Frequency estimation of undamped exponential signals using genetic algorithms

In this paper, we consider the problem of frequency estimation of undamped superimposed exponential signals model. We propose two iterative techniques of frequency estimation using genetic algorithms. The proposed methods use an elitism based generational genetic algorithm for obtaining the least squares and the approximate least squares estimates. In the simulation studies, it is observed that the proposed methods give nearly efficient estimates, having mean square error almost attaining the corresponding Cramér-Rao lower bound. The proposed methods significantly do not depend on the initial guess values otherwise required for other iterative methods of frequency estimation. It is also observed that the proposed methods have fairly high breakdown point with respect to different types of outliers present in the data. Outlier robustness and accuracy of the proposed methods are compared with the classical approaches for this problem.     

Robust capacity maximization transceiver design for MIMO OFDM systems多天线正交频分复用系统中鲁棒性容量最大化的收发机设计

In this paper, we investigated capacity maximization problem for Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing systems with imperfect channel state information (CSI). To the best of our knowledge, the considered problem is still an open problem. However, the transceiver designs for MIMO OFDM systems have been extensively studied. It seems nobody gives closed-form solutions for resource allocation for MIMO OFDM systems with statistical channel estimation errors up to date. In our work, based on practical channel estimation algorithm, the channel estimation errors are first derived and then the robust resource allocation problem has been formulated. The structure of the optimal robust precoder is first derived, based on which the optimization problem will be simplified significantly. Furthermore, based on the Lagrangian dual method, a robust power allocation algorithm is proposed. The proposed power allocation can be considered as a variant of water-filling solution named cluster water-filling solution. Finally, simulation results show that our proposed robust design outperforms the non-robust design in terms of channel capacity.     

FMCW微波液位测量的快速频率估计算法

在FMCW(调频连续波)微波液位测量系统中,回波差拍正弦信号的频率检测是实现距离测量的关键.本文比较了基于离散傅里叶变换的正弦信号频率估计方法:矩形窗比值公式法和能量重心校正法,并分析了这两种算法在频率估计中各自存在的问题,通过分析噪声对频率测量的影响并结合两者的优势,提出一种基于FFT的快速频率估计算法,实验结果表明该方法的测量精度高,计算量小,具有实现简单、快速实时等特点.     

Robust state estimator design for uncertain linear systems using optimization techniques

The estimation of state variables of dynamic systems in noisy environments has been an active research field in recent decades. In this way, Kalman filtering approach may not be robust in the presence of modeling uncertainties. So, several methods have been proposed to design robust estimators for the systems with uncertain parameters. In this paper, an optimized filter is proposed for this problem considering an uncertain discrete-time linear system. After converting the subject to an optimization problem, three algorithms are used for optimizing the state estimator parameters: particle swarm optimization (PSO) algorithm, modified genetic algorithm (MGA) and learning automata (LA). Experimental results show that, in comparison with the standard Kalman filter and some related researches, using the proposed optimization methods results in robust performance in the presence of uncertainties. However, MGA-based estimation method shows better performance in the range of uncertain parameter than other optimization methods.     

Correspondence-free determination of the affine fundamental matrix

Fundamental matrix estimation is a central problem in computer vision and forms the basis of tasks such as stereo imaging and structure from motion. Existing algorithms typically analyze the relative geometries of matched feature points identified in both projected views. Automated feature matching is itself a challenging problem. Results typically have a large number of false matches. Traditional fundamental matrix estimation methods are very sensitive to matching errors, which led naturally to the application of robust statistical estimation techniques to the problem. In this work, an entirely novel approach is proposed to the fundamental matrix estimation problem. Instead of analyzing the geometry of matched feature points, the problem is recast in the frequency domain through the use of integral projection, showing how this is a reasonable model for orthographic cameras. The problem now reduces to one of identifying matching lines in the frequency domain which, most importantly, requires no feature matching or correspondence information. Experimental results on both real and synthetic data are presented that demonstrate the algorithm is a practical technique for fundamental matrix estimation. The behavior of the proposed algorithm is additionally characterized with respect to input noise, feature counts, and other parameters of interest     

基于抗差自适应容积卡尔曼滤波的超紧耦合跟踪方法

为降低基于单一调节回路的超紧耦合结构存在的反作用影响,设计了一种基于双回路的超紧耦合结构.基于此,为解决所设计结构中跟踪环路的非线性滤波问题,针对测量异常误差和动力学模型误差,提出了一种基于抗差自适应容积卡尔曼滤波(Robust adaptive cubature Kalman filter,RACKF)的超紧耦合跟踪算法.该算法采用稳健M估计调整容积卡尔曼滤波 (Cubature Kalman filter,CKF)算法,对观测量中粗差的影响“程度”进行探测和处理,以减小观测量异常误差产生的影响,同时利用自适应调节因子对算法进行调节修正,以处理动态扰动误差引入的影响.实验结果表明: 所提出的方法能有效地处理模型不准确所引入的误差,较好地实现全球定位系统(Global positioning system,GPS)卫星信号的高精度和稳定跟踪,其跟踪性能远优于基于单一回路的跟踪方法,同时优于基于无迹卡尔曼滤波(Unscented Kalman filter,UKF)和基于CKF的跟踪方法,提升了导航系统在高动态条件下的适应性能.     

Hybrid dead-beat observers for a class of nonlinear systems

This paper studies both the full order and the reduced order dead-beat observer problem for a class of nonlinear systems, linear in the unmeasured states. A novel hybrid observer design strategy is proposed, with the help of the notion of strong observability in finite time. The proposed methodology is applied for the estimation of the frequency of a sinusoidal signal. The results show that accurate estimation can be provided even if the signal is corrupted by high frequency noise. A brief discussion of the robustness properties of the proposed observer with respect to measurement errors is also provided.     

Optic flow field segmentation and motion estimation using a robustgenetic partitioning algorithm

Optic flow motion analysis represents an important family of visual information processing techniques in computer vision. Segmenting an optic flow field into coherent motion groups and estimating each underlying motion is a very challenging task when the optic flow field is projected from a scene of several independently moving objects. The problem is further complicated if the optic flow data are noisy and partially incorrect. In this paper, the authors present a novel framework for determining such optic flow fields by combining the conventional robust estimation with a modified genetic algorithm. The baseline model used in the development is a linear optic flow motion algorithm due to its computational simplicity. The statistical properties of the generalized linear regression (GLR) model are thoroughly explored and the sensitivity of the motion estimates toward data noise is quantitatively established. Conventional robust estimators are then incorporated into the linear regression model to suppress a small percentage of gross data errors or outliers. However, segmenting an optic flow field consisting of a large portion of incorrect data or multiple motion groups requires a very high robustness that is unattainable by the conventional robust estimators. To solve this problem, the authors propose a genetic partitioning algorithm that elegantly combines the robust estimation with the genetic algorithm by a bridging genetic operator called self-adaptation     

滑动频率估计算法在微波测量液位中的应用

在微波测量液位的系统中,正弦信号的频率估计是实现液位测量的关键.比较并分析了离散FFT变换的两种正弦信号频率估计方法:Rife比值法和三角形法,提出了一种滑动频率估计方法,通过实时确定比例因子,得到了滑动频谱校正算法.实验结果表明,该方法频率估计精度较高,估计性能优于单一的Rife法、三角形法,适合于低信噪比条件下的实...     

分段自相关频率估计算法研究

针对两步自相关频率估计算法（TSA算法）的估计性能和计算量不能兼顾的缺点, 提出了基于正弦信号LP性质的分段自相关频率估计新算法。该算法首先将观测信号二等分, 得到与源信号频率相同、相位不同且噪声互不相关的观测信号, 然后利用正弦信号的LP性质, 结合自相关函数建立参数估计方程, 得到高精度频率估计算法, 最后通过多次蒙特卡洛仿真对算法的有效性进行了验证。仿真结果表明, 分段自相关算法整体优于TSA算法, 有较强的实用性。     

Robust discrete-time set-based adaptive predictive control for nonlinear systems

The problem of robust adaptive predictive control for a class of discrete-time nonlinear systems is considered. First, a parameter estimation technique, based on an uncertainty set estimation, is formulated. This technique is able to provide robust performance for nonlinear systems subject to exogenous variables. Second, an adaptive MPC is developed to use the uncertainty estimation in a framework of min–max robust control. A Lipschitz-based approach, which provides a conservative approximation for the min–max problem, is used to solve the control problem, retaining the computational complexity of nominal MPC formulations and the robustness of the min–max approach. Finally, the set-based estimation algorithm and the robust predictive controller are successfully applied in two case studies. The first one is the control of anonisothermal CSTR governed by the van de Vusse reaction. Concentration and temperature regulation is considered with the simultaneous estimation of the frequency (or pre-exponential) factors of the Arrhenius equation. In the second example, a biomedical model for chemotherapy control is simulated using control actions provided by the proposed algorithm. The methods for estimation and control were tested using different disturbances scenarios.     

一种新的基于线性EIV模型的鲁棒估计算法

提出了一种新的基于线性EIV模型的鲁棒估计算法——鲁棒扩充算法．该算法从结构化数据区域出发,逐渐扩充模型数据集,并不断更新模型参数的估计,直至找到所有模型数据．在每次迭代中,使用C-Step方法对集合进行调整,从而保证了算法的鲁棒性．同时,提出了关于粗差数据和结构化数据分布的结构化密度假设,结合Mean Shift算法,完成对算法的初始位置选取．仿真结果表明,该算法可以有效地处理含有多个结构和大量离群样本的混杂数据,与现有算法相比,具有更强的鲁棒性和更高的精度．     

On perceptual distortion minimization and nonlinear least-squares frequency estimation

In this paper, we present a framework for perceptual error minimization and sinusoidal frequency estimation based on a new perceptual distortion measure, and we state its optimal solution. Using this framework, we relate a number of well-known practical methods for perceptual sinusoidal parameter estimation such as the prefiltering method, the weighted matching pursuit, and the perceptual matching pursuit. In particular, we derive and compare the sinusoidal estimation criteria used in these methods. We show that for the sinusoidal estimation problem, the prefiltering method and the weighted matching pursuit are equivalent to the perceptual matching pursuit under certain conditions.     

应用欠采样技术的多正弦波频率估计方法研究

为了解决高频段信号由于受到A/D转换器和后续信号处理器件运算速度和成本的限制,提出一种用欠采样获得Nyquist采样的信号处理方法。将信号分成两路进行欠采样,根据两路信号构造一个新信号,得到一个虚拟的Nyquist采样值,进行傅里叶变换,单信号时直接得到所估计信号的频率值;多信号情况下在得到各信号频率估计值的同时,会得到因频率“交叉”引起的虚假频率点,通过对双路傅氏变换的结果进行处理可以消除虚假频率点。仿真实验验证了该方法的有效性。     

A genetic algorithm based robust learning credit assignment cerebellar model articulation controller

In this paper, a novel approach of genetic algorithm based robust learning credit assignment cerebellar model articulation controller (GCA-CMAC) is proposed. The cerebellar model articulation controller (CMAC) is a neurological model, which has an attractive property of learning speed. However, the distributions of errors into the addressed hypercubes of CMAC are not proportional to their credibility and may cause unacceptable learning performance. The credit assignment CMAC (CA-CMAC) can solve this problem by using the creditability of hypercubes that the calculated errors are assigned proportional to the inverse of learning times. Afterward, the obtained learning times can be optimized by genetic algorithm (GA) to increase its accuracy. In this paper, the proposed algorithm is to combine credit assignment ideas and GA to provide accurate learning for CMAC. Moreover, we embed the robust learning approach into the GCA-CMAC and dynamically adjust the learning constant for training data with noise or outliers. From simulation results, it shows that the proposed algorithm outperforms other CMACs.     

一种基于块匹配的鲁棒运动估计算法

详细论述了基于块匹配的鲁棒运动估计算法。跟已有的基于块匹配的运动估计算法比较，首先，我们引入颜色信息来提高运动估计的准确性；其次，在更广泛的意义上运用自适应策略来减少计算量并同时保证算法的鲁棒性；最后，提出的基于预测修正的复合查找方法充分利用了物体运动的全局信息，克服了三步查找算法以及全查找算法的缺点并充分发挥它们二者的优点从而提高查找的效率和匹配精度。实验结果表明基于块匹配的鲁棒运动估计算法具有抗干扰能力强、运动估计准确、计算效率高等优点。     

An enhanced mixed modulated Lagrange explicit time delay estimator with noisy input

The mixed modulated Lagrange explicit time delay estimation (MMLETDE) algorithm provides an efficient time delay estimation between narrowband or sinusoidal signals. However, it does not explicitly consider the additive measurement noise at the input, which actually exists in practice. Aiming at this issue, an enhanced MMLETDE algorithm is proposed for noisy inputs based on the unbiased impulse response estimation technique, assuming that the noise power ratio is known a priori. Simulation results show that for narrowband signals or sinusoids over a wide frequency range, the proposed algorithm with a small filter order performs well in moderate and high noise scenarios.     

遗传反馈的多特征图像检索

基于内容的图像检索是随着数字多媒体技术的发展和普及而新兴的一门信息检索技术。针对当前该领域存在的对图像描述不准确、查询精度低以及反馈次数较多的问题,提出一种基于遗传反馈的图像检索算法。该算法以遗传算法和相关反馈为基础,利用多特征进行检索,避免在利用单一特征进行检索时所出现的不同图像具有相同单一特征（颜色、纹理和形状等）的问题,对图像进行多特征描述可以从多个角度对图像进行定义,大大减少了不同图像却具有相同特征的概率。与现有的算法相比,其具有自动调整图像特征权重、较低反馈次数和较高查询精度的特性。实验结果表明,该算法对于旋转、平移和尺度变化具有较强的鲁棒性,同时具有减少反馈次数和较高查询精度的性能。     

Design of Asymptotically Convergent Frequency Estimator Using Contraction Theory

The problem of estimating unknown frequencies of a sinusoidal signal simultaneously is a classical problem in signal and system theory. Many approaches and algorithms are proposed in literature to develop estimators for a measurable sinusoidal signal having multiple sinusoids with unknown amplitudes, frequencies and phases. In this work, an asymptotically convergent frequency estimator is given for estimation of n-unknown frequencies of a measurable sinusoidal signal. The contraction theory approach is adopted to show the asymptotic convergence of the proposed estimator in quite a simplified manner. Approach given here exploits the results of contraction theory related to semi-contracting systems. The nonlinear estimator based on dynamic system approach, guarantees global boundedness and convergence of the state and frequency estimation for all initial conditions and frequency values. It further ensures simultaneous globally convergent estimation of states and frequencies of a sinusoid involving multiple frequencies. Numerical simulations are presented for different combinations of frequencies to justify the claim.     

基于关键点的类别级三维可形变目标姿态估计

为了解决类别级三维可形变目标姿态估计问题,基于目标的关键点,提出了一种面向类别的三维可形变目标姿态估计方法。该方法设计了一种基于关键点的端到端深度学习框架,框架以PointNet++为后端网络,通过特征提取、部位分割、关键点提取和基于关键点的姿态估计部分实现可形变目标的姿态估计,具有计算精度高、鲁棒性强等优势。同时,基于ANCSH方法设计了适用于K-AOPE网络的关键点标准化分层表示方法,该方法仅需提取目标少量的关键点即可表示类别物体。为了验证方法的有效性,在公共数据集shape2motion上进行测试。实验结果显示,提出的姿态估计方法(以眼镜类别为例)在旋转角上的误差分别为2.3°、3.1°、3.7°,平移误差分别为0.034、0.030、0.046,连接状态误差为2.4°、2.5°,连接参数误差为1.2°、0.9°,0.008、0.010。与ANCSH方法相比,所提方法具有较高的准确性和鲁棒性。