A Novel Clutter Suppression Algorithm for Low-Slow-Small Targets Detecting Based on Sparse Adaptive Filtering

Passive detection of low-slow-small(LSS)targets is easily interfered by direct signal and multipath clutter,and the traditional clutter suppression method has the contradiction between step size and convergence rate.In this paper,a frequency domain clutter suppression algorithm based on sparse adaptive filtering is proposed.The pulse compression operation between the error signal and the input reference signal is added to the cost function as a sparsity constraint,and the criterion for filter weight updating is improved to obtain a purer echo signal.At the same time,the step size and penalty factor are brought into the adaptive iteration process,and the input data is used to drive the adaptive changes of parameters such as step size.The proposed algorithm has a small amount of calculation,which improves the robustness to parameters such as step size,reduces the weight error of the filter and has a good clutter suppression performance.

Vibration Suppression for Active Magnetic Bearings Using Adaptive Filter with Iterative Search Algorithm

Active Magnetic Bearing(AMB) is a kind of electromagnetic support that makes the rotor movement frictionless and can suppress rotor vibration by controlling the magnetic force. The most common approach to restrain the rotor vibration in AMBs is to adopt a notch filter or adaptive filter in the AMB controller. However, these methods cannot obtain the precise amplitude and phase of the compensation current. Thus, they are not so effective in terms of suppressing the vibrations of the fundamental and other harmonic orders over the whole speed range. To improve the vibration suppression performance of AMBs,an adaptive filter based on Least Mean Square(LMS) is applied to extract the vibration signals from the rotor displacement signal. An Iterative Search Algorithm(ISA) is proposed in this paper to obtain the corresponding relationship between the compensation current and vibration signals. The ISA is responsible for searching the compensating amplitude and shifting phase online for the LMS filter, enabling the AMB controller to generate the corresponding compensation force for vibration suppression. The results of ISA are recorded to suppress vibration using the Look-Up Table(LUT) in variable speed range. Comprehensive simulations and experimental validations are carried out in fixed and variable speed range, and the results demonstrate that by employing the ISA, vibrations of the fundamental and other harmonic orders are suppressed effectively.

A Method for Reducing Ocean Wave-Induced Magnetic Noises in Shallow-Water MT Data Using a Complex Adaptive Filter

In shallow-water areas,the marine magnetotelluric(MT)method faces a challenge in the investigation of seabed conductivity structures due to electrical and magnetic noises induced by ocean waves,which seriously contaminate MT data.Ocean waves can affect electric and magnetic fields to different extents.In general,their influence on magnetic fields is considerably greater than that on electric fields.In this paper,a complex adaptive filter is adopted to reduce wave-induced magnetic noises in the frequency domain.The processing results of synthetic and measured MT data indicate that the proposed method can effectively reduce wave-induced magnetic noises and provide reliable apparent resistivity and phase data.

Spline adaptive filtering algorithm based on different iterative gradients:Performance analysis and comparison

Two novel spline adaptive filtering(SAF)algorithms are proposed by combining different iterative gradient methods,i.e.,Adagrad and RMSProp,named SAF-Adagrad and SAF-RMSProp,in this paper.Detailed convergence performance and computational complexity analyses are carried out also.Furthermore,compared with existing SAF algorithms,the influence of step-size and noise types on SAF algorithms are explored for nonlinear system identification under artificial datasets.Numerical results show that the SAF-Adagrad and SAFRMSProp algorithms have better convergence performance than some existing SAF algorithms(i.e.,SAF-SGD,SAF-ARC-MMSGD,and SAF-LHC-MNAG).The analysis results of various measured real datasets also verify this conclusion.Overall,the effectiveness of SAF-Adagrad and SAF-RMSProp are confirmed for the accurate identification of nonlinear systems.

DOA Estimation Algorithm Based on Adaptive Filtering in Spatial Domain

In this paper, a novel DOA estimation methodology based upon the technology of adaptive nulling antenna is proposed. Initially, the nulling antenna obtains the weight vector by LMS algorithm and power inversion criterion.Afterwards, reciprocal of the antenna pattern is defined as the spatial spectrum and the extracted peak values are corresponded to the estimated DOA. Through observation of the spectrum and data analysis of variable steps and SNRs, the simulation results demonstrate that the proposed method can estimate DOA above board. Furthermore, the estimation error of the proposed technique is directly proportional to step size and is inversely proportional to SNR. Unlike the existing MUSIC algorithm, the proposed algorithm has less computational complexity as it eliminates the need of estimating the number of signals and the eigenvalue decomposition of covariance matrix. Also it outperforms MUSIC algorithm, the recently proposed MUSIC-Like algorithm and classical methods by achieving better resolution with narrow width of peaks.

A novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise,minimum mean square variance criterion and least mean square adaptive filter

Underwater acoustic signal processing is one of the research hotspots in underwater acoustics.Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing.Owing to the complexity of marine environment and the particularity of underwater acoustic channel,noise reduction of underwater acoustic signals has always been a difficult challenge in the field of underwater acoustic signal processing.In order to solve the dilemma,we proposed a novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),minimum mean square variance criterion(MMSVC) and least mean square adaptive filter(LMSAF).This noise reduction technique,named CEEMDAN-MMSVC-LMSAF,has three main advantages:(i) as an improved algorithm of empirical mode decomposition(EMD) and ensemble EMD(EEMD),CEEMDAN can better suppress mode mixing,and can avoid selecting the number of decomposition in variational mode decomposition(VMD);(ii) MMSVC can identify noisy intrinsic mode function(IMF),and can avoid selecting thresholds of different permutation entropies;(iii) for noise reduction of noisy IMFs,LMSAF overcomes the selection of deco mposition number and basis function for wavelet noise reduction.Firstly,CEEMDAN decomposes the original signal into IMFs,which can be divided into noisy IMFs and real IMFs.Then,MMSVC and LMSAF are used to detect identify noisy IMFs and remove noise components from noisy IMFs.Finally,both denoised noisy IMFs and real IMFs are reconstructed and the final denoised signal is obtained.Compared with other noise reduction techniques,the validity of CEEMDAN-MMSVC-LMSAF can be proved by the analysis of simulation signals and real underwater acoustic signals,which has the better noise reduction effect and has practical application value.CEEMDAN-MMSVC-LMSAF also provides a reliable basis for the detection,feature extraction,classification and recognition of underwater acoustic signals.

Using LMS Adaptive Filter in Direct Wave Cancellation

The way to use the least-mean-square (LMS) arithmetic to cancel the direct wave for a passive radar system is introduced. The model of the direct wave is deduced. By using the LMS adaptive FIR filter, the software solution for FM passive radar system is developed instead of the hardware consumption of the existent experiment system of passive radar. Further more some simulative results are given. The simulative results indicate that using LMS arithmetic to cancel the direct wave is effective.

Unsupervised robust adaptive filtering against impulsive noise

An implementation of adaptive filtering, composed of an unsupervised adaptive filter （UAF）, a multi-step forward linear predictor （FLP）, and an unsupervised multi-step adaptive predictor （UMAP）, is built for suppressing impulsive noise in unknown circumstances. This filtering scheme, called unsupervised robust adaptive filter （URAF）, possesses a switching structure, which ensures the robustness against impulsive noise. The FLP is used to detect the possible impulsive noise added to the signal, if the signal is ＂impulse-free＂, the filter UAF can estimate the clean sig- nal. If there exists impulsive noise, the impulse corrupted samples are replaced by predicted ones from the FLP, and then the UMAP estimates the clean signal. Both the simulation and experimental results show that the URAF has a better rate of convergence than the most recent universal filter, and is effective to restrict large disturbance like impulsive noise when the universal filter fails.

A robust subband adaptive filter algorithm for sparse and block-sparse systems identification

This paper presents a new subband adaptive filter(SAF)algorithm for system identification scenario under impulsive interference,named generalized continuous mixed p-norm SAF(GCMPN-SAF)algorithm.The proposed algorithm uses a GCMPN cost function to combat the impul-sive interference.To further accelerate the convergence rate in the sparse and the block-sparse system identification processes,the proportionate versions of the proposed algorithm,the L0-norm GCMPN-SAF(L0-GCMPN-SAF)and the block-sparse GCMPN-SAF(BSGCMPN-SAF)algorithms are also developed.Moreover,the convergence analysis of the proposed algorithm is provided.Simulation results show that the proposed algorithms have a better performance than some other state-of-the-art algorithms in the literature with respect to the convergence rate and the tracking capability.

Novel LMS adaptive filtering algorithm with variable step size

By analyzing algorithms available for variable step size least mean square(LMS)adaptive filter,a new modified LMS adaptive filtering algorithm with variable step size is proposed,along with performance analysis based on different parameters.Compared with the existing algorithms through the simulation,the proposed algorithm has faster convergence speed and smaller steady state error.

A NEW ADAPTIVE FILTERING SCHEME BASED ON WAVELET TRANSFORM

Based on the scale function representation for a function in L2(R), a new wavelet transform based adaptive system identification scheme is proposed. It can reduce the amount of computation by exploiting the decimation properties and keep the advantage of quasi-orthogonal transform of the discrete wavelet, transform (DWT). The issue has been supported by computer simulations.

Unsupervised FIR adaptive filtering and its frequency domain analysis

An unsupervised minimum mean square error FIR adaptive filtering （UAF） algorithm is proposed to estimate the system＇ s input signal. The algorithm only uses the system＇ s output signal and noise variance without requiring knowledge of a reference signal. The frequency analysis shows that the UAF is a multi-spot bandpass filter with passing frequency determined by the system＇ s input signal. Namely, the UAF chooses the expected frequency and extremely restricts the unwanted fre- quency signal by using weight-updating scheme in time domain. However, the UAF presents the Gibbs phenomenon since the ideal filter is infinitely long which is unrealizable. The simulation and experimental results show that the UAF could effectively reduce the amplitude of the noise and im- prove the signal to noise ratio.

Subband adaptive filter with variable reusing order of coefficient vectors

To increase the convergence rate of the improved normalized subband adaptive filter,a simple but effective method is presented to change the reusing order of coefficient vectors of the adaptive filter. At the beginning of adaptation the algorithmjust uses its current coefficient vector to update the adaptive filter to maintain fast convergence rate,while in steady state it employs several most recent coefficient vectors to update the adaptive filter to reduce misalignment. Simulation results showthat the proposed algorithmcan obtain both fast convergence rate and small steady-state misalignment.

Time-resolution enhanced multi-path OTD measurement using an adaptive filter based incoherent OFDR

High accuracy and time resolution optical transfer delay(OTD)measurement is highly desired in many multi-path applications,such as optical true-time-delay-based array systems and distributed optical sensors.However,the time resolution is usually limited by the frequency range of the probe signal in frequency-multiplexed OTD measurement techniques.Here,we proposed a time-resolution enhanced OTD measurement method based on incoherent optical frequency domain reflectometry(I-OFDR),where an adaptive filter is designed to suppress the spectral leakage from other paths to break the resolution limitation.A weighted least square(WLS)cost function is first established,and then an iteration approach is used to minimize the cost function.Finally,the appropriate filter parameter is obtained according to the convergence results.In a proof-of-concept experiment,the time-domain response of two optical links with a length difference of 900 ps is successfully estimated by applying a probe signal with a bandwidth of 400 MHz.The time resolution is improved by 2.78times compared to the theoretical resolution limit of the inverse discrete Fourier transform(iDFT)algorithm.In addition,the OTD measurement error is below±0.8 ps.The proposed algorithm provides a novel way to improve the measurement resolution without applying a probe signal with a large bandwidth,avoiding measurement errors induced by the dispersion effect.

Adaptive H_(∞)Filtering Algorithm for Train Positioning Based on Prior Combination Constraints

To solve the problem of data fusion for prior information such as track information and train status in train positioning,an adaptive H∞filtering algorithm with combination constraint is proposed,which fuses prior information with other sensor information in the form of constraints.Firstly,the train precise track constraint method of the train is proposed,and the plane position constraint and train motion state constraints are analysed.A model for combining prior information with constraints is established.Then an adaptive H∞filter with combination constraints is derived based on the adaptive adjustment method of the robustness factor.Finally,the positioning effect of the proposed algorithm is simulated and analysed under the conditions of a straight track and a curved track.The results show that the positioning accuracy of the algorithm with constrained filtering is significantly better than that of the algorithm without constrained filtering and that the algorithm with constrained filtering can achieve better performance when combined with track and condition information,which can significantly reduce the train positioning error.The effectiveness of the proposed algorithm is verified.

Image Enhancement Using Adaptive Fractional Order Filter

Image enhancement is an important preprocessing task as the contrast is low in most of the medical images,Therefore,enhancement becomes the mandatory process before actual image processing should start.This research article proposes an enhancement of the model-based differential operator for the images in general and Echocardiographic images,the proposed operators are based on Grunwald-Letnikov(G-L),Riemann-Liouville(R-L)and Caputo(Li&Xie),which are the definitions of fractional order calculus.In this fractional-order,differentiation is well focused on the enhancement of echocardiographic images.This provoked for developing a non-linear filter mask for image enhancement.The designed filter is simple and effective in terms of improving the contrast of the input low contrast images and preserving the textural features,particularly in smooth areas.The novelty of the proposed method involves a procedure of partitioning the image into homogenous regions,details,and edges.Thereafter,a fractional differential mask is appropriately chosen adaptively for enhancing the partitioned pixels present in the image.It is also incorporated into the Hessian matrix with is a second-order derivative for every pixel and the parameters such as average gradient and entropy are used for qualitative analysis.The wide range of existing state-of-the-art techniques such as fixed order fractional differential filter for enhancement,histogram equalization,integer-order differential methods have been used.The proposed algorithm resulted in the enhancement of the input images with an increased value of average gradient as well as entropy in comparison to the previous methods.The values obtained are very close(almost equal to 99.9%)to the original values of the average gradient and entropy of the images.The results of the simulation validate the effectiveness of the proposed algorithm.

Research on High Speed Communication Method of ELF-EM While Drilling Based on Adaptive Combined Filtering Algorithm

In terms of the electromagnetic wave measurement while drilling,the extremely low frequency electromagnetic(ELF-EM)signal below 20Hz is usually chosen as the downhole data carrier.To improve the transmission rate of ELF-EM signal and the signal to noise ratio(SNR)at the receiving end,the DQPSK modulation was proposed as the modulation method for the communication of electromagnetic wave system.Different from the traditional IQ orthogonal modulation and coherent demodulation methods,the proposed phase selection modulation and correlation algorithm demodulation are easier to implement and more practical.With regard to the communication synchronization,a fast algorithm,which based on the normalized cross-relation number,was used for waveform matching,and the maximum point of the correlation coefficient was used as the starting point of communication synchronization.The communication simulation results show that the proposed DQPSK modulation signal based on the adaptive combined filtering algorithm has better terminal error rate and transmission rate than the traditional modulation method.Under the same carrier frequency and code width,the transmission rate of DQPSK modulation is 4 to 5 times and 2 times that of PPM modulation and 2DPSK modulation respectively.The communication modulation and demodulation modes as well as the decoding algorithm with combined adaptive filter proposed in this paper can effectively solve practical engineering problems.

Improved Adaptive Iterated Extended Kalman Filter for GNSS/INS/UWB-Integrated Fixed-Point Positioning

To provide stable and accurate position information of control points in a complex coastal environment,an adaptive iterated extended Kalman filter(AIEKF)for fixed-point positioning integrating global navigation satellite system,inertial navigation system,and ultra wide band(UWB)is proposed.In thismethod,the switched global navigation satellite system(GNSS)and UWB measurement are used as the measurement of the proposed filter.For the data fusion filter,the expectation-maximization(EM)based IEKF is used as the forward filter,then,the Rauch-Tung-Striebel smoother for IEKF filter’s result smoothing.Tests illustrate that the proposed AIEKF is able to provide an accurate estimation.

Adaptive Linear Filtering Design with Minimum Symbol Error Probability Criterion

Adaptive digital filtering has traditionally been developed based on the minimum mean square error （MMSE） criterion and has found ever-increasing applications in communications. This paper presents an alternative adaptive filtering design based on the minimum symbol error rate （MSER） criterion for communication applications. It is shown that the MSER filtering is smarter, as it exploits the non-Gaussian distribution of filter output effectively. Consequently, it provides significant performance gain in terms of smaller symbol error over the MMSE approach. Adopting Parzen window or kernel density estimation for a probability density function, a block-data gradient adaptive MSER algorithm is derived. A stochastic gradient adaptive MSER algorithm, referred to as the least symbol error rate, is further developed for sample-by-sample adaptive implementation of the MSER filtering. Two applications, involving single-user channel equalization and beamforming assisted receiver, are included to demonstrate the effectiveness and generality of the proposed adaptive MSER filtering approach.

Adaptive Federal Kalman Filtering for SINS/GPS Integrated System

A new adaptive federal Kalman filter for a strapdown integrated navigation system/global positioning system (SINS/GPS) is given. The developed federal Kalman filter is based on the trace operation of parameters estimation's error covariance matrix and the spectral radius of update measurement noise variance-covariance matrix for the proper choice of the filter weight and hence the filter gain factors. Theoretical analysis and results from simulation in which the SINS/GPS was compared to conventional Kalman filter are presented. Results show that the algorithm of this adaptive federal Kalman filter is simpler than that of the conventional one. Furthermore, it outperforms the conventional Kalman filter when the system is undertaken measurement malfunctions because of its possession of adaptive ability. This filter can be used in the vehicle integrated navigation system.