Adaptive Bistable Stochastic Resonance Based Weak Signal Reception in Additive Laplacian Noise

Weak signal reception is a very important and challenging problem for communication systems especially in the presence of non-Gaussian noise,and in which case the performance of optimal linear correlated receiver degrades dramatically.Aiming at this,a novel uncorrelated reception scheme based on adaptive bistable stochastic resonance(ABSR)for a weak signal in additive Laplacian noise is investigated.By analyzing the key issue that the quantitative cooperative resonance matching relationship between the characteristics of the noisy signal and the nonlinear bistable system,an analytical expression of the bistable system parameters is derived.On this basis,by means of bistable system parameters self-adaptive adjustment,the counterintuitive stochastic resonance(SR)phenomenon can be easily generated at which the random noise is changed into a benefit to assist signal transmission.Finally,it is demonstrated that approximately 8dB bit error ratio(BER)performance improvement for the ABSR-based uncorrelated receiver when compared with the traditional uncorrelated receiver at low signal to noise ratio(SNR)conditions varying from-30dB to-5dB.

An innovative detection method of high frequency BPSK signal with low signal-to-noise ratio

Based on chaotic oscillator system, this paper proposes a novel method on high frequency low signal-to-noise ratio BPSK(Binary Phase Shift Keying) signal detection. Chaotic oscillator system is a typical non-linear system which is sensitive to periodic signals and immune to noise at the same time. Those properties make it possible to detect low signal-to-noise ratio signals. The BPSK signal is a common signal type which is widely used in modern communication. Starting from the analysis of advantages of chaotic oscillator system and signal features of the BPSK signal, we put forward a unique method that can detect low signal-to-noise ratio BPSK signals with high frequency. The simulation results show that the novel method can detect low signal-to-noise ratio BPSK signals with frequency in an order of magnitude of 108 Hz, and the input Signal-to-Noise Ratio threshold can be -20 dB.

Combining Radon-ambiguity transform with second-order difference to improve detection probability of LFM signals in low SNR

The Radon-ambiguity transform （RAT）, although efficient for detecting the linear frequency modulated signals （LFMs）, is troubled by the energy accumulation of noise in low signal-to-noise ratio （SNR）. A secondorder difference （SOD） method is proposed to treat with this problem. In the SOD method, the optimal search step and difference step are derived from the LFM rate resolution formula. The sharpness of the peaks of RAT is measured by curvature, and the sharpness, but not the magnitude of the peaks, is used to detect the LFMs. The SOD method removes the noise energy accumulation and reserves the drastically changing components integrally; thus, it improves the detection probability of LFMs in low SNR. The expected performance of the new method is verified by 100 Monte Carlo simulations.

Long term integration of radar signals with unknown Doppler shift for ubiquitous radar

Ubiquitous radar is a new radar system that provides continuous and uninterrupted multifunction capability within a coverage volume. Continuous coverage from close-in ＂pop-up＂ targets in clutter to long-range targets impacts selection of waveform parameters. The coherent processing interval （CPI） must be long enough to achieve a certain signal-to-noise ratio （SNR） that ensures the efficiency of detection. The condition of detection in the case of low SNR is analyzed, and three different cases that would occur during integration are discussed and a method to determine the CPI is presented. The simulation results show that targets detection with SNR as low as -26 dB in the experimental system can possibly determine the CPI.

New Wavelet Thresholding Algorithm in Dropping Ambient Noise from Underwater Acoustic Signals

Underwater Wireless Communication, largely dependent on the acoustic communication between the machines, is largely affected by various types of noise in the shallow and deep water. However ambient noise which is due to multiple sources (e.g. shipping, wind) and no one source dominates. Ambient noise masks the acoustic signal to a large extent. Hence today it has drawn the attention of the experts to reduce its effect on the received signal. This paper discusses ambient noise problem and devises a new wavelet thresholding method to reduce its effect. Afterwards a comparative study on statistical parameters is shown to prove the efficiency of the devised method.

Micro-Doppler feature extraction of micro-rotor UAV under the background of low SNR

Micro-Doppler feature extraction of unmanned aerial vehicles(UAVs)is important for their identification and classification.Noise and the motion state of the UAV are the main factors that may affect feature extraction and estimation precision of the micro-motion parameters.The spectrum of UAV echoes is reconstructed to strengthen the micro-motion feature and reduce the influence of the noise on the condition of low signal to noise ratio(SNR).Then considering the rotor rate variance of UAV in the complex motion state,the cepstrum method is improved to extract the rotation rate of the UAV,and the blade length can be intensively estimated.The experiment results for the simulation data and measured data show that the reconstruction of the spectrum for the UAV echoes is helpful and the relative mean square root error of the rotating speed and blade length estimated by the proposed method can be improved.However,the computation complexity is higher and the heavier computation burden is required.

Wigner-Hough transform based on slice's entropy and its application to multi-LFM signal detection

To enhance the capacity of the radar-reconnaissance interception receiver recognizing linear frequency modulated (LFM) at a low signal-noise ratio, this paper presents WignerHough transform (WHT) of the LFM signal and its corresponding characteristics, derives the probability density functions of the LFM signal and Gaussian white noise within WHT based on entropy (WHTE), dimension under different assumptions and puts forward a WHT algorithm based on entropy of slice to improve the capacity of detecting the LFM signal. Entropy of the WHT domain slice is adopted to assess the information size of polar radius or angle slice, which is converted into the weight factor to weight every slice. Double-deck weight is used to weaken the influences of noise and disturbance terms and WHTE treatment and signal detection procedure are also summarized. The rationality of the algorithm is demonstrated through theoretical analysis and formula derivation, the efficiency of the algorithm is verified by simulation comparison between WHT, fractional Fourier transform and periodic WHT, and it is highlighted that the WHTE algorithm has better detection accuracy and range of application against strong noise background.

Tracking algorithm of BPSK signal in low bit SNR and high dynamic scenarios

A new tracking algorithm is proposed aiming at the tracking problem in low bit signal-to- noise ratio （i. e. , Eb/N0 ） scenarios, in which the bit clock regenerated by bit synchronization loop decides loop update moment. The double frequency processing and non-coherent accumulation tech- nologies are applied to eliminate the impact of data polarity inversion, and then long time accumula- tion improves the input signal-to-noise ratio of discriminator. The frequency locked loop and phase locked loop constitute a carrier loop in parallel, which can meet the high dynamic demands. The ef- fectiveness of this algorithm has been corroborated by theoretical analysis, simulation and measure- ments, and the new tracking algorithm has been used in an aerospace engineering project successfully.

基于基带S模式信号的联合TOA估计算法研究

针对广域多点定位系统中接收信号信噪比(signal to noise ratio,SNR)低导致到达时间(time of arrival,TOA)提取不准确的问题,提出了一种匹配滤波结合非相干积累的联合TOA估计算法,该算法能够有效降低匹配滤波法在低SNR下TOA估计的均方根误差(root mean square error,RMSE).联合算法通过对二次雷达驻留时间内接收的应答信号的匹配滤波输出做非相干积累,在最大值点处标记时间戳进行TOA估计,利用能量累积原理提高SNR,从而使得估计精确度得以提升.仿真结果表明:该算法可在-15 dB SNR,53 MHz采样频率及9个积累信号时,达到24.302 ns的TOA估计精确度.所提联合TOA估计算法具有高精确度与高稳健性的特点,能够在-15~0 dB SNR将估计精确度提升至25 ns以下,为提取低SNR S模式信号TOA进而提升广域多点定位精确度提供了一种有效的方法.

基于小波奇异特征约束的期望最大时延估计算法

针对低信噪比条件下非平稳信号时延估计精度低的问题,提出基于小波奇异特征约束的期望最大时延估计算法。设计小波奇异性特征尺度广义互相关矩阵,构建多尺度小波奇异特征约束下的期望最大化模型。推导参数更新公式,利用期望最大化算法并行迭代,求取奇异性特征显著性最大条件下信号的自适应尺度以及该尺度下声源信号的最优时延估计值。仿真和实验结果表明,所提算法在低信噪比条件下,相较于传统广义互相关时延估计算法以及改进算法具有较高的时延估计精度,并且有效提高了误差约束范围内的有效估计成功率。

利用双通道激光雷达验证低信噪比反演算法

针对相干测风激光雷达(LiDAR)在低信噪比(SNR)下反演算法的可靠性和精度难以验证的问题,搭建了一种双通道的脉冲相干测风LiDAR系统,可以同时获取高、低SNR的2组数据,并使用高SNR通道的结果作为真值,比较利用不同的算法在低SNR通道进行矢量风速估计的结果。该系统避免了使用其他观测设备作为真值时难以配准或观测目标不统一的问题。本文研究利用雷达获取的观测结果验证了各个算法的有效性,并在各个算法间进行了横向对比。最终,依据各个算法的性能表现和计算复杂度,指明了各个算法具有优势的使用场景。该结果对于充分利用现有系统、使用新算法提高低SNR下的数据获取效率,或在指定探测指标的前提下降低激光能量等硬件要求、实现系统小型化有一定意义。

基于混沌粒子群优化的图像Contourlet阈值去噪

提出了基于混沌粒子群优化的图像Contourlet阈值去噪方法.该方法在Contourlet变换域内利用混沌粒子群算法来确定最优阈值,再通过软阈值函数去噪,且不需要噪音方差等先验信息.实验结果表明:该方法与小波Bayeshrink阈值、基于粒子群的小波阈值、Contourlet自适应阈值等去噪方法相比,能有效地去除高斯白噪音和椒盐噪音的混合噪音,提高峰值信噪比,并较好地保留图像的细节和纹理,从而明显地改善了图像的视觉效果.

基于级数展开的位置误差加权定位方法

随着对海洋探索的愈加深入,对水下目标探测能力的要求也愈发严格。针对噪声干扰严重时算法性能大幅度下降的问题,文章主要研究仅利用分布式矢量水听器的波达方向信息对未知目标进行融合定位。在已有的基于位置误差加权方法的基础上,利用几何二次约束减小波达方位角量测误差,并结合泰勒级数展开,将一阶权值推导至高阶,解决在低信噪比情况下定位精度差的问题。仿真结果表明:相对于传统的基于方差倒数的自适应加权算法,该方法在定位精度上有很大提高,尤其在低信噪比的情况下效果显著。

基于二分搜索的宽带多普勒模型信号捕获方法

针对深空通信等超高动态低信噪比通信场景中,传统信号捕获方法存在着动态范围不足、精确度不够以及使用窄带多普勒模型导致对接收信号近似精确度不高的问题,提出一种基于二分搜索的接收端采样率调整方法。使用宽带多普勒模型对接收信号进行建模,并使用扫频余弦类信号进行多普勒频偏估计和定时估计,处理多普勒伸缩后的信号与本地信号采样率不匹配的问题。对所提方法进行同步性能仿真,仿真结果表明,所提方法采用正反扫频线性调频(UD-LFM)信号,能在信噪比为-49dB、最大多普勒频偏为2MHz的条件下,多普勒频偏估计误差小于400 Hz,定时估计误差不超过60 ns。相比不使用本文所提方法进行直接捕获,本文所提方法具有更高的捕获精确度以及更低的信噪比门限。

铝合金电池端板低压铸造工艺参数的设计与优化

以某新能源汽车铝合金电池端板为研究对象,基于正交试验设计以及信噪比分析法探究低压铸造过程中金属液的浇注温度、模具预热温度以及充型时间3个工艺参数对铸件成型质量的影响,以缩松缩孔体积值、凝固时间和二次枝晶间距大小为评价指标,探究出一套标准化的工艺设计参数,为新能源汽车铝合金电池端板低压铸造成型工艺研究与实际生产提供重要参考。研究结果显示:浇注温度690℃,模具预热温度330℃,充型时间8 s时铸件无缩松缩孔缺陷,二次枝晶间距最小,凝固时间最短。

复杂背景下小样本声呐图像目标检测

水下目标检测具有重要意义,在军事和民用领域都发挥着重要作用。实际场景中可以获得的声呐图像非常有限,且声呐图像的信噪比较低,无法得到较好的检测结果。因此,本文引入小样本学习,基于Faster RCNN两阶段目标检测算法,选择不同的策略对模型进行优化,得到了较好的检测结果并验证了小样本目标检测在声呐图像领域的可行性。根据混响对声呐图像的影响进行仿真实验,得到不同混响背景下的声呐图像,对比分析了不同数据集下训练模型的检测性能。实验结果表明,在训练样本中增加混响信号可以提高低信噪比条件下的目标检测精度。

基于改进VMD-WTFD算法的二次谐波光谱信号反演分析

采用变分模态分解和小波阈值调整函数(VMD-WTFD)算法在处理噪声时,信号波形恢复往往偏离标准的谐波信号,导致反演结果出现偏差。提出了一种改进VMD-WTFD算法,该算法采用新型阈值函数对VMD-WTFD算法进行改进,然后以此改进算法对二次谐波光谱信号进行反演分析。仿真与实验结果表明:与其它几种降噪算法相比,改进VMD-WTFD算法在不同调制度下反演出的原始信号的峰值、峰位和峰谷值更接近原始标准二次谐波光谱信号,去噪后的信噪比为17.545 dB,相关系数可达到0.9929;可以较清晰地反演出人体呼出气体中CO_(2)的二次谐波幅值。

低载噪比突发扩频信号的快速捕获设计

突发扩频信号是卫星无线电测定业务的一种扩频调制信号,针对传统突发扩频信号,即入站信号,对快速捕获灵敏度要求越来越高的需求,提出了一种低载噪比突发扩频信号的快速捕获硬件设计实现方法。利用同步头加Walsh码基于快速傅里叶变换(Fast Fourier Transform,FFT)的突发扩频信号全并行处理的方法,入站信号快捕接收的积分时长能够达到15 ms,相较于传统的捕获算法,性能实现了约2 dB的提升。仿真验证及工程实现结果表明,该方案载噪比可达到37.5 dBHz,为未来全球化小型化通用化接收终端提供了改进方向。

基于分式二次规划的互模糊函数赋形方法

在开展认知雷达波形设计时,由于发射波形与接收滤波器的非匹配体制,互模糊函数赋形相比传统模糊函数赋形优化自由度更高。该文针对强杂波条件下微弱运动目标检测问题,以最大化信干噪比为优化准则,提出了一种联合发射相位编码序列与接收滤波器设计的互模糊函数赋形方法。在恒模约束下,优化问题被建模为二次分式规划形式;然后通过引入辅助变量,并利用共轭梯度法求解Stiefel流形空间上的最小化问题,非凸优化据此转化为恒模约束二次优化问题;通过交替循环和类幂迭代算法求得最优解。此外考虑到发射波形受硬件限制而难以实现严格恒模,该文构建了一种低峰均比约束二次优化问题模型,并利用最近邻向量法求得最优解。最后,不同参数下的仿真与实测数据实验表明,该文赋形方法相较于传统方法具有较高的信干噪比增益和收敛速度。

基于FRFT的低信噪比LFM信号参数快速估计算法

基于分数阶傅里叶变换(Fractional Fourier Transform,FRFT)对线性调频(Linear Frequency Modulated,LFM)信号参数进行估计,问题关键是确定FRFT最佳阶数,根据误差迭代思想提出新的参数估计算法,该算法利用归一化带宽和旋转角的转化关系,由估计误差推算角度差值,有效降低了运算量,不需要调频斜率正负的先验信息,改进的对数搜索算法可以进一步提高参数估计结果的稳定性和可靠性。仿真结果表明,信噪比在-8 dB以上时该方法在高效率的前提下仍具有良好的参数估计性能,平均估计误差在1%以内,估计结果接近Cramer-Rao下限,满足工程实时处理需求。