进ROMP的信道估计方法.该方法结合压缩采样匹配追踪（CoSaMP）、稀疏度自适应匹配追踪（SAMP）和变步长的优点,实现稀疏信号快速准确的重建.仿真结果表明,与基于OMP、ROMP、CoSaMP、SAMP的信道估计方法相比,所提方法有效提高了MIMO-OFDM系统的归一化均方误差（NMSE）和误码率（BER）性能.     

基于贝叶斯检验模型的压缩感知算法及应用

针对正交匹配追踪(OMP)算法需设置冗余的支撑集,导致信号重构时运算量变大、抗噪性能和重构性能变差等问题,提出了一种基于贝叶斯模型的OMP(BOMP,bayesian orthogonal matching pursuit)算法。首先利用贝叶斯检验模型和OMP算法合理去除支撑集中的冗余部分,得到相等或略大于信号真实稀疏度的支撑集;其次构建BOMP的信号重构算法;最后将算法应用于ISAR成像。仿真和实测数据结果表明,由于本文算法可近似估计到信号的真实稀疏度,因此具有更好的抗噪性能以及重构精度,相应的运算量也明显减少。