基于球面交点的AFM算法的误差分析与改进方法

基于球面交点的模糊度函数法是一种利用GPS信号求解载体姿态的有效算法,但其成功率受观测噪声影响很大,针对这一问题,该文对该算法进行了误差分析并提出提高成功率的改进方法。首先将原算法推广到双差观测模型,然后分析噪声的统计特性和误差传播的特点,深入研究了其对姿态解算成功率的影响,最后通过整周模糊度识别算法降低求解过程对噪声的敏感性。实验表明:相比于原算法,改进算法有效地提高了GPS姿态解算的成功率,同时消除了原算法对公共时钟的限制。     

Genetic algorithm and wavelet hybrid scheme for ECG signal denoising

This paper introduces an effective hybrid scheme for the denoising of electrocardiogram (ECG) signals corrupted by non-stationary noises using genetic algorithm (GA) and wavelet transform (WT). We first applied a wavelet denoising in noise reduction of multi-channel high resolution ECG signals. In particular, the influence of the selection of wavelet function and the choice of decomposition level on efficiency of denoising process was considered. Selection of a suitable wavelet denoising parameters is critical for the success of ECG signal filtration in wavelet domain. Therefore, in our noise elimination method the genetic algorithm has been used to select the optimal wavelet denoising parameters which lead to maximize the filtration performance. The efficiency performance of our scheme is evaluated using percentage root mean square difference (PRD) and signal to noise ratio (SNR). The experimental results show that the introduced hybrid scheme using GA has obtain better performance than the other reported wavelet thresholding algorithms as well as the quality of the denoising ECG signal is more suitable for the clinical diagnosis.     

采用局部特征尺度分解的跳频信号参数盲估计算法

针对现有跳频信号参数盲估计算法存在时间分辨率和频率分辨率矛盾这一问题,提出了一种基于局部特征尺度分解的跳频信号参数估计新算法。该算法将跳频信号迭代地分解成若干个内禀尺度分量并进行降噪处理,然后对其最大瞬时幅度进行小波变换和傅里叶变换即可估计出跳频信号的跳变时刻和跳频周期,最后根据得到的跳变时刻和跳频周期可以进一步估计出跳频频率集。该算法不受时频不确定性原理的影响,能够在未知先验知识的条件下估计出跳频信号的跳周期、跳变时刻和跳频频率集。最后通过仿真验证了算法的有效性。      

有噪信号下ADC量化特性的分析与应用

传统的ADC特性参数的测试,通常是将一纯度很高的模拟正弦信号作为激励信号,然后在无噪的条件下,通过对数字输出结果进行分析从而得出ADC的特性参数.然而,在很多实际应用领域中,通常输入的信号叠加有噪声.本文研究了有噪信号下ADC量化特性,提出了有噪信号下ADC量化特性的分析模型,并从理论上做出了相应解释.研究分析表明,有噪信号下ADC量化有其自身规律,利用本文提出的测试方法,输入信号的任何微小变化都可反映在测试结果中,这一方面可以使得在原高分辨率ADC的基础上获得更高的分辨率成为可能,另一方面可为微弱信号采集系统正确选用性能优良的ADC提供足够的技术支持,有助于改进微弱信号检测或低信噪比情况下的系统设计.最后给出了ADC有噪信号下的量化累加输出信噪比曲线,可为实际工程应用提供参考.     

Breathing detection: towards a miniaturized, wearable, battery-operated monitoring system

This paper analyzes the main challenges associated with noninvasive, continuous, wearable, and long-term breathing monitoring. The characteristics of an acoustic breathing signal from a miniature sensor are studied in the presence of sources of noise and interference artifacts that affect the signal. Based on these results, an algorithm has been devised to detect breathing. It is possible to implement the algorithm on a single integrated circuit, making it suitable for a miniature sensor device. The algorithm is tested in the presence of noise sources on five subjects and shows an average success rate of 91.3% (combined true positives and true negatives).     

有色随机噪声背景下互谱TLS-ESPRIT估计方法

现代互谱估计是抑制有色观测噪声的一种有效方法。对旋转不变技术估计信号参量的方法进行了深入的分析,并进而提出了有色声随机信号背景下互谱TLS-ESPRIT估计方法。这种方法的突出特点是几乎不需要任何色噪声的先验信息,并在很低信噪比下工作,避免了以往互谱估计本身所固有的在整个频域上的谱峰搜索,可直接通过特征值确定信号参数估计值。此方法只需两次SVD分解,计算量大大降低。仿真结果表明,该方法具有较好的谱估计分辨率和良好的稳定性。     

提高导引头测角精度的一种新方法

分析了基于小波变换系数极大值原理的小波阈值降噪算法。由于导引头接收信号的信噪比影响了MUSIC超分辨算法的测角精度，因此提出将小波阈值降噪算法用于MUSIC超分辨算法精度分析，并进行了仿真验证，实现了改善信噪比，提高导引头测角精度的目的。     

低采样率高分辨率压缩功率谱估计方法的仿真研究

低采样率的宽带功率谱估计在很多领域具有应用价值.采用压缩多核采样结构得到信号的压缩测量值, 然后建立测量值相关函数与信号相关函数之间的关系, 用最小二乘法实现相关函数估计, 最后实现功率谱的估计.该压缩采样方法的等效采样率为M/N·f_s, 可在没有任何对时域或频域稀疏性的假设条件下降低采样率.仿真分析表明, 该方法的系统噪声与加性噪声性能比周期图法略有降低, 但只要系统设计合理, 对于一定信噪比的信号, 系统噪声与加性噪声基本可以忽略, 并给出了对应的理论分析.估计分辨率与周期图法相比, 等效长度相同时略有提高; 由于本文方法降低了测量值的数目, 对于一定长度的数据来说, 估计分辨率得到了极大的提高.本文方法适用于低信噪比信号的低采样率高分辨率功率谱估计.     

一种基于高阶累积量的FSK信号识别新方法

文中介绍了一种基于高阶累积量的FSK信号分类方法.该方法直接对中频信号进行高阶累积量的特征提取,不需要进行载频估计和符号同步,克服了以往高阶累积量特征对参数估计的依赖.在判决方法上,结合了欧式距离分类和大数逻辑准则,提高了分类性能.仿真实验表明,该方法具有较好的识别性能,信噪比不低于6dB时,2/4FSK信号的识别率在98%以上,信噪比不低于1 3dB时,2/4/8FSK信号的识别率在90%以上.     

Noise reduction in oversampled filter banks using predictivequantization

We introduce two methods for quantization noise reduction in oversampled filter banks. These methods are based on predictive quantization (noise shaping or linear prediction). It is demonstrated that oversampled noise shaping or linear predictive subband coders are well suited for subband coding applications where, for technological or other reasons, low-resolution quantizers have to be used. In this case, oversampling combined with noise shaping or linear prediction improves the effective resolution of the subband coder at the expense of increased rate. Simulation results are provided to assess the achievable quantization noise reduction and resolution enhancement, and to investigate the rate-distortion properties of the proposed methods     

脉冲噪声下基于分数低阶矩的MQAM信号载频与符号率联合估计

针对传统MQAM信号载频与符号率估计算法在脉冲噪声下性能不佳甚至失效的问题,该文结合分数低阶矩与共变理论,推导了MQAM的分数低阶循环谱密度函数,并分析了升余弦脉冲成型条件下的MQAM信号循环谱特征。将分数低阶矩引入离散频域平滑谱(DFSM)估计。提出了一种基于分数低阶矩的MQAM载波频率与符号率联合参数估计算法,采用相邻谱切面求平均的方法,有效的避免了由于分辨率不足造成估计误差。仿真实验结果表明,与二阶DFSM循环谱估计算法相比,本文算法不仅能够抑制脉冲噪声的影响,而且在较为恶劣的噪声条件下均能有效的估计MQAM信号的载频与符号率,具有更好的抗干扰能力与适用性,适用于不同调制阶数的MQAM信号。      

S模式交叠信号的PA分离算法性能分析

为了提升高密度信号背景下对S模式应答的接收能力,投影算法(PA)在阵列接收的基础上采用投影技术分离S模式交叠信号,因其具有良好的稳定性和实用性而成为经典的信号分离方法。首先,分析了PA算法针对的问题在各种空中流量密度下的发生概率;其次,在PA算法的基础上推导了交叠信源数检测与信号到达方向的关系,进一步分析了信号到达角(DOA)与噪声增益之间的关系,并说明了信号DOA对分离后的信号解码的影响;最后,以均匀线阵(ULA)为例仿真说明了信号DOA对信源数检测、噪声增益的影响。结果表明,对于两个S模式交叠的情况,PA算法在六阵元ULA下可以达到80%以上的检测成功率,并有较好的分离效果。     

Microwave imaging of aircraft

Three methods of imaging aircraft from the ground with microwave radar with quality suitable for aircraft target recognition are described. The imaging methods are based on a self-calibration procedure called adaptive beamforming that compensates for the severe geometric distortion inherent in any imaging system that is large enough to achieve the high angular resolution necessary for two-dimensional target imaging. The signal processing algorithm is described and X-band (3-cm)-wavelength experiments demonstrate its success on commercial aircraft flying into Philadelphia International Airport.<>     

Robust and efficient recovery of a signal passed through a filterand then contaminated by non-Gaussian noise

Consider a channel where a continuous periodic input signal is passed through a linear filter and then is contaminated by an additive noise. The problem is to recover this signal when we observe n repeated realizations of the output signal. Adaptive efficient procedures, that are asymptotically minimax over all possible procedures, are known for channels with Gaussian noise and no filter (the case of direct observation). Efficient procedures, based on the smoothness of a recovered signal, are known for the case of Gaussian noise. Robust rate-optimal procedures are known as well. However, there are no results on robust and efficient data-driven procedures; moreover, the known results for the case of direct observation indicate that even a small deviation from Gaussian noise may lead to a drastic change. We show that for the considered case of indirect data and a particular class of so-called supersmooth filters there exists a procedure of recovery of an input signal that possesses the desired properties; namely, it is: adaptive to the smoothness of the input signal; robust to the distribution of the noise; globally and pointwise-efficient, that is, its minimax global and pointwise risks converge with the best constant and rate over all possible estimators as n→∞; and universal in the sense that for a wide class of linear (not necessarily bounded) operators the efficient estimator is a plug-in one. Furthermore, we explain how to employ the obtained asymptotic results for the practically important case of small n (large noise)     

利用过采样技术提高ADC测量分辨率

针对目前单片机内嵌ADC分辨率较低,而外接高分辨率ADC成本又较高的情况,提出了用“过采样”技术使在有用的测量频带内的信噪比得到改善,从而提高ADC测量的分辨率。并利用Matlab对其结论进行仿真,且在TMS320LF2407 DSP上予以实现,结果表明信噪比和测量分辨率明显提高。     

MIMO系统下随机接入的实现及吞吐量研究

随机接入技术作为LTE系统中的关键技术之一,接入成功率关系到传输时延,LTE系统下的随机接入所采用的传统冲突解决算法主要是基于网络层的重传机制。提出了一种MIMO下的随机接入实现方案,基站端可以利用多天线接收和先进的MIMO信号处理技术的结合,进而联合高层解决冲突,仿真结果表明了该方案可以显著地提高系统的吞吐量。     

宽带连续混沌波形信号的研究与设计

针对现代战场对信息信号低截获概率的要求,分析研究了宽带连续混沌信号的低截获性,并对失谐耦合型考必兹宽带混沌电路进行研究,并分析比较了不同耦合方式的Colpitts振荡器电路,同时对该混沌电路产生的连续混沌波形信号做出分析,最后仿真出了该混沌信号的模糊函数图。仿真结果显示该混沌信号基底分布均匀,近似于理想的"图钉型",具有良好的距离和速度分辨率的同时,也具有更强的杂波抑制能力。     

Wavelet thresholding techniques for power spectrum estimation

Estimation of the power spectrum S(f) of a stationary random process can be viewed as a nonparametric statistical estimation problem. We introduce a nonparametric approach based on a wavelet representation for the logarithm of the unknown S(f). This approach offers the ability to capture statistically significant components of ln S(f) at different resolution levels and guarantees nonnegativity of the spectrum estimator. The spectrum estimation problem is set up as a problem of inference on the wavelet coefficients of a signal corrupted by additive non-Gaussian noise. We propose a wavelet thresholding technique to solve this problem under specified noise/resolution tradeoffs and show that the wavelet coefficients of the additive noise may be treated as independent random variables. The thresholds are computed using a saddle-point approximation to the distribution of the noise coefficients     

Resolution limits in signal recovery

Resolution analysis for the problem of signal recovery from finitely many linear measurements is the subject of this paper. The classical Rayleigh limit serves only as a lower bound on resolution since it does not assume any recovery strategy and is based only on observed data. We show that details finer than the Rayleigh limit can be recovered by simple linear processing that incorporates prior information. We first define a measure of resolution based on allowable levels of error that is more appropriate for current signal recovery strategies than the Rayleigh definition. In the practical situation in which only finitely many noisy observations are available, we have to restrict the class of signals in order to make the resolution measure meaningful. We consider the set of bandlimited and essentially timelimited signals since it describes most signals encountered in practice. For this set, we show how to precompute resolution limits from knowledge of measurement functionals, signal-to-noise ratio, passband, energy concentration regions, energy concentration factor, and a prescribed level of error tolerance. In the process, we also derive an algorithm for high-resolution signal recovery. We illustrate the results with examples in one and two dimensions     

A Nonlinear Noise-Shaping Delta-Sigma Modulator with On-Chip Reinforcement Learning*

The stability and quality of noise shaping is a concern in the design of higher-order delta-sigma modulators for oversampled analog-to-digital conversion. We reformulate noise-shaping modulation alternatively as a nonlinear control problem, where the objective is to find the binary modulation sequence that minimizes signal swing in a cascade of integrators operating on the difference between the input signal and the modulation sequence. Reinforcement learning is used to adaptively optimize a nonlinear neural classifier, which outputs modulation bits from the values of the input signal and integration state variables. Analogous to the pole balancing control problem, a punishment signal triggers learning whenever any of the integrators saturate. Experimental results obtained from a VLSI modulator with integrated classifier, trained to produce stable noise shaping modulation of orders one and two, are presented. The classifier contains an array of 64 locally tuned, binary address-encoded neurons and is trained on-chip with a variant on reinforcement learning.