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.     

Comparison of simultaneous signals obtained from a dual-field-of-view lidar and its application to noise reduction based on empirical mode decomposition

Although the empirical mode decomposition (EMD) method is an effective tool for noise reduction in lidar signals, evaluating the effectiveness of the denoising method is difficult. A dual-field-of-view lidar for observing atmospheric aerosols is described. The backscattering signals obtained from two channels have different signal-to-noise ratios (SNRs). The performance of noise reduction can be investigated by comparing the high SNR signal and the denoised low SNR signal without a simulation experiment. With this approach, the signal and noise are extracted to one intrinsic mode function (IMF) by the EMD- based denoising; thus, the threshold method is applied to the IMFs. Experimental results show that the improved threshold method can effectively perform noise reduction while preserving useful sudden-change information.     

Dual-beam coaxial amplification system of stimulated Brillouin scattering based on polarization control technology

In the field of Brillouin lidar, it has very important significance to find one method that can amplify the Brillouin scattering signal in real time. One new-type Brillouin lidar detection system based on Nd:YAG pulsed laser and polarization control device is designed in this paper. The key point of this detection system is to have two pulsed coherent lights with same frequency, same polarization and same initial phase, of which one beam is taken as the detection wave for generating stimulated Brillouin scattering signal and the other beam is taken as pumping wave for real time and effective amplification of stimulated Brillouin scattering signal. This detection system mainly includes two pulsed lasers and one electro-optical polarization controller. The laser is mainly used to obtain the pulsed lights with same frequency and same phase, and the polarization controller is mainly used to change the polarization state of two coaxial beams to make them change into same polarization state from orthogonal polarization state thus to enable the pumping wave to amplify the backward stimulated Brillouin scattering signal. It is shown from the experimental results that the adoption of this new system can realize the effect of pumping amplification and can increase the signal to noise ratio to a certain extent.     

Lidar signal de-noising based on wavelet trimmed thresholding technique

Lidar is an efficient tool for remote monitoring, but the effective range is often limited by signal-to-noise ratio (SNR). By the power spectral estimation, we find that digital filters are not fit for processing lidar signals buried in noise. In this paper, we present a new method of the lidar signal acquisition based on the wavelet trimmed thresholding technique to increase the effective range of lidar measurements. The performance of our method is investigated by detecting the real signals in noise. The experiment results show that our approach is superior to the traditional methods such as Butterworth filter.     

A method of background noise reduction in lidar data

This paper applies a theoretical approach to the calculation of background noise levels during the analysis of lidar (light detection and ranging) data. We develop a method for the identification of background noise concealed within lidar signals under clear atmospheric or homogeneous aerosol layer conditions and derive an equation for the calculation of these noise levels from a theoretical consideration of the lidar equation. An increasing range-corrected signal indicates that a large amount of background noise exist in the return signal. We calculate the level of background noise by selecting three equidistant points in the return signal from the homogeneous layer and inputting the range and intensity of these points into the derived equation. Background noise calculations using actual lidar signals were in good agreement with calculations based on a simulated lidar signal. The background noise equation was verified using both observational lidar data and a simulated signal, indicating that it provides a reasonable measure of background noise levels in lidar data.     

Fourier-wavelet regularized deconvolution (ForWaRD) for lidar systems based on TEA-CO2 laser

Lidar is an efficient tool for remotely measuring physical quantities or detecting targets. To improve the range resolution in long pulse lidars, such as lidar systems based on TEA-CO₂ lasers, deconvolution methods were used by previous investigators. Deconvolution is a noise sensitive process. In order to avoid noise amplification during the deconvolution process, the Fourier-wavelet regularized deconvolution method is used to deconvolve and denoise the back-scattered lidar signal simultaneously. This method is applied to lidar systems based on the TEA-CO₂ laser and the results are compared to nitrogen tail clipping method. Numerical simulation shows, in comparison to the clipping nitrogen tail technique, by using the ForWaRD method; the range resolution and working distance of the lidar is improved and the clipping tail apparatus is also eliminated.     

基于回波信号仿真的瑞利-喇曼-米激光雷达研制

 在分析瑞利、喇曼和米散射仿真回波信号的基础上,研制了一台探测大气温度、气溶胶和卷云的瑞利-喇曼-米散射激光雷达,实现了一台激光雷达针对大气温度、气溶胶和卷云光学特性的多参数探测。为提高瑞利和喇曼微弱回波信号信噪比,采用了极高灵敏度的R4632光电倍增管和光子计数技术;为实现对大气气溶胶和卷云的探测,532 nm回波信号采取高低分层技术、高层通道回波衰减方法和探测器门控技术。瑞利-喇曼-米散射激光雷达的探测结果证明了利用仿真回波信号指导激光雷达设计的可行性。     

脉冲激励下环形耦合Duffing振子间的瞬态同步突变现象

研究非周期信号激励下Duffing振子动力学行为变化特征时,发现处于倍周期分岔的环形耦合Duffing振子系统,在一定的参数条件下,脉冲信号能引起其中一个振子与其他振子运动轨迹间出现短暂失同步的现象即瞬态同步突变现象.利用这种现象可以快速检测出强噪声背景中的微弱脉冲信号,从而扩展了现有的Duffing振子对非周期信号的检测范围及应用领域. 关键词： 瞬态同步突变 微弱信号检测 脉冲信号 Duffing振子     

基于压缩光的量子激光雷达技术

利用Wigner函数对真空态、单光子态、压缩态在相空间的噪声分布进行仿真,并系统分析了基于压缩光的量子相干激光雷达和压缩光注入式量子激光雷达.研究表明,相比经典激光雷达,较高压缩度有利于量子相干激光雷达探测信噪比的提升,理论上8dB的压缩度可以使信噪比提高6.25倍;而压缩光注入式量子激光雷达系统的空间分辨率主要取决于真空压缩光的压缩度和无噪声相敏放大系统的增益.由于压缩光对探测信噪比的提升作用,量子激光雷达在微弱信号探测和高分辨率成像领域具有显著优势.     

An Entropy-Based Anti-Noise Method for Reducing Ranging Error in Photon Counting Lidar

Photon counting lidar for long-range detection faces the problem of declining ranging performance caused by background noise. Current anti-noise methods are not robust enough in the case of weak signal and strong background noise, resulting in poor ranging error. In this work, based on the characteristics of the uncertainty of echo signal and noise in photon counting lidar, an entropy-based anti-noise method is proposed to reduce the ranging error under high background noise. Firstly, the photon counting entropy, which is considered as the feature to distinguish signal from noise, is defined to quantify the uncertainty of fluctuation among photon events responding to the Geiger mode avalanche photodiode. Then, the photon counting entropy is combined with a windowing operation to enhance the difference between signal and noise, so as to mitigate the effect of background noise and estimate the time of flight of the laser pulses. Simulation and experimental analysis show that the proposed method improves the anti-noise performance well, and experimental results demonstrate that the proposed method effectively mitigates the effect of background noise to reduce ranging error despite high background noise.     

Denoising lidar signal by combining wavelet improved threshold with wavelet domain spatial filtering

Lidar is an effective tool for remotely monitoring target or object, but the lidar signal is often affected by various noises or interferences. Therefore, detecting the weak signals buried in noises is a fundamental and important problem in the lidar systems. In this paper, an effective noise reduction method combining wavelet improved threshold with wavelet domain spatial filtration is presented to denoise pulse lidar signal and is investigated by detecting the simulating pulse lidar signals in noise. The simulation results show that this method can effectively identify the edge of signal and detect the weak lidar signal buried in noises.     

基于背景特征参数的激光雷达目标检测

激光雷达的弱小目标检测是激光雷达的关键技术, 其主要研究难点之一是在低信噪比下, 可用于区分目标与背景噪声的特征少。研究的对象是激光雷达的远距离目标回波, 主要指空中飞机目标。根据试验得到的数据, 发现目标点在背景中往往是一些孤立的点, 与背景的相关性较小。而背景中的任一点与前后背景点相关性较强, 可以用周围的点进行线形或非线性表示。为解决低信噪比下激光雷达的目标检测问题, 提出了基于背景特征参数的目标检测算法。运用高阶统计量作为背景特征值对杂波数据进行处理。在一个小区域内, 背景的高阶统计量不会有很大的起伏, 而目标在它所在的区域内具有相对突出的变化。信噪比得以提高, 然后通过恒虚警检测和多帧相关检测, 获取真正的目标。试验结果表明该方法非常有效, 实时性强, 具有较高的实用价值。     

一种基于广义Duffing振子的水中弱目标检测方法

海洋环境中,在水下目标的线谱频率未知或者目标辐射噪声的连续谱很弱时,很难实现水中弱目标的准确检测,本文提出基于广义Duffing振子检测系统的水下目标辐射噪声检测方法.通过对传统周期扰动的Duffing振子信号检测系统的分析和推广,提出了一种可输入非周期、非平稳信号的广义Duffing振子检测系统,可检测输入的无先验信息目标信号.为实现广义Duffing振子系统运动状态的精确、有效判断,提出了一种相空间图形的离散分布列计算方法,通过类网格函数实现了利用统计复杂度对系统输出的嵌入式表征,从而实现了无先验信息时的水中弱目标的嵌入式检测.相同条件下与传统检测方法仿真对比可知,本文提出的方法可以检测到更低信噪比下的目标,并能满足水中检测实时性要求.     

Noise reduction for lidar returns using local threshold wavelet analysis

Remote sensing technique of lidar belongs to the category of weak signal extraction under strong background noise. For effectively reducing the noise of lidar return signal, a wavelet analysis method using local threshold value is employed. In the local threshold value wavelet method, different threshold values are used to quantify the high frequency coefficients of every decomposition layer. Both the numerical simulation signal contaminated by random noise of different standard deviation and the practical Mie lidar returns were adopted, and the comparisons among sliding-window method, global threshold method and local threshold method were performed for verifying the feasibility of the local threshold method. Experiment results show that the local threshold wavelet method is a useful de-noising method which shows better effects of noise reduction than other two methods.     

密布式多输入多输出声呐阵列目标波达方向估计

针对低信噪比条件下多输入多输出声呐受对称噪声分量影响导致测向性能降低的情况,提出了一种基于协方差矩阵重构方法的波达方向估计算法。首先,将噪声场分为对称噪声和非对称噪声两部分,利用协方差矩阵虚部与对称信号无关的性质,去掉协方差矩阵的实部来降低对称噪声对目标波达方向估计精度的影响,采用降维转换方法和矩阵虚部置换原理重构协方差矩阵的实部,避免了双频谱的干扰。然后利用Toeplitz方法对重构的协方差矩阵进行解相干修正,通过奇异值分解获得噪声子空间,最后对目标的波达方向进行估计,可实现微弱信号的准确测向。理论分析和实验结果表明,该方法明显抑制了对称噪声,提高了目标的波达方向估计性能,具有运算速度快、自由度高和目标分辨力强的特点。      

Locking-based frequency measurement and synchronization of chaotic oscillators with complex dynamics

We propose a method for the determination of a characteristic oscillation frequency for a broad class of chaotic oscillators generating complex signals. It is based on the locking of standard periodic self-sustained oscillators by an irregular signal. The method is applied to experimental data from chaotic electrochemical oscillators, where other approaches of frequency determination (e.g., based on Hilbert transform) fail. Using the method we characterize the effects of phase synchronization for systems with ill-defined phase by external forcing and due to mutual coupling.     

光子计数激光雷达的时间相关卡尔曼深度估计

在高背景噪声和低积分时间的激光雷达远距离成像场景中,针对传统方法得到的深度图像目标被噪声淹没和深度估计偏差较大的问题,提出了一种基于信号光子时间相关性和自适应卡尔曼滤波器的深度信息估计方法。首先,提取在时间上具有聚集特征的光子计数形成集合;然后,分析了影响信号光子在时间上分布的因素并使用静态高斯线性模型来描述该集合;最后将集合中的所有光子飞行时间乱序,输入改进的自适应卡尔曼滤波器,从而迭代估计深度值。在信号噪声比为1的室内,积分时间分别为10 ms和1 ms时,本文方法相对传统的最大似然方法在均方根误差指标上提升了40%和38%。在信噪比约为0.135的室外2 km目标成像实验中,在信号光子数分别为100、33和17的情况下,本文方法成像效果都优于传统最大似然估计方法和时间相关光子快速去噪方法,得到的深度图像都更清晰,噪声更低。在高噪声和短积分时间下,本文方法可以被运用于激光雷达远距离成像的深度信息估计和图像恢复中。     

Signal enhancement of a novel multi-address coding lidar backscatters based on a combined technique of demodulation and wavelet de-noising

Multi-address coding (MAC) lidar is a novel lidar system recently developed by our laboratory. By applying a new combined technique of multi-address encoding, multiplexing and decoding, range resolution is effectively improved. In data processing, a signal enhancement method involving laser signal demodulation and wavelet de-noising in the downlink is proposed to improve the signal to noise ratio (SNR) of raw signal and the capability of remote application. In this paper, the working mechanism of MAC lidar is introduced and the implementation of encoding and decoding is also illustrated. We focus on the signal enhancement method and provide the mathematical model and analysis of an algorithm on the basis of the combined method of demodulation and wavelet de-noising. The experimental results and analysis demonstrate that the signal enhancement approach improves the SNR of raw data. Overall, compared with conventional lidar system, MAC lidar achieves a higher resolution and better de-noising performance in long-range detection.     

Noise reduction for lidar returns using self-adaptive wavelet neural network

Lidar has been widely applied in many fields, such as meteorology and environment. However, because lidar returns are very weak, the influence of noise on useful signal is very serious. To obtain useful lidar return signals from raw data, a self-adaptive method combining wavelet analysis and a neural network that suppresses noise is proposed, in which the orthogonal Daubechies wavelet family serves as node functions in the hidden layer of the neural network, a search algorithm is selected to optimize the parameters and thresholds, and the Levenberg–Marquardt algorithm is adopted in the neural network gradient algorithm. Some comparative experiments were carried out to verify the feasibility of the noise reduction method and the results showed that the signal-to-noise ratio (SNR) of the common wavelet threshold denoising method is about 10, while that of the self-adaptive wavelet neural network denoising method is more than 20. From the experimental results, it can be seen that the wavelet neural network denoising method has less distortion and a higher SNR value than other methods, giving it superior performance.