Acoustic sound speed profile inversion based on orthogonal matching pursuit

The estimation of ocean sound speed profiles(SSPs)requires the inversion of an acoustic field using limited observations.Such inverse problems are underdetermined,and require regularization to ensure physically realistic solutions.The empirical orthonormal function(EOF)is capable of a very large compression of the data set.In this paper,the non-linear response of the sound pressure to SSP is linearized using a first order Taylor expansion,and the pressure is expanded in a sparse domain using EOFs.Since the parameters of the inverse model are sparse,compressive sensing(CS)can help solve such underdetermined problems accurately,efficiently,and with enhanced resolution.Here,the orthogonal matching pursuit(OMP)is used to estimate range-independent acoustic SSPs using the simulated acoustic field.The superior resolution of OMP is demonstrated with the SSP data from the South China Sea experiment.By shortening the duration of the training set,the temporal correlation between EOF and test sets is enhanced,and the accuracy of sound velocity inversion is improved.The SSP estimation error versus depth is calculated,and the 99%confidence interval of error is within±0.6 m/s.The 82%of mean absolute error(MAE)is less than 1 m/s.It is shown that SSPs can be well estimated using OMP.

Based on Compressed Sensing of Orthogonal Matching Pursuit Algorithm Image Recovery

Compressive sensing theory mainly includes the sparsely of signal processing,the structure of the measurement matrix and reconstruction algorithm.Reconstruction algorithm is the core content of CS theory,that is,through the low dimensional sparse signal recovers the original signal accurately.This thesis based on the theory of CS to study further on seismic data reconstruction algorithm.We select orthogonal matching pursuit algorithm as a base reconstruction algorithm.Then do the specific research for the implementation principle,the structure of the algorithm of AOMP and make the signal simulation at the same time.In view of the OMP algorithm reconstruction speed is slow and the problems need to be a given number of iterations,which developed an improved scheme.We combine the optimized OMP algorithm of constraint the optimal matching of item selection strategy,the backwards gradient projection ideas of adaptive variance step gradient projection method and the original algorithm to improve it.Simulation experiments show that improved OMP algorithm is superior to traditional OMP algorithm of improvement in the reconstruction time and effect under the same condition.This paper introduces CS and most mature compressive sensing algorithm at present orthogonal matching pursuit algorithm.Through the program design realize basic orthogonal matching pursuit algorithms,and design realize basic orthogonal matching pursuit algorithm of one-dimensional,two-dimensional signal processing simulation.

Coherence Based Sufficient Condition for Support Recovery Using Generalized Orthogonal Matching Pursuit

In an underdetermined system,compressive sensing can be used to recover the support vector.Greedy algorithms will recover the support vector indices in an iterative manner.Generalized Orthogonal Matching Pursuit(GOMP)is the generalized form of the Orthogonal Matching Pursuit(OMP)algorithm where a number of indices selected per iteration will be greater than or equal to 1.To recover the support vector of unknown signal‘x’from the compressed measurements,the restricted isometric property should be satisfied as a sufficient condition.Finding the restricted isometric constant is a non-deterministic polynomial-time hardness problem due to that the coherence of the sensing matrix can be used to derive the sufficient condition for support recovery.In this paper a sufficient condition based on the coherence parameter to recover the support vector indices of an unknown sparse signal‘x’using GOMP has been derived.The derived sufficient condition will recover support vectors of P-sparse signal within‘P’iterations.The recovery guarantee for GOMP is less restrictive,and applies to OMP when the number of selection elements equals one.Simulation shows the superior performance of the GOMP algorithm compared with other greedy algorithms.

Coherence-based performance analysis of the generalized orthogonal matching pursuit algorithm

The performance guarantees of generalized orthogonal matching pursuit（ gOMP） are considered in the framework of mutual coherence. The gOMP algorithmis an extension of the well-known OMP greed algorithmfor compressed sensing. It identifies multiple N indices per iteration to reconstruct sparse signals.The gOMP with N≥2 can perfectly reconstruct any K-sparse signals frommeasurement y = Φx if K 〈1/N（1/μ-1） ＋1,where μ is coherence parameter of measurement matrix Φ. Furthermore,the performance of the gOMP in the case of y = Φx ＋ e with bounded noise ‖e‖2≤ε is analyzed and the sufficient condition ensuring identification of correct indices of sparse signals via the gOMP is derived,i. e.,K 〈1/N（1/μ-1）＋1-（2ε/Nμxmin） ,where x min denotes the minimummagnitude of the nonzero elements of x. Similarly,the sufficient condition in the case of G aussian noise is also given.

Proportional integral derivative controller design using Legendre orthogonal functions

The Legendre orthogonal functions are employed to design the family of PID controllers for a variety of plants. In the proposed method, the PID controller and the plant model are represented with their corresponding Legendre series. Matching the first three terms of the Legendre series of the loop gain with the desired one gives the PID controller parameters. The closed loop system stability conditions in terms of the Legendre basis function pole(λ) for a wide range of systems including the first order, second order, double integrator, first order plus dead time, and first order unstable plants are obtained. For first order and double integrator plants, the closed loop system stability is preserved for all values of λ and for the other plants, an appropriate range in terms of λ is obtained. The optimum value of λ to attain a minimum integral square error performance index in the presence of the control signal constraints is achieved. The numerical simulations demonstrate the benefits of the Legendre based PID controller.

IDENTIFICATION OF 3-D OBJECTS FROM THREE ORTHOGONAL SILHOUETTES USING NORMALIZED LINEAR QUAD-OCTREES

Linear octrees offer a volume representation of 3-D objects, which is quite compactand lends itself to traditional object processing operations. However, the linear octree structurefor generating the representation of 3-D objects from three orthogonal silhouettes by using thevolume intersection technique is dependent on viewpoints. The recognition achieved from match-ing object representations to model representations requires that the representations of objectsare independent of viewpoints. In order to obtain independent representations of viewpoints,the three principal axes of the object should be obtained from the moment of inertia matrix bycomputing its eigenvectors. The linear octree is projected onto the image planes of the three prin-cipal views (along the principal axes) to obtain the three normalized linear quadtrees. The objectmatching procedure has two phases: the first phase is to match the normalized linear quadtrees ofthe unknown object to a subset of models contained in a library utilizing a measure of symmetricdifference; the second phase is to generate the normalized linear octrees of the object and theseselected models and then to match the normalized linear octree of the unknown object with themodel having the minimum symmetric difference.

Robustness of orthogonal matching pursuit under restricted isometry property

Orthogonal matching pursuit （OMP） algorithm is an efficient method for the recovery of a sparse signal in compressed sensing, due to its ease implementation and low complexity. In this paper, the robustness of the OMP algorithm under the restricted isometry property （RIP） is presented. It is shown that 5K＋V/KOK,1 〈 1 is sufficient for the OMP algorithm to recover exactly the support of arbitrary /（-sparse signal if its nonzero components are large enough for both 12 bounded and lz~ bounded noises.

Analysis of orthogonal multi-matching pursuit under restricted isometry property

Orthogonal multi-matching pursuit（OMMP）is a natural extension of orthogonal matching pursuit（OMP）in the sense that N（N≥1）indices are selected per iteration instead of 1.In this paper,the theoretical performance of OMMP under the restricted isometry property（RIP）is presented.We demonstrate that OMMP can exactly recover any K-sparse signal from fewer observations y=φx,provided that the sampling matrixφsatisfiesδKN-N＋1＋√K/NθKN-N＋1,N〈1.Moreover,the performance of OMMP for support recovery from noisy observations is also discussed.It is shown that,for l_2 bounded and l_∞bounded noisy cases,OMMP can recover the true support of any K-sparse signal under conditions on the restricted isometry property of the sampling matrixφand the minimum magnitude of the nonzero components of the signal.

A new result on recovery sparse signals using orthogonal matching pursuit

Orthogonal matching pursuit(OMP)algorithm is a classical greedy algorithm widely used in compressed sensing.In this paper,by exploiting the Wielandt inequality and some properties of orthogonal projection matrix,we obtained a new number of iterations required for the OMP algorithm to perform exact recovery of sparse signals,which improves significantly upon the latest results as we know.

New code match strategy for wideband code division multiple access code tree management

Orthogonal variable spreading factor channelization codes are widely used to provide variable data rates for supporting different bandwidth requirements in wideband code division multiple access (WCDMA) systems. A new code match scheme for WCDMA code tree management was proposed. The code match scheme is similar to the existing crowed-first scheme. When choosing a code for a user, the code match scheme only compares the one up layer of the allocated codes, unlike the crowed-first scheme which perhaps compares all up layers. So the operation of code match scheme is simple, and the average time delay is decreased by 5.1%. The simulation results also show that the code match strategy can decrease the average code blocking probability by 8.4%.

THE EXACT RECOVERY OF SPARSE SIGNALS VIA ORTHOGONAL MATCHING PURSUIT

This paper aims to investigate sufficient conditions for the recovery of sparse signals via the orthogonal matching pursuit （OMP） algorithm. In the noiseless case, we present a novel sufficient condition for the exact recovery of all k-sparse signals by the OMP algorithm, and demonstrate that this condition is sharp. In the noisy case, a sufficient condition for recovering the support of k-sparse signal is also presented. Generally, the computation for the restricted isometry constant （RIC） in these sufficient conditions is typically difficult, therefore we provide a new condition which is not only computable but also sufficient for the exact recovery of all k-sparse signals.

The Recovery Guarantee for Orthogonal Matching Pursuit Method to Reconstruct Sparse Polynomials

Orthogonal matching pursuit(OMP for short)algorithm is a popular method of sparse signal recovery in compressed sensing.This paper applies OMP to the sparse polynomial reconstruction problem.Distinguishing from classical research methods using mutual coherence or restricted isometry property of the measurement matrix,the recovery guarantee and the success probability of OMP are obtained directly by the greedy selection ratio and the probability theory.The results show that the failure probability of OMP given in this paper is exponential small with respect to the number of sampling points.In addition,the recovery guarantee of OMP obtained through classical methods is lager than that of ℓ_(1)-minimization whatever the sparsity of sparse polynomials is,while the recovery guarantee given in this paper is roughly the same as that of ℓ_(1)-minimization when the sparsity is less than 93.Finally,the numerical experiments verify the availability of the theoretical results.

胶泥缓冲器与高射机枪动力学性能优化匹配

为了改善机枪的射击稳定性从而提高射击精度,设计一款应用于12.7 mm重型机枪的单出杆式黏弹性胶泥缓冲器,用变刚度Kelvin模型对其进行数学建模.用正交试验法对黏弹性胶泥缓冲器的刚度、阻尼系数和预压力进行优化匹配分析,获得胶泥缓冲器与高射机枪动力学匹配最好的一组性能优化参数.优化匹配的黏弹性胶泥缓冲器与弹簧缓冲器动力学性能仿真比较结果表明,使用胶泥缓冲器的12.7 mm重型机枪后坐力更小,后坐速度变化更平稳,枪口波动更小.

基于改进广义正交匹配追踪的低地球轨道卫星MIMO-OTFS系统的信道估计方法

针对基于多输入多输出(MIMO)技术和正交时频空间(OTFS)调制的低地球轨道卫星系统的复杂性带来的信道估计困难问题,提出一种基于改进广义正交匹配追踪(GOMP)的信道估计方法。根据单输入单输出(SISO)-OTFS系统的输入输出关系和低地球轨道卫星信道的传播特性,建立一种基于MIMO-OTFS的低地球轨道卫星信道模型,并将系统的信道估计问题转化为稀疏信号的恢复问题。考虑到传统的GOMP算法存在对稀疏度的过度依赖和对稀疏信号的重构精度差等问题,所提方法结合了分段弱正交匹配追踪(SWOMP)的弱选择思想和广义Jaccard系数的相似性准则,以快速准确地重建稀疏信号。仿真结果表明,当天线数为16且导频开销比为0.5时,与正交匹配追踪(OMP)算法相比,所提方法的归一化均方误差(NMSE)降低了约2.5 dB,误码率(BER)降低了约5 dB。

基于压缩感知的缺失机械振动信号重构新方法

针对工业机械设备实时监测中不可控因素导致的振动信号数据缺失问题,提出一种基于自适应二次临近项交替方向乘子算法(adaptive quadratic proximity-alternating direction method of multipliers, AQ-ADMM)的压缩感知缺失信号重构方法。AQ-ADMM算法在经典交替方向乘子算法算法迭代过程中添加二次临近项,且能够自适应选取惩罚参数。首先在数据中心建立信号参考数据库用于构造初始字典,然后将K-奇异值分解(K-singular value decomposition, K-SVD)字典学习算法和AQ-ADMM算法结合重构缺失信号。对仿真信号和两种真实轴承信号数据集添加高斯白噪声后作为样本,试验结果表明当信号压缩率在50%~70%时,所提方法性能指标明显优于其它传统方法,在重构信号的同时实现了对含缺失数据机械振动信号的快速精确修复。

权重化QR分解的正交匹配追踪算法硬件实现

为在小型化、低成本的硬件平台实现正交匹配追踪(Orthogonal Matching Pursuit,OMP)算法,针对OMP算法中最小二乘法的问题,该文构造一个确定性的传感矩阵,提出一种低复杂度、低资源的权重化QR分解的OMP(Weighted QR decomposition OMP,WQR-OMP)算法硬件结构,在ZYNQ 7020型号芯片上搭建WQR-OMP SOC系统.WQR-OMP算法在传感矩阵进行QR分解后,根据三角矩阵R中元素的分布特性,通过权重化运算只保留主对角线上的元素而其他余元素归零,得到对角矩阵D,然后近似计算稀疏向量的解.实验结果表明:与基于QR分解的OMP(QR decomposition OMP,QR-OMP)和Batch-OMP算法的硬件结构相比,WQR-OMP算法硬件结构的重构速度更快、存储资源更少.在压缩率为0.25的条件下,WQR-OMP SOC系统对256×256分辨率图像的重构时间为400 ms左右,其速率比仅使用ARM处理器的重构速率提高了约6.3倍.与其他现有研究对比,该系统在Block RAM存储资源消耗较少的情况下,进一步提升了重构速度,适用于存储资源受限的硬件平台.

基于正交匹配追踪的深度波数谱分解及其在含油气储层预测中的应用

常规时间域地震属性分析是利用由叠前深度偏移数据转换至时间域的数据,这种转换将造成有效高频信息的损失,为了充分利用深度域资料成像精度高的优势,有必要开展深度域资料属性分析工作。由于深度域波数与频率和波速有关,如何获得高分辨率深度波数谱是深度域地震属性分析的关键。采用基于稀疏反演的谱分解方法,通过建立深度域过完备子波库,利用正交匹配追踪算法提高深度波数谱计算分辨率,通过理论模型的深度波数谱属性计算,并与时频谱属性进行对比,分析含油气储层的深度波数谱变化特征,通过实际资料的深度波数谱属性分析应用,验证利用深度波数谱进行含油气性预测的实用性。结果表明,基于正交匹配追踪算法的深度波数谱分解方法具有较高分辨率,可作为深度域含油气储层预测的高精度手段之一。实际数据应用表明,深度波数谱含油气储层下方出现低波数伴影现象,可作为深度域指示油气储层存在的标志,基于正交匹配追踪的深度波数谱分解方法可有效识别低波数伴影异常,对深度域地震资料进行含油气储层预测。

微型电动汽车正面碰撞约束系统匹配研究

目前对微型电动汽车相关研究逐渐增多,但碰撞安全性能方面的研究相对较少。基于国内某款微型电动汽车在进行49 km/h正面碰撞后车内Hybrid Ⅲ 50th男性假人伤害程度较高的问题,对该车约束系统的参数匹配进行优化处理。参考相关规范对驾乘人员的保护要求,以仿真分析结果中的头部伤害指标HIC、头部累积3 ms合成加速度、加权综合损伤数值WIC为研究指标,进行极差分析来综合选取参数及水平值;通过正交试验进行优化设计仿真,最终得出最佳约束系统的最佳参数匹配方式,使得微型电动汽车在发生碰撞事故时驾驶员头部位置的伤害得到降低,同时维持或降低胸部和下肢伤害水平,综合损伤也得到降低。结果表明优化后的该约束系统可达到对驾驶员最优的保护效果。

最优字典选择多频段雷达信号宽带融合

多频段雷达带宽融合外推是一种提升雷达带宽、解决小目标高分辨成像的有效手段。然而,现有多频段融合算法仍面临运算慢、精度低等问题。为此,该文提出基于最优字典选择正交匹配追踪的多频段融合外推雷达超分辨距离成像方法。首先,对多频段信号进行参数化建模,提出基于蛇优化的信号相参配准方法,实现多频段信号高精度相位对齐;然后,利用几何绕射模型,提出基于最优字典选择正交匹配追踪的多频段信号模型估计方法,实现多频段信号融合外推,估计未知频段频谱,获取大带宽信号;最后,通过仿真和实测数据,验证了该方法的可行性。该方法在保障高精度的前提下,通过简化模型粗估计与完整模型精估计结合,有效降低了运算量,实现了快速精确多频段融合外推处理。

正交匹配追踪优化稀疏频散Radon变换多模式导波分离方法

原始频散Radon变换(DRT)方法中的有限孔径效应以及算子非正交性影响了DRT导波信号参数提取的精度,因此提出了一种稀疏频散Radon变换方法以实现更精确的导波混叠模式分离和参数估计。该方法基于导波信号的参数域稀疏性,引入正交匹配追踪算法构造代价函数,经多次迭代在频散Radon域中获得稀疏能量谱,可精确分离阵列导波信号中的混叠模式并去噪。对不同信噪比下各模式仿真信号,稀疏DRT方法的信号重建误差较原始频散Radon变换方法下降51.1%。仿真和实验验证了所提算法在导波模式参数提取和混合模式分离方面的可靠性。