ADAPTIVE HARMONIC CANCELLATION APPLIED IN ELECTRO-HYDRAULIC SERVO SYSTEM WITH ANN

The method for harmonic cancellation based on artificial neural network (ANN)is proposed. The task is accomplished by generating reference signal with frequency that should beeliminated from the output. The reference input is weighted by the ANN in such a way that it closelymatches the harmonic. The weighted reference signal is added to the fundamental signal such thatthe output harmonic is cancelled leaving the desired signal alone. The weights of ANN are adjustedby output harmonic, which is isolated by a bandpass filter. The above concept is used as a basis forthe development of adaptive harmonic cancellation (AHC) algorithm. Simulation results performedwith a hydraulic system demonstrate the efficiency and validity of the proposed AHC control scheme.

Variable step-size affine projection algorithm based on global speech absence probability for adaptive feedback cancellation

A novel approach is proposed for improving adaptive feedback cancellation using a variable step-size affine projection algorithm(VSS-APA) based on global speech absence probability(GSAP).The variable step-size of the proposed VSS-APA is adjusted according to the GSAP of the current frame.The weight vector of the adaptive filter is updated by the probability of the speech absence.The performance measure of acoustic feedback cancellation is evaluated using normalized misalignment.Experimental results demonstrate that the proposed approach has better performance than the normalized least mean square(NLMS) and the constant step-size affine projection algorithms.

Adaptive digital self-interference cancellation based on fractional order LMS in LFMCW radar

Adaptive digital self-interference cancellation(ADSIC)is a significant method to suppress self-interference and improve the performance of the linear frequency modulated continuous wave(LFMCW)radar.Due to efficient implementation structure,the conventional method based on least mean square(LMS)is widely used,but its performance is not sufficient for LFMCW radar.To achieve a better self-interference cancellation(SIC)result and more optimal radar performance,we present an ADSIC method based on fractional order LMS(FOLMS),which utilizes the multi-path cancellation structure and adaptively updates the weight coefficients of the cancellation system.First,we derive the iterative expression of the weight coefficients by using the fractional order derivative and short-term memory principle.Then,to solve the problem that it is difficult to select the parameters of the proposed method due to the non-stationary characteristics of radar transmitted signals,we construct the performance evaluation model of LFMCW radar,and analyze the relationship between the mean square deviation and the parameters of FOLMS.Finally,the theoretical analysis and simulation results show that the proposed method has a better SIC performance than the conventional methods.

Multi-channel differencing adaptive noise cancellation with multi-kernel method

Although a various of existing techniques are able to improve the performance of detection of the weak interesting sig- nal, how to adaptively and efficiently attenuate the intricate noises especially in the case of no available reference noise signal is still the bottleneck to be overcome. According to the characteristics of sonar arrays, a multi-channel differencing method is presented to provide the prerequisite reference noise. However, the ingre- dient of obtained reference noise is too complicated to be used to effectively reduce the interference noise only using the clas- sical linear cancellation methods. Hence, a novel adaptive noise cancellation method based on the multi-kernel normalized least- mean-square algorithm consisting of weighted linear and Gaussian kernel functions is proposed, which allows to simultaneously con- sider the cancellation of linear and nonlinear components in the reference noise. The simulation results demonstrate that the out- put signal-to-noise ratio （SNR） of the novel multi-kernel adaptive filtering method outperforms the conventional linear normalized least-mean-square method and the mono-kernel normalized least- mean-square method using the realistic noise data measured in the lake experiment.

Adaptive Step Size Control of LMS-based Interference Cancellation for ICS Repeater in Wibro Environment

The use of repeater for the support of high rate data trans- mission and the extension of cell coverage is imperative for the Wibro system, which based on the IEEE 802.16e standardization. Generally, if the separation between transmitting and receiving antennas is not sufficient, the oscillation of repeater and the interference due to the feedback signals from original transmitted signal may be oectLrr. Hence, the Interference Cancellation System （ICS） should be implemented as the important part of the repeater system for the mobile cellular systems in order to eliminate unwanted signals from the corrupted signals in the receiver. In this paper, we propose an adaptive technique for the Least Mean Square（LMS）-based interference cancellation methods by changing the step size according to the variation of channel envirorauent in onter to improve the performance degradation which occta-rs by using the fixed step size approach. Simulation results show that the proposed scheme attains a little lower Ber Error Rate（BER） performance and much faster convergence speed compared to the conventional LMS-based interference cancellation techniques. The proposed scheme can be applied to other Orthogonal Frequency Division Multiple（OFDM）-based cellular systenas and also be expected to achieve a similar performance improvement to IMT-advanced system, which is called as the next generation mobile communication standards.

Adaptive cancellation of Es layer interference using auxiliary horizontal antennas

Based on a dual-polarization high-frequency wave radar system, an adaptive system using horizontal antennas for the suppression of the Es layer interference （ELI） is deseribech The data received from the horizontal antennas were correlated with the data received from the Vertically Polarized Antennas （VPAs） to estimate and cancel the interference adaptively in the VPAs. Suppressing the interference after each coherent integration time interval, about 25 dB signal-to-interference ratio is expected with the experimentally derived data.

Least mean square error difference minimum criterion for adaptive chaotic noise canceller

The least mean square error difference （LMS-ED） minimum criterion for an adaptive chaotic noise canceller is proposed in this paper. Different from traditional least mean square error minimum criterion in which the error is uncorrelated with the input vector, the proposed LMS-ED minimum criterion tries to minimize the correlation between the error difference and input vector difference. The novel adaptive LMS-ED algorithm is then derived to update the weights of adaptive noise canceller. A comparison between cancelling performances of adaptive least mean square （LMS）, normalized LMS （NLMS） and proposed LMS-ED algorithms is simulated by using three kinds of chaotic noises. The simulation results clearly show that the proposed algorithm outperforms the LMS and NLMS algorithms in achieving small values of steady-state excess mean square error. Moreover, the computational complexity of the proposed LMS-ED algorithm is the same as that of the standard LMS algorithms.

Hardware implementation of adaptive filter for noise cancellation using TMS320C6713

Daily, we experience the effects of audio noise, which contaminates the original information bearing signal with noise from its surrounding environment. This paper focuses on real-time hardware implementation of multi-tap adaptive noise cancellation （ANC） system by using the least mean square （LMS） algorithm on TMS320C6713 to remove undesired noise from a received signal for various audio related applications. Three different experiments are carried out by considering different audio inputs to test the efficiency of the designed ANC system. The ＇C＇ code implementation of LMS algorithm is introduced and simulated in code composer studio （CCS）, then realized on the digital signal processor （DSP） C6713. The 300 Hz, 500 Hz, 800 Hz, 1 kHz and 3 kHz of tone signals and male speech signal are used as the reference inputs to trace the noise of signal until it is eliminated. The performance of ANC system is studied in terms of convergence speed, order of the filter and signal-to-noise ratio （SNR）. The experimentam results demonstrate that the designed system shows a consider- able improvement in SNR.

Frequency-Domain Wideband Acoustic Noise Cancellation System

When building an adaptive noise cancellation system for wideband acoustic signals, one can meet some difficulties in practical implementation of such a system. The major problem is related to the necessity of using real-time signal generation and processing. In this paper the active noise control system which utilizes adaptation in frequency domain is considered. It is shown that the proposed algorithms simplify practical implementation of a noise cancellation system. The results of computer simulations and prototype experiments show the effectiveness of the proposed methods. .

An anti-main-lobe jamming algorithm for airborne early warning radar based on APC-SVRGD joint optimization

Main lobe jamming seriously affects the detection performance of airborne early warning radar.The joint processing of polarization-space has become an effective way to suppress the main lobe jamming.To avoid the main beam distortion and wave crest migration caused by the main lobe jamming in adaptive beamforming,a joint optimization algorithm based on adaptive polarization canceller(APC)and stochastic variance reduction gradient descent(SVRGD)is proposed.First,the polarization plane array structure and receiving signal model based on primary and auxiliary array cancellation are established,and an APC iterative algorithm model is constructed to calculate the optimal weight vector of the auxiliary channel.Second,based on the stochastic gradient descent principle,the variance reduction method is introduced to modify the gradient through internal and external iteration to reduce the variance of the stochastic gradient estimation,the airspace optimal weight vector is calculated and the equivalent weight vector is introduced to measure the beamforming effect.Third,by setting up a planar polarization array simulation scene,the performance of the algorithm against the interference of the main lobe and the side lobe is analyzed,and the effectiveness of the algorithm is verified under the condition of short snapshot number and certain signal to interference plus noise ratio.

Nonparametric VSS-APA based on precise background noise power estimate

The adaptive algorithm used for echo cancellation(EC) system needs to provide 1) low misadjustment and 2) high convergence rate. The affine projection algorithm(APA) is a better alternative than normalized least mean square(NLMS) algorithm in EC applications where the input signal is highly correlated. Since the APA with a constant step-size has to make compromise between the performance criteria 1) and 2), a variable step-size APA(VSS-APA) provides a more reliable solution. A nonparametric VSS-APA(NPVSS-APA) is proposed by recovering the background noise within the error signal instead of cancelling the a posteriori errors. The most problematic term of its variable step-size formula is the value of background noise power(BNP). The power difference between the desired signal and output signal, which equals the power of error signal statistically, has been considered the BNP estimate in a rough manner. Considering that the error signal consists of background noise and misalignment noise, a precise BNP estimate is achieved by multiplying the rough estimate with a corrective factor. After the analysis on the power ratio of misalignment noise to background noise of APA, the corrective factor is formulated depending on the projection order and the latest value of variable step-size. The new algorithm which does not require any a priori knowledge of EC environment has the advantage of easier controllability in practical application. The simulation results in the EC context indicate the accuracy of the proposed BNP estimate and the more effective behavior of the proposed algorithm compared with other versions of APA class.

Microwave and IF Cancellation of Leakage Signal for Spread Spectrum Radar

This paper discusses the requirements for the cancellation of leakage signal, analyzes in detail the principals of cancelling leakage signal in microwave and IF band, and proposes the schemes to realize adaptive cancellation. Computer analog has shown that the proposed scheme has a very high convergence speed and can reach a high cancellation ratio.

A method of multi-channel reference signals acquiring in broadband ANC

In a flank array on an unmanned underwater vehicle （UUV）, self-generated noise which has broadband and colored spectrum property in frequency and spatial domain is the main factor affecting the performance of weak signal detection, so the technique of adaptive noise cancellation （ANC） as well as physical denoising and active noise cancellation are often used in practice. Because ANC is based on correlations, improvements in performance come from better correlation between reference signals and primary signals. Taking full advantage of the characteristics of flank arrays and the characteristics of information obtained from hydrophones, a new method for reference signal acquisition for adaptive noise cancellation is proposed, in which the multi-channel reference signals are obtained by accurate delaying for a given direction of arrival （DOA） and differencing between adjacent outputs of array elements. The validity of the proposed method was verified through system modeling simulations and lake experiments which showed good performance with little additional computational burden.

Real-time adaptive noise cancelling for signal-to-noise enhancement in direct absorption spectroscopy

An adaptive filter for cancelling noise contained in the direct absorption spectra is reported. This technique takes advantage of the periodical nature of the repetitively scanned spectral signal, and requires no prior knowledge of the detailed properties of noises. An experimental system devised for measuring CH4 is used to test the performance of the filter. The measurement results show that the signal-to-noise （S/N） value is improved by a factor of 2. A higher enhancement factor of the S/N value of 5.4 is obtained through open-air measurement owing to higher distortions of the raw data. In addition, the response time of this filter, which characterizes the real-time detection ability of the system, is nine times shorter than that of a conventional signal averaging solution, under the condition that the filter order is 100.

Lung sounds auscultation technology based on ANC - ICA algorithm in high bat- tlefield noise environment

AIM:To explore the more accurate lung sounds auscultation technology in high battlefield noise environment.METHODS: In this study, we restrain high background noise using a new method-adaptive noise canceling based on independent component analysis (ANC-ICA), the method, by incorporating both second-order and higher-order statistics can remove noise components of the primary input signal based on statistical independence.RESULTS:The algorithm retained the local feature of lung sounds while eliminating high background noise, and performed more effectively than the conventional LMS algorithm.CONCLUSION:This method can cancel high battlefield noise of lung sounds effectively thus can help diagnose lung disease more accurately.

A postbeamformer adaptive noise cancelling system

In this paper was discussed a broadband PIC adaptive system for the towed-line array, and was presented a multichannel sequence least squares lattice (MSLSL) algorithm, which is appropriate for a broadband adaptive canceller of multi-interference beams. Applying the multi-stage MSLSL algorithm to the towed-line array sea trial data, satisfactory results are obtained.

A Humanoid Method for Extracting Abnormal Engine Sounds from Engine Acoustics Based on Adaptive Volterra Filter

The improvement of SNR （Signal-to-Noise Ratio） of abnormal engine sounds is of great help in improving the accuracy of engine fault diagnosis. By imitating the way that human technicians use to distinguish abnormal engine sounds from engine acoustics, a humanoid abnormal sound extracting method is proposed. By implementing adaptive Volterra filter in the canonical Adaptive Noise Cancellation （ANC） system, the proposed method is capable of tracing the engine baseline sound which exhibits an intrinsic nonlinear dynamics. Besides, by introducing a template noise tailored from the records of engine baseline sound and taking it as virtual input of the adaptive Volterra filter, the priori knowledge of engine baseline sound, such as inherent correlation, periodicity or phase information, and stochastic factors, is taken into consideration. The hybrid simulations prove that the proposed method is functional. Since the method proposed is essentially a single-sensor based ANC, hopefully, it may become an effective way to extricate the dilemma that canonical dual-sensor based ANC encounters when it is used in extracting fault-featured signals from observed signals.

Ultrasonic detection of interface in multilayers: Theory and adaptive noise canceling

The analytic expression of the received echo in multilayers NDT (Non-Destruction Evaluation) is derived. Then based on the analytic solution, interface signals are analyzed; and it is concluded that the received ultrasonic echo of mulilayers is composed of all the interface signals. By using the adaptive canceling of the signals from interfaces 0 and 1, the signals from interface 2 can be extracted. The method is applied to simulated and real echoes of multilayers,and the signals from interface 2 is separated successfully Based on the amplitude and arrival time of the signal from interface 2, the bond quality and depth of the interface can be evaluated

Fast Affine Projection Algorithm for Adaptive Noise Canceling and Its Application on the Fetal Electrocardiogram Extraction

Aimed at the problem of adaptive noise canceling(ANC),three implementary algorithms which are least mean square(LMS) algorithm,recursive least square(RLS) algorithm and fast affine projection(FAP) algorithm,have been researched.The simulations were made for the performance of these algorithms.The extraction of fetal electrocardiogram(FECG) is applied to compare the application effect of the above algorithms.The proposed FAP algorithm has obvious advantages in computational complexity,convergence speed and steadystate error.

Adaptive feedback cancellation based on variable step-size affine projection for hearing aids

A new variable step-size （VSS） affine projection algorithm （APA） （VSS-APA） was proposed for adaptive feedback cancellation suitable for hearing aids. So, a nonlinear function between step-size and estimation error is established and automatically adjusted according to the change of the estimation error, which leads to low misalignment and fast convergence speed. Analysis of the proposed algorithm offers large capacities in converging to the objective system. Simulation shows that the proposed algorithm achieves lower misalignment and faster convergence speed compared to fixed step-size APA and conventional adaptive algorithms.