混沌信号自适应协同滤波去噪

混沌信号协同滤波去噪算法充分利用了混沌信号的自相似结构特征,具有良好的信噪比提升性能.针对该算法的滤波参数优化问题,考虑到最优滤波参数的选取受到信号特征、采样频率和噪声水平的影响,为提高该算法的自适应性使其更符合实际应用需求,基于排列熵提出一种滤波参数自动优化准则.依据不同噪声水平的混沌信号排列熵的不同,首先选取不同滤波参数对含噪混沌信号进行去噪,然后计算各滤波参数对应重构信号的排列熵,最后通过比较各重构信号的排列熵,选取排列熵最小的重构信号对应的滤波参数为最优滤波参数,实现滤波参数的优化.分析了不同信号特征、采样频率和噪声水平情况下滤波参数的选取规律.仿真结果表明,该参数优化准则能在不同条件下对滤波参数进行有效的自动最优化,提高了混沌信号协同滤波去噪算法的自适应性.     

一种参数优化的混沌信号自适应去噪算法

针对非线性自适应混沌信号去噪算法的参数优化问题, 考虑到最优滤波窗长受到不同因素的影响, 为提高该算法的自适应性, 提出一种滤波窗长自动最优化的判决准则. 依据混沌信号和噪声自相关函数的不同, 首先采用不同窗长对含噪混沌信号进行去噪, 然后计算每个窗长对应的残差自相关度(RAD), 最后通过对最小RAD所对应的窗长进行一定比例收缩实现窗长的最优化. 仿真结果表明, 该判决准则能够在不同条件下对滤波窗长进行有效的自动最优化, 提高了混沌信号去噪算法的自适应性.     

An Adaptive Denoising Algorithm for Noisy Chaotic Signals Based on Local Sparse Representation

An adaptive denoising algorithm based on local sparse representation （local SR） is proposed. The basic idea is applying SR locally to clusters of signals embedded in a high-dimensional space of delayed coordinates. The clusters of signals are represented by the sparse linear combinations of atoms depending on the nature of the signal. The algorithm is applied to noisy chaotic signals denoising for testing its performance. In comparison with recently reported leading alternative denoising algorithms such as kernel principle component analysis （Kernel PCA）, local independent component analysis （local ICA）, local PCA, and wavelet shrinkage （WS）, the proposed algorithm is more efficient.     

一种混沌映射的相空间去噪方法

对于混沌映射来说,它们的频谱比混沌流的频谱更广阔,与噪声频谱的重叠率更高,所以混沌流的去噪方法对它们并不适用. 在半盲分析法的框架下,混沌系统的参数估计问题终将归结为最小二乘估计问题. 本文从最小二乘拟合的角度出发估计混沌映射的演化参数,进而通过相空间重构以及投影操作,实现对观测信号的噪声抑制. 实验结果表明,该算法的去噪效果优于扩展卡尔曼滤波器（extended Kalman filter,EKF）和无先导卡尔曼滤波器（unscneted Kalman filter,UKF）,并且能够较大程度地将信号源的混沌特征量还原. 关键词： 混沌 噪声抑制 相空间重构 投影     

混沌信号的压缩感知去噪

对非线性时间序列进行噪声抑制是从中提取有效信息的前提. 混沌信号的去噪算法不仅要使滤波后的信号具有较高的信噪比, 也要具有较好的不确定性. 从压缩感知的角度出发,提出了一种新的噪声抑制方法. 该方法包括估计噪声方差, 以及依据动态的稀疏度将观测值往确定的过完备字典上投影. 仿真实验表明, 该方法比常用的小波阈值法和局部曲线拟合法具有更高的输出信噪比, 而原始信号的混沌特性也能得到较大程度的恢复.     

An innovation based random weighting estimation mechanism for denoising fiber optic gyro drift signal

In Interferometric Fiber Optic Gyroscope (IFOG), the diminution of random noise and drift error is a critical task. These errors degrade the performance of IFOG. In this paper, a modified adaptive Kalman gain correction (AKFG) algorithm is proposed to denoise IFOG signal. The covariance matrix of innovation sequence is estimated using weighted average window method in which the weights are randomly generated in the range [0, 1]. Innovation based random weighted estimation (IRWE)-AKFG is applied to denoise the IFOG drift signal. The Kalman gain is adaptively updated using the covariance matrix of innovation sequence. The proposed algorithm is applied for denoising IFOG signal under static and dynamic environment. Allan variance method is used to analyze and quantify the stochastic errors in IFOG sensor. The performance of the proposed algorithm is compared with Conventional Kalman filter (CKF) and the simulation results reveal that the proposed algorithm is an efficient algorithm for denoising the IFOG signal.     

Double-image encryption based on discrete fractional random transform and chaotic maps

A novel double-image encryption algorithm is proposed, based on discrete fractional random transform and chaotic maps. The random matrices used in the discrete fractional random transform are generated by using a chaotic map. One of the two original images is scrambled by using another chaotic map, and then encoded into the phase of a complex matrix with the other original image as its amplitude. Then this complex matrix is encrypted by the discrete fractional random transform. By applying the correct keys which consist of initial values, control parameters, and truncated positions of the chaotic maps, and fractional orders, the two original images can be recovered without cross-talk. Numerical simulation has been performed to test the validity and the security of the proposed encryption algorithm. Encrypting two images together by this algorithm creates only one encrypted image, whereas other single-image encryption methods create two encrypted images. Furthermore, this algorithm requires neither the use of phase keys nor the use of matrix keys. In this sense, this algorithm can raise the efficiency when encrypting, storing or transmitting.     

A universal algorithm to generate pseudo-random numbers based on uniform mapping as homeomorphism

A specific uniform map is constructed as a homeomorphism mapping chaotic time series into [0,1] to obtain sequences of standard uniform distribution. With the uniform map, a chaotic orbit and a sequence orbit obtained are topologically equivalent to each other so the map can preserve the most dynamic properties of chaotic systems such as permutation entropy. Based on the uniform map, a universal algorithm to generate pseudo random numbers is proposed and the pseudo random series is tested to follow the standard 0-1 random distribution both theoretically and experimentally. The algorithm is not complex, which does not impose high requirement on computer hard ware and thus computation speed is fast. The method not only extends the parameter spaces but also avoids the drawback of small function space caused by constraints on chaotic maps used to generate pseudo random numbers. The algorithm can be applied to any chaotic system and can produce pseudo random sequence of high quality, thus can be a good universal pseudo random number generator.     

基于提升小波变换的阈值改进去噪算法在紫外可见光谱中的研究

在紫外可见光谱定量分析中,由于分光光度计内部的光学系统、光源、检测器、电子元器件,电路设计以及外部环境干扰等因素产生的随机噪声,严重影响光谱定量分析结果的准确性,为提高紫外可见光谱分析精度,需要对光谱数据进行去噪预处理。由于小波分析具有多分辨率,低熵性、去相关性等特点,基于小波分析的去噪算法优于传统的去噪算法,目前基于小波去噪的方法主要有模极大值去噪算法,系数相关去噪算法,阈值去噪算法,工程实际应用以Donoho的阈值去噪法最为常用。根据Donoho阈值消噪原理,提出一种基于提升小波变换的阈值改进算法,一方面使用提升小波变换,提升小波变换是第二代小波变换,继承了小波的多分辨率特性,并且不需要进行傅里叶变换,从而具有算法简单,速度快,实现简单的优点;另一方面提出了一种新的阈值函数,克服了硬阈值函数在阈值处不连续以及软阈值函数存在恒定偏差的问题,同时对阈值估计进行了调整,有利于信号小波系数的保留和噪声小波系数的剔除。对三组多金属离子混合溶液的实测紫外可见光谱信号,添加随机噪声后使用该方法进行去噪处理,并使用信噪比(SNR)和均方根误差(RMSE)进行去噪性能评价。试验结果表明,提出的算法优于Donoho的软硬阈值去噪算法,能够有效提高光谱信噪比和降低均方根误差,从而更好地消除光谱信号中的噪声和保留光谱信号中一些重要的细节特征,比较适合用于紫外可见光谱数据建模之前的去噪预处理,在紫外可见光谱信号分析中具有较好的应用前景。     

Denoising of chaotic signal using independent component analysis and empirical mode decomposition with circulate translating

In this paper, a new method to reduce noises within chaotic signals based on ICA(independent component analysis)and EMD(empirical mode decomposition) is proposed. The basic idea is decomposing chaotic signals and constructing multidimensional input vectors, firstly, on the base of EMD and its translation invariance. Secondly, it makes the independent component analysis on the input vectors, which means that a self adapting denoising is carried out for the intrinsic mode functions(IMFs) of chaotic signals. Finally, all IMFs compose the new denoised chaotic signal. Experiments on the Lorenz chaotic signal composed of different Gaussian noises and the monthly observed chaotic sequence on sunspots were put into practice. The results proved that the method proposed in this paper is effective in denoising of chaotic signals.Moreover, it can correct the center point in the phase space effectively, which makes it approach the real track of the chaotic attractor.     

Denoising via truncated sparse decomposition

This paper proposes a denoising algorithm called truncated sparse decomposition(TSD) algorithm,which combines the advantage of the sparse decomposition with that of the minimum energy model truncation operation.Experimental results on two real chaotic signals show that the TSD algorithm outperforms the recently reported denoising algorithms- non-negative sparse coding and singular value decomposition based method.     

基于多混沌系统的双图像光学加密算法

为了扩展双图像光学加密算法的密钥空间,克服双随机相位加密系统中随机相位掩模作为密钥难于存储、传输和重构的问题,突破传统图像加密的研究思路,提出了一种基于多混沌系统的双图像加密算法,构造了光学加密系统。系统增加混沌系统参数作为密钥,利用混沌加密密钥空间大和图像置乱隐藏性好的特点,构建基于Logistic混沌映射的图像置乱算法,利用Kent混沌映射生成的伪随机序列构造出一对随机相位掩模,分别放置在分数傅里叶变换光学装置的两端,图像经加密系统变换后得到密文。数值仿真结果表明,算法的密钥敏感性极高,能够有效地对抗统计攻击,具有较高的安全性。     

A Bit Shift Image Encryption Algorithm Based on Double Chaotic Systems

A chaotic system refers to a deterministic system with seemingly random irregular motion, and its behavior is uncertain, unrepeatable, and unpredictable. In recent years, researchers have proposed various image encryption schemes based on a single low-dimensional or high-dimensional chaotic system, but many algorithms have problems such as low security. Therefore, designing a good chaotic system and encryption scheme is very important for encryption algorithms. This paper constructs a new double chaotic system based on tent mapping and logistic mapping. In order to verify the practicability and feasibility of the new chaotic system, a displacement image encryption algorithm based on the new chaotic system was subsequently proposed. This paper proposes a displacement image encryption algorithm based on the new chaotic system. The algorithm uses an improved new nonlinear feedback function to generate two random sequences, one of which is used to generate the index sequence, the other is used to generate the encryption matrix, and the index sequence is used to control the generation of the encryption matrix required for encryption. Then, the encryption matrix and the scrambling matrix are XORed to obtain the first encryption image. Finally, a bit-shift encryption method is adopted to prevent the harm caused by key leakage and to improve the security of the algorithm. Numerical experiments show that the key space of the algorithm is not only large, but also the key sensitivity is relatively high, and it has good resistance to various attacks. The analysis shows that this algorithm has certain competitive advantages compared with other encryption algorithms.     

A novel feedback active noise control for broadband chaotic noise and random noise

The feedback active noise control (ANC) can be seen as a predictor, the conventional method based on filtered-x least mean square (FXLMS) algorithm can only be useful for linear and tonal noise, but for nonlinear and broadband noise, it is useless. The feedback ANC using functional link artificial neural networks (FLANN) based on filtered-s least mean square (FSLMS) algorithm can reduce some nonlinear noise such as chaotic noise, but the noise cancellation performance is not very well, at the same time, it is not useful to random noise. To solve the problem above, a new feedback ANC using wavelet packet FXLMS (WPFXLMS) algorithm is proposed in this paper. By decomposing the broadband noise into several band-limited parts which are predictable and each part is controlled independently, the proposed algorithm can not only suppress the chaotic noise, but also mitigate the random noise. Compared with FXLMS and FSLMS algorithms, proposed WPFXLMS algorithm also holds the best performance on noise cancellation. Numerous simulations are conducted to demonstrate the effectiveness of the proposed WPFXLMS algorithm.     

Chaotic signal denoising algorithm based on sparse decomposition

Denoising of chaotic signal is a challenge work due to its wide-band and noise-like characteristics. The algorithm should make the denoised signal have a high signal to noise ratio and retain the chaotic characteristics. We propose a denoising method of chaotic signals based on sparse decomposition and K-singular value decomposition(K-SVD) optimization. The observed signal is divided into segments and decomposed sparsely. The over-complete atomic library is constructed according to the differential equation of chaotic signals. The orthogonal matching pursuit algorithm is used to search the optimal matching atom. The atoms and coefficients are further processed to obtain the globally optimal atoms and coefficients by K-SVD. The simulation results show that the denoised signals have a higher signal to noise ratio and better preserve the chaotic characteristics.     

基于小波变换的压缩感知理论对水质检测紫外-可见光谱数据的去噪研究

消除噪声影响对提高直接光谱法水质检测系统的测量稳定性和精度都具有重要意义。直接光谱法在线水质检测系统通常采用长寿命、无需预热的脉冲氙灯和适用于复杂检测环境的工业级光谱探测装置。针对整个光谱探测系统受到光源、光路和光电转换器件的严重影响,测定的光谱数据含有大量噪声这一实际问题,提出了基于小波变换的压缩感知去噪算法,并与传统小波阈值去噪方法进行了比较实验。针对化学需氧量为200 mg·L-1的邻苯二甲酸氢钾标液的紫外-可见光谱数据进行去噪处理,采用压缩感知去噪算法,将信号在小波域内分解,得到含噪高频系数;采用随机高斯矩阵作为压缩感知算法的观测矩阵,压缩比设置为2,对高频系数进行观测;选择正交匹配追踪算法恢复高频小波系数的稀疏性,从而达到去噪目的。同时针对传统的小波阈值去噪,采用daubechies4作为小波基的软阈值滤波方法对光谱数据进行去噪处理。为验证去噪算法的可行性,采集某溪水和城市生活污水的光谱信号分别采用以上两种方法进行去噪处理,实验结果表明：基于小波变换的压缩感知去噪算法适用于紫外-可见光谱法在线水质检测系统,该方法能在保留水样原始光谱信号的吸收特征的前提下有效地去噪,且去噪效果优于小波阈值去噪算法。与小波阈值去噪算法相比,信噪比提高了12.201 5 dB,均方根误差减小了0.009 3,峰值信噪比增加了5.299 dB。不仅避免了小波阈值去噪过程中阈值的选取依靠主观判断问题,而且在重构过程中有效地抑制了噪声,为直接光谱法检测水质参数提供了一种新的解决方案。     

Double image encryption based on discrete multiple-parameter fractional Fourier transform and chaotic maps

A double image encryption method is proposed by utilizing discrete multiple-parameter fractional Fourier transform and chaotic maps. One of the two original images scrambled by one chaotic map is encoded into the amplitude of a complex signal with the other original image as its phase. The complex signal multiplied by another chaotic random phase mask is then encrypted by discrete multiple-parameter fractional Fourier transform. The parameters in chaotic map and discrete multiple-parameter fractional Fourier transform serve as the keys of this encryption scheme. Numerical simulations have been done to demonstrate the performance of this algorithm.     

Digital image watermarking using gyrator transform and chaotic maps

We propose a new method for digital image watermarking using gyrator transform and chaotic maps. Four chaotic maps have been used in the proposed technique. The four chaotic maps that have been used are the logistic map, the tent map, the Kaplan-Yorke map and the Ikeda map. These chaotic maps are used to generate the random phase masks and these random phase masks are known as chaotic random phase masks. A new technique has been proposed to generate the single chaotic random phase mask by using two chaotic maps together with different seed values. The watermark encoding method in the proposed technique is based on the double random phase encoding method. The gyrator transform and two chaotic random phase masks are used to encode the input image. The mean square error, the peak signal-to-noise ratio and the bit error rate have been calculated. Robustness of the proposed technique has been evaluated in terms of the chaotic maps, the number of the chaotic maps used to generate the CRPM, the rotation angle of the gyrator transform and the seed values of the chaotic random phase masks. Optical implementation of the technique has been proposed. The computer simulations are presented to verify the validity of the proposed technique.     

Multi-Image Encryption Algorithm Based on Cascaded Modulation Chaotic System and Block-Scrambling-Diffusion

To address the problem of a poor security image encryption algorithm based on a single chaotic map, this paper proposes a cascade modulation chaotic system (CMCS) that can generate multiple chaotic maps. On this basis, a multi-image encryption algorithm with block-scrambling-diffusion is proposed using CMCS. The algorithm makes full use of the features of CMCS to achieve the effect of one encryption at a time for images. Firstly, the key-value associated with the plaintexts is generated using a secure hash algorithm-512 (SHA-512) operation and random sequence, and the three images are fully confused by the double scrambling mechanism. Secondly, the scrambled image is converted into a bit-level matrix, and the pixel values are evenly distributed using the bit-group diffusion. Finally, the non-sequence diffusion of hexadecimal addition and subtraction rules is used to improve the security of the encryption algorithm. Experimental results demonstrate that the encryption algorithm proposed in this paper has a good encryption effect and can resist various attacks.     

Improvement of signal-to-noise ratio in chaotic laser radar based on algorithm implementation

Chaotic laser radar based on correlation detection is a high-resolution measurement tool for remotely monitoring targets or objects.However,its effective range is often limited by the side-lobe noise of correlation trace,which is always increased by the randomness of the chaotic signal itself and other transmission channel noises or interferences.The experimental result indicates that the wavelet denoising method can recover the real chaotic lidar signal in strong period noise disturbance,and a signal-to-noise ratio of about 8 dB is increased.Moreover,the correlation average discrete-component elimination algorithm significantly suppresses the side-lobe noise of the correlation trace when 20 dB of chaotic noise is embedded into the chaotic probe signal.Both methods have advantages and disadvantages.