Wavelet Notch Filter Design of Spread-Spectrum Communication Systems for High-Precision Wireless Positioning

Spread-spectrum communication systems are now commonly used in the field of cellular telephone positioning. However, wireless positioning systems by low-power spread-spectrum communication are extremely vulnerable to high-power interference, which limits achievable measurement precision. In this paper, a bandwidth variable wavelet notch filter design method is proposed to suppress a large number of jammers in multiple locations with noise interfering with spread-spectrum systems. The filter uses combinations of Gaussian wavelets with optimal time-frequency localization and computational efficiency for real-time operation of denoising. The performance of the adaptive filter has been evaluated by experiments associated with a spread-spectrum communication system model employing a reliable noise detector to locate the filter notch. Experimental results demonstrate that the proposed wavelet notch filter removes the narrow-band interference in accordance with the corrupted frequency contents while minimizing signal distortion and information loss, which leads to high-precision wireless positioning.     

不同频率位置的窄带干扰对DSSS通信性能的影响分析

衡量窄带干扰抑制算法性能高低的一个主要标准是评估干扰处于最恶劣频率位置时的干扰输出平均功率的大小。在扩频序列周期很长且严格平衡的理想条件下,最恶劣干扰的频率位置为DSSS信号载波频率。基于典型的m序列DSSS系统,分析了处在不同频率位置的窄带干扰对DSSS系统的影响,所得结果与理想条件有所不同,这对于扩频系统的设计与应用具有指导意义。     

基于子波变换的无线通信网络干扰信号检测研究

在冗杂环境下,为有效识别无线通信网络干扰信号,提出基于子波变换的无线通信网络干扰信号检测研究。将干扰信号分为单音、多音、调频等类型,利用射线模型描述通信信道损失,确立噪声影响下的干扰信号结构,提取信号时频分布特征;在子波变换过程引入阈值滤波算法,将噪声投影在不同子波空间中,合理设置阈值,计算新的变换系数,实现信号去噪;采用混沌循环谱方法,将干扰信号检测转换为二元假设检验问题,获取二阶时变检测函数,计算决策量,结合门限值完成干扰信号检测。实验结果表明,该方法能够有效过滤噪声,检测出的干扰信号波形与频率与实际情况相符。     

Frequency shift keyed narrowband interference rejection: optimal exponential weighting factor for the RLS algorithm

Cochannel narrowband interference can limit the performance of direct sequence spread spectrum (DSSS) and high frequency (HF) systems. Narrowband interference (NBI) can be single tone, chirped or frequency shift keyed (FSK) in nature and numerous techniques for its removal have been proposed. Linear adaptive prediction filters based on autoregressive modelling have been suggested owing to their ability to perform in a non-stationary environment. In the FSK narrowband interference case, adaptive filters are susceptible to excess residual errors owing to instantaneous frequency step changes and the finite convergence time required for the filter to adapt to a new interference frequency. The signal degradation owing to this type of interference becomes greater in high SNR regimes and has been found to be a function of the frequency parameters of the FSK interference signal. The paper discusses the convergence and frequency tracking properties of the recursive least squares (RLS) adaptive lattice filter using a posteriori estimation errors in the presence of FSK narrowband interference. An optimal exponential weighting factor that balances convergence time and steady state error is derived for this case of NBI. Results are compared to those of a previously proposed fast converging minimum frequency error (FCMFE) RLS lattice filter.     

Performance of a fast frequency-hopped noncoherent MFSK receiverwith nonideal adaptive gain control

An error probability analysis is performed for an orthogonal noncoherent M-ary frequency-shift keying (MFSK) communication system employing fast frequency-hopped (FFH) spread spectrum with diversity. The signal is assumed to be transmitted through a frequency-nonselective slowly fading channel with partial-band noise interference. The partial-band interference is modeled as a Gaussian process. Both the information signal and the partial-band noise interference signal are assumed to be affected by channel fading; it is assumed that the two fading processes are independent and that channel fading need not necessarily affect the information signal and the interference signal in the same way. Each diversity reception is assumed to fade independently according to a Rician process. Adaptive gain control is employed to minimize partial-band interference effects, and the effect of inaccurate noise measurement on the ability of the adaptive gain control receiver to reject partial-band interference is examined. The effect of thermal noise is included in the analysis     

Polarization filtering for narrowband interference suppression in ultra‐wideband communications

To suppress narrowband interference (NBI) in an ultra‐wideband (UWB) communications environment, a null phase‐shift polarization (NPSP) filter is proposed. The proposed NPSP filter is a combination of a linear polarization‐vector transformer (PVT), a conventional single notch polarization (SNP) filter, and an amplitude and phase compensator (APC). The NBI, which has polarized states different from those of the UWB, can be suppressed completely and the UWB signal can be recovered without distortion if the polarized states can be estimated exactly. Analytical and simulation results indicate that the signal‐to‐interference ratio (SIR) can be improved effectively after NPSP filtering. The proposed NPSP filter can be implemented in a time‐hopping spread spectrum (TH‐SS) or a direct‐sequence spread spectrum (DS‐SS) UWB system. Copyright © 2007 John Wiley & Sons, Ltd.     

基于自适应小波包变换的直接序列扩频通信窄带干扰抑制技术

本文提出一种基于小波包变换的直接序列扩频通信窄带（单、多音）干扰抑制技术。该技术通过小波包分解将信号和干扰分离,然后再对滤波处理后的分解结果进行小波包合成,以此达到窄带干扰去除的目的。文中对这种方案的性能进行了分析并给出了仿真结果。结果表明,与传统的方法相比,它具有较好的误比特率性能,较快的干扰定位速度和简单的计算复杂度。是宽带系统中行之有效的窄带干扰抑制方法,尤其适合于窄带干扰以近似于比特率的速度快速变化的场合。     

直扩通信中基于ATF算法的窄带干扰抑制技术优化

提出了一种直扩通信中基于ATF算法的窄带干扰抑制技术的优化方法.采用自适应滤波技术取代子带剔除法,自适应地跟踪窄带干扰,改善了有用信号的丢弃情况,有效地抑制了窄带干扰.仿真结果表明,该算法在性能上更优越,干扰定位速度更快,干扰抑制能力得到较大提高.     

直扩系统中IIR格型滤波器抑制窄带干扰新方法与性能分析

该文提出一种直扩系统中基于IIR格型陷波器时域频域相结合的窄带干扰抑制新方法,该方法采用时域频域并行处理的结构,通过最优加窗和频谱校正技术在频域精确估计出干扰信号的频率,根据频率估计误差和干信比求出最优陷波带宽,由此自适应地调整时域IIR格型陷波器的参数。分析和实验表明,该方法较传统的自适应IIR窄带干扰抑制方法不仅具有更高的稳定性和实时性,而且在干扰得到有效抑制的情况下使有用信号的损失达到最小。     

基于混合编程的高精度激光多普勒信号处理技术

针对激光多普勒信号中存在较大噪声干扰的实际情况,为了抑制这些噪声干扰,提高激光多普勒测速仪的测量精度,提出了对激光多普勒信号进行最小均方差(LMS)自适应滤波后作快速傅里叶变换(FFT),基于混合编程思想对所得到的频谱,先进行频谱细化,再进行频谱校正的信号处理方法,并对理想正弦信号和实测多普勒信号分别进行仿真计算和实验研究。仿真和实验结果表明：LMS自适应滤波技术可以有效抑制激光多普勒测量中的多频率噪声的干扰,此技术能够适应于很宽的信噪比范围,大大提高多普勒信号的信噪比;频谱细化技术可以提高激光多普勒信号的频谱分辨率,频谱校正技术可以准确地校正多普勒频率,使校正后的频率更加接近于真实值;信号处理精度比直接进行FFT提高2~3倍。     

一种改进的基于ATF算法的直扩通信干扰抑制技术

提出了直扩通信中一种改进的基于ATF（自适应时频）算法的干扰抑制方法。对原有算法进行了优化并给出了相应的自适应去干扰算法。采用自适应滤波技术取代子带剔除法,自适应地跟踪窄带干扰,改善了有用信号的丢弃情况,有效地抑制了窄带干扰。分析和仿真结果表明,基于ATF算法的自适应小波包时频干扰抑制方法比传统的子带剔除法在性能上更为优越,干扰定位速度也较快,干扰抑制能力强。     

多径分量噪声干扰下无线电扩频通信优化技术

无线电扩频通信受到多径分量噪声的干扰,导致通信信道均衡性不好,抗干扰能力不强,误码率较高。需要进行噪声干扰抑制,改善无线电扩频通信质量。提出一种基于自适应级联陷波和码间干扰抑制的无线电扩频通信优化技术。构建无线电扩频通信系统传输结构模型,采用自适应陷波器对无线电扩频通信中的多径分量噪声进行有效抑制,降低了进入信号通频带内的干扰功率,提高解调器的输出信噪比和信干比。采用扩展后的序列去调制载波,将信号搬移到载频上,实现信道均衡,提高抗干扰能力。仿真结果表明,该算法能有效降低无线电扩频通信的误比特率,抗干扰性较强,提高通信质量。     

PSO-based learning of sub-band adaptive thresholding function for image denoising

In this paper, a new computationally efficient approach has been proposed for denoising the images which are corrupted by Gaussian noise. In this approach, relatively recent category of stochastic global optimization technique i.e., particle swarm optimization (PSO) technique have been proposed for learning the parameters of adaptive thresholding function required for optimum performance. The proposed PSO-based denoising approach not only speeds up the optimization but also improves the performance in comparison with wavelet transform-based thresholding neural network (WT-TNN) approach. The results obtained shows better edge preservation performance with bior6.8 wavelet filter when compared to db8 wavelet filter. Further, problem of dependency of learning time on initial value of thresholding parameters and noise level in the image have been sorted out in the proposed approach.     

Outage Analysis of OFDM‐Based Cognitive AF Relay Network in the Presence of Narrowband Interference

Orthogonal frequency‐division multiplexing (OFDM) is one of the most widely used technologies in current wireless communication systems and standards. Cognitive radio (CR) provides a robust solution to the problem of spectrum congestion as it offers opportunistic usage of frequency bands that are not occupied by primary users. Due to the underlying sensing, spectrum shaping, scaling, and interoperable capabilities of OFDM, it has been adapted as a best transmission technology for CR wireless systems. However, the performance of an OFDM‐based CR wireless system is affected by the existence of narrowband interference (NBI) from other users. Further, due to carrier frequency offset in NBI sources, NBI energy may spread over all subcarriers of an OFDM signal. In this paper, a fixed Amplify‐and‐Forward (AF) relay that operates at a frequency band that is different from that of direct mode is introduced to suppress the effect of NBI. Analytical expressions are derived for outage probability in direct, AF‐relay, and incremental relaying modes. The outage performance of the proposed AF relay–based CR network is proven to be better than that of direct mode.     

Least mean p-power error criterion for adaptive FIR filter

An adaptive FIR filter based on the least mean p-power error (MPE) criterion is investigated. First, some useful properties of MPE function are studied. Three main results are as follows: 1) MPE function is a convex function of filter coefficients; so it has no local minima. 2) When input process and desired process are both Gaussian processes, then MPE function has the same optimum solution as the conventional Wiener solution for any p. 3) When input process and desired process are non-Gaussian processes, then MPE function may have better optimum solution than Wiener solution. Next, a least mean p-power (LMP) error adaptive algorithm is derived and some application examples are presented. Consequently, when the signal is corrupted by an impulsive noise, the adaptive algorithm with p=1 is preferred. Furthermore, when the signal is corrupted by noise or interference, the adaptive algorithm with proper choice of p may be preferred     

Quickest physical‐layer MGD anomaly detection for jamming attacks in centralized modulated wideband converter‐based ROC curve

In wireless communication systems, physical‐layer security menaces have evolved from jammers. Jammers, due to their furtive nature, make wireless communication systems vulnerable. The novelty in this work is to combine centralized modulated wideband converter, which is a networking system developed from the modulated wideband converter–based sub‐Nyquist sampling theory with a multivariate Gaussian distribution (MGD) anomaly detector‐based receiver operating characteristic curve that plot the detection rate (DR) versus false alarm rate (FAR) at various threshold values. We supposed the presence of a group of jammers in the spectrum corrupted with the primary source signal and noise. The received primary signal at each cognitive radio (CR) receiver is converted in to a digital signal using an analog‐to‐information converter. Each CR receiver give minimum number of samples denoted N₁. All these compressed samples from every CR receiver are collected in the form of matrix called compressed sampling matrix, which is considered directly as the input of the MGD detector. The intelligent MGD detector proposed in the level of fusion center is based on the characteristics of the MGD. The numerical results show that this new system of combination detects faster anomalies perfectly in the presence of jammers in the spectrum in real‐time scenarios. Performance evaluation is performed in terms of DR versus FAR at different detection threshold values, under the presence of attacks in the system. By employing well‐known machine learning algorithms called MGD, the performance of this new proposed system shows good.     

Bayesian Sparse Reconstruction Method of Compressed Sensing in the Presence of Impulsive Noise

The majority of existing recovery algorithms in the framework of compressed sensing are not robust to the impulsive noise. However, the impulsive noise is always present in the actual communication and signal processing system. In this paper, we propose a method named ‘Bayesian sparse reconstruction’ to recover the sparse signal from the measurement vector which is corrupted by the impulsive noise. The Bayesian sparse reconstruction method is composed of five parts, which are the preliminary detection of the location set of impulses, the impulsive noise fast relevance vector machine algorithm, the step of pruning, Bayesian impulse detection algorithm and the maximum a posteriori estimate of the sparse vector. The Bayesian sparse reconstruction method can achieve effective signal recovery in the presence of impulsive noise, depending on the mutual influence of the impulsive noise fast relevance vector machine algorithm, the step of pruning and the Bayesian impulse detection algorithm. Experimental results show that the Bayesian sparse reconstruction method is robust to the impulsive noise and effective in the additive white Gaussian noise environment.     

Hybrid ARQ schemes for DS spread spectrum systems in the presence of narrow‐band interference

This paper is concerned with a direct sequence spread spectrum system making use of automatic repeat request (ARQ) schemes and operating in the presence of narrow‐band (NB) interference and additive white Gaussian noise. In this study, we evaluate the system's throughput efficiency when a suppression filter is employed at the receiver to mitigate the effect of NB interference. Specifically, we investigate how the performance is influenced by various parameters such as the number of filter taps, processing gain, type of interference, and the signal power‐to‐interference power ratio. The simulation results show that using the suppression filter significantly improves the throughput performance of the hybrid ARQ schemes in the presence of NB interference. Copyright © 2002 John Wiley & Sons, Ltd.     

Analysis of Joint Multiband Sensing‐Time M‐QAM Signal Detection in Cognitive Radios

We analyze a wideband spectrum in a cognitive radio (CR) network by employing the optimal adaptive multiband sensing‐time joint detection framework. This framework detects a wideband M‐ary quadrature amplitude modulation (M‐QAM) primary signal over multiple nonoverlapping narrowband Gaussian channels, using the energy detection technique so as to maximize the throughput in CR networks while limiting interference with the primary network. The signal detection problem is formulated as an optimization problem to maximize the aggregate achievable secondary throughput capacity by jointly optimizing the sensing duration and individual detection thresholds under the overall interference imposed on the primary network. It is shown that the detection problems can be solved as convex optimization problems if certain practical constraints are applied. Simulation results show that the framework under consideration achieves much better performance for M‐QAM than for binary phase‐shift keying or any real modulation scheme.     

基于小波降噪的供水管道泄漏检测算法研究

背景噪声的降低是实现供水管道泄漏点准确定位的基础,也是提高定位精度的关键所在。本文在研究供水管道泄漏检测原理、泄漏点振动信号特征与其背景噪声特性的基础上,提出采用小波降噪和低通滤波相结合的方法来提高信噪比。在MATLB平台上利用sinc函数的叠加产生具有准确时延的2路模拟信号,并加入低信噪比的高斯噪声来模拟2路传感器的检测信号。采用广义互相关法求得延迟时间,仿真结果表明：小波降噪后的互相关运算结果十分逼近延迟时间的实际值,提高了检漏的精度。