Nonlinear Filtering Method Research Articles

Distributed Multiband Synthetic Aperture Radar Image Fusion Based on Wavelet Transform in the Internet of Things Environment

Abstract In order to improve the detection and recognition capabilities of distributed multiband synthetic aperture radar (SAR) images in the Internet of Things environment, a distributed multiband SAR image fusion algorithm based on wavelet transform is proposed for the Internet of Things environment. The multispectral/hyperspectral imager is used to detect and process the distributed multiband SAR image. The feature extraction method of fast spatial geographic water target range radar signal source is used to extract and segment the distributed multiband SAR image. The wavelet multiscale transform method is used to segment the SAR image, and the linear filtering and nonlinear filtering methods are used to detect the edge contour features. Using the distributed multiband SAR image fusion technology based on the calculation of high-frequency subband edge function and the segmentation of regional gray contour curve, the splitting and broadening of the peak spectrum of the target image of the radar signal source in the fast spatial geographical waters, as well as the radar target positioning parameters, the noise filtering, and anti-jamming detection of the distributed multiband SAR image are realized, and the distributed multiband SAR image fusion is realized combined with wavelet transform. The test results show that the output peak signal-to-noise ratio of distributed multiband SAR image fusion using this method is high, and the performance of detection and recognition of SAR imaging targets and the ability of edge contour feature extraction are good.

Development and Dynamic State Estimation for Robotic Knee–Ankle Orthosis With Shape Memory Alloy Actuators

Abstract The development of rehabilitation robots has long been an issue of increasing interest in a wide range of fields. An important aspect of the ongoing research field is applying flexible components to rehabilitation equipment to enhance human−machine interaction. Another major challenge is to accurately estimate the individual’s intention to achieve safe operation and efficient training. In this article, a robotic knee−ankle orthosis (KAO) with shape memory alloy (SMA) actuators is developed, and the estimation method is proposed to determine the joint torque. First, based on the analysis of human lower limb structure and walking patterns, the mechanical design of the KAO that can achieve various rehabilitation training modes is detailed. Next, the dynamic model of the hybrid-driven KAO is established using the thermodynamic constitutive equation and Lagrange formalism. In addition, the joint torque estimation is realized by the nonlinear Kalman filter method. Finally, the prototype and human subject experiments are conducted, and the experimental results demonstrate that the KAO can assist lower limb movements. In the three experimental scenarios, reductions of 59.1%, 16.5%, and 73% of the torque estimation error during the knee joint movement are observed, respectively.

State of charge estimation for Li-ion batteries based on iterative Kalman filter with adaptive maximum correntropy criterion

Nonlinear filter methods are commonly employed for model-driven based battery state of charge (SOC) estimation because of their ability to suppress Gaussian noise. Due to the uncertainty of complicated operational conditions, the minimum mean square error (MMSE) criterion based Kalman filter (KF) methods may not guarantee sufficient SOC estimation accuracy in the presence of non-Gaussian noise. To enhance the anti-interference performance of the KFs based SOC estimation method, a novel methodology that combines the adaptive kernel width based maximum correntropy criterion (AMCC) with the Levenberg-Marguardt (L-M) principle based adaptive iterative extended KF (AIEKF) is proposed. Firstly, the MMSE criterion of the EKF is replaced by the MCC criterion and an adaptive kernel width strategy is designed to weaken the effect of anomalous data. Then, the AMCC-corrected covariance matrix and state are updated using an L-M optimized multi-step iterative filter during the measurement update phase. Finally, the combination of AMCC and AIEKF is devised to execute SOC estimation. Several sets of data from different temperatures and operational conditions are used to verify the validity of the proposed AMCC-AIEKF method. Compared with the adaptive EKF (AEKF) and the adaptive iterative EKF (AIEKF) method, the AMCC-AIEKF method has the advantage in terms of SOC estimation accuracy and robustness under different operational conditions with noiseless, Gaussian noise and non-Gaussian noise.

IMAGE ENHANCEMENT AND DE-NOISING TECHNIQUES OF MAGNETIC RESONANCE IMAGES

Medical images have noise, such as salt and pepper, speckle, and gaussian noise. So, getting the accuracy of magnetic resonance images is challenging always. The accuracy of brain images is essential for the clinical diagnosis process. The nonlinear method of median and morphological filters is used for enhancement, contrast adjustment, and histogram equalization for medical images and speech processing, to preserve them from noise and edge features. Because of its edge-preserving properties, this median spatial filter window is critically important and better for removing impulsive noise and speckle noise. Nine different shapes of windows and five different sizes of windows and five noise density injections are used for evaluating the performance of the median filter. The first person J.W.Tukey, did the smoothing process by median rank selection filter. The denoising using morphological process is also considered for analysis. These two nonlinear methods like median filtering and morphological methods are used for the restoration of noise from the original image. These two methods are applied in normal and seizure-affected images to calculate quality assessment metrics and also the performance evaluation metrics from magnetic resonance images. Any image needs to be denoised to get the accuracy, precision, and F-measure kind of common feature evaluation metrics. This paper evaluates two nonlinear filtering methods and their performance evaluated based on shapes and size of windows and for suppressing or de-noising to produce original noise-free images to assist clinically.

Indirect and direct estimation of pharmacokinetic parameters in dynamic diffuse fluorescence tomography by adaptive extended Kalman filtering.

Pharmacokinetic parameter estimation with the support of dynamic diffuse fluorescence tomography (DFT) can provide helpful diagnostic information for tumor differentiation and monitoring. Adaptive extended Kalman filtering (AEKF) as a nonlinear filter method has the merits of high quantitativeness, noise robustness, and initialization independence. In this paper, indirect and direct AEKF schemes combining with a commonly used two-compartment model were studied to estimate the pharmacokinetic parameters based on our self-designed dynamic DFT system. To comprehensively compare the performances of both schemes, the selection of optimal noise covariance matrices affecting estimation results was first studied, then a series of numerical simulations with the metabolic time ranged from 4.16 min to 38 min was carried out and quantitatively evaluated. The comparison results show that the direct AEKF outperforms the indirect EKF in estimation accuracy at different metabolic velocity and demonstrates stronger stability at the large metabolic velocity. Furtherly, the in vivo experiment was conducted to achieve the indocyanine green pharmacokinetic-rate images in the mouse liver. The experimental results confirmed the capability of both schemes to estimate the pharmacokinetic-rate images and were in agreement with the theory predictions and the numerical simulation results.

A Nonlinear Data-Driven Towed Array Shape Estimation Method Using Passive Underwater Acoustic Data

Large-aperture towed linear hydrophone array has been widely used for beamforming-based signal enhancement in passive sonar systems; however, its performance can drastically decrease due to the array distortion caused by rapid tactical maneuvers of the towed platform, oceanic currents, hydrodynamic effects, etc. In this paper, an enhanced data-driven shape array estimation scheme is provided in the passive underwater acoustic data, and a novel nonlinear outlier-robust particle filter (ORPF) method is proposed to acquire enhanced estimates of time delays in the presence of distorted hydrophone array. A conventional beamforming technique based on a hypothetical array is first used, and the detection of the narrow-band components is sequentially carried out so that the corresponding amplitudes and phases at these narrow-band components can be acquired. We convert the towed array estimation problem into a nonlinear discrete-time filtering problem with the joint estimates of amplitudes and time-delay differences, and then propose the ORPF method to acquire enhanced estimates of the time delays by exploiting the underlying properties of slowly changing time-delay differences across sensors. The proposed scheme fully exploits directional radiated noise targets as sources of opportunity for online array shape estimation, and thus it requires neither the number nor direction of sources to be known in advance. Both simulations and real experimental data show the effectiveness of the proposed method.

Hyperspectral Image Classification Based on Bilateral Filter With Multispatial Domain

The bilateral filter (BF) is a nonlinear filtering method, which can remove noise and retain better edge information. It has been widely used in the field of hyperspectral images (HSIs) filtering. In this letter, we propose a novel spectral-spatial information integration method based on the BF with multispatial domain (MBF). The proposed method includes three steps. First, principal component analysis (PCA) is used for the original HSI to obtain multiple components containing almost all information; second, multiple principal components are used as both spatial domain and range domain information for BF; finally, the extreme learning machine (ELM) is used for classification. To verify the effectiveness of the proposed approach, we evaluate performance on three benchmark data sets. Our method will improve the existing filtering methods by constructing multiple spatial domains for filtering, which will make more effective use of spatial features and solve the problem of lack of spatial information in HSIs. This method is compared with other filtering algorithms. Comparative experiments show that our proposed method can improve the classification accuracy. And the MBF information is more effective than the BF with single spatial domain information and other filtering methods.

The Gaussian multiplicative approximation for state‐space models

Applications such as structural health monitoring (SHM) often rely on the analysis of time-series using methods such as state-space models (SSM). In this paper, we propose an analytical method called the Gaussian multiplicative approximation (GMA) that is applicable to multiplicative state-space models that are often encountered in practical SHM applications. The method enables the analytical inference of the mean vector and the covariance matrix for the product of two hidden states in the transition and/or observation models using linear estimation theory and the online estimation of model parameters as hidden states. The potential of combining the GMA and Bayesian dynamic linear models (BDLM) is illustrated through the development of (1) a generic component called online autoregressive that can estimate both the state variable and the parameter together; (2) a generic component called trend multiplicative for multiplicative seasonality model to identify non-harmonic periodic pattern whose amplitude changes linearly with time; and (3) a generic component called double kernel regression to identify non-harmonic periodic pattern that involves the product of two periodic kernel regression components. The SHM-based case studies presented confirm that the GMA exceeds the performance of the existing nonlinear Kalman filter methods in terms of accuracy along with the computational cost.

Application of Adaptive Robust CKF in SINS/GPS Initial Alignment with Large Azimuth Misalignment Angle

When the strapdown inertial navigation system does not perform coarse alignment, the misalignment angle is generally a large angle, and a nonlinear error model and a nonlinear filtering method are required. For large azimuth misalignment, the initial alignment technology with a large azimuth misalignment angle is researched in this paper. The initial alignment technology with a large azimuth misalignment angle is researched in this paper. First, the SINS/GPS nonlinear error model is established. Secondly, in the view of observation gross errors and inaccurate noise statistical characteristics, an adaptive robust CKF algorithm is proposed. Finally, according to the simulation analysis and experiment, the adaptive robust CKF algorithm can augment the stability and improve the filter estimation precision and convergence rate.

Research on Projection Filtering Method Based on Projection Symmetric Interval and Its Application in Underwater Navigation

For non-linear systems (NLSs), the state estimation problem is an essential and important problem. This paper deals with the nonlinear state estimation problems in nonlinear and non-Gaussian systems. Recently, the Bayesian filter designer based on the Bayesian principle has been widely applied to the state estimation problem in NLSs. However, we assume that the state estimation models are nonlinear and non-Gaussian, applying traditional, typical nonlinear filtering methods, and there is no precise result for the system state estimation problem. Therefore, the larger the estimation error, the lower the estimation accuracy. To perfect the imperfections, a projection filtering method (PFM) based on the Bayesian estimation approach is applied to estimate the state. First, this paper constructs its projection symmetric interval to select the basis function. Second, the prior probability density of NLSs can be projected into the basis function space, and the prior probability density solution can be solved by using the Fokker–Planck Equation (FPE). According to the Bayes formula, the proposed estimator utilizes the basis function in projected space to iteratively calculate the posterior probability density; thus, it avoids calculating the partial differential equation. By taking two illustrative examples, it is also compared with the traditional UKF and PF algorithm, and the numerical experiment results show the feasibility and effectiveness of the novel nonlinear state estimation filter algorithm.

Prevalence of shifted Rayleigh filter for passive surveillance in underwater

PurposeFrom many decades, bearings-only tracking (BOT) is the interested problem for researchers. This utilises nonlinear filtering methods for state estimation as there is only information about the target, i.e. bearing is a nonlinear measurement. The measurement bearing is tangentially related to the target state vector. There are many nonlinear filtering algorithms developed so far in the literature.Design/methodology/approachIn this research work, the recently developed nonlinear filtering algorithm, i.e. shifted Rayleigh filter (SRF), is applied to BOT.FindingsThe SRF is tested for two-dimensional BOT against various scenarios. The simulation results emphasise that the SRF performs well when compared to the standard nonlinear filtering algorithm, unscented Kalman filter (UKF).Originality/valueSRF utilises the nonlinearities present in the bearing measurement through the use of moment matching. The SRF is able to produce the solution in highly noisy environment, long ranges and high dimension tracking.

Synthesis of an Algorithm for Estimating Signal Distortions in a Direct Conversion Receiver Based on Combining a Regularizing Procedure and a Nonlinear Filtering Method

Synthesis of an Algorithm for Estimating Signal Distortions in a Direct Conversion Receiver Based on Combining a Regularizing Procedure and a Nonlinear Filtering Method

Perbandingan Metode Gaussian Filter dengan Median Filter dalam Mereduksi Noise Pada Citra Digital

Basically all humans want something perfect as well as in a digital image. One problem that often occurs in the digital image is noise or damage on the image. Noise is an error that occurs in the process of shooting so that unwanted signals exist. Improvement of digital images containing noise is an important thing in digital image processing. In designing the applications, the writer uses two methods i.e. Gaussian filter and Median Filter methods. Gaussian filter method is a linear filter with a weighted value for each pixel members is selected based on the shape of the members’ function. While the median filter method is one of the non-linear filter methods that arranges a bunch of pixel intensity values then replaces the pixel values being processed with a median value. From the test results of a digital image that has a noise, it is known that the result of the median filter method is better than Gaussian filter to reduce noise in digital images.Key words: Image Filtering, Method Gaussian Filter, Median Filter

Finite-time Dynamic Surface Fault-tolerant Control for Hypersonic Vehicle with Mismatched Disturbances

This paper studies the tracking control problem of hypersonic vehicle in the process of external disturbance, parameters uncertainties and actuator faults. Firstly, a non-triangular velocity and altitude nonlinear subsystem with mismatched disturbances and actuator faults are established based on the feedback linearization model. Secondly, adaptive dynamic surface fault-tolerant controllers are designed for velocity and altitude subsystems by combining nonlinear filter, adaptive control and back-stepping method. Finally, the Lyapunov stability theory and numercial simulation are carried out to verify the availability of the proposed control strategy.

Harmonic Inversion Based Posterirori Minimization in HPA for 5G Communication Framework

The Number of Android Mobile (Voice, Mobile Data, Internet) users getting increased year by year, But there is a major scarcity of operating frequency for the users. Even though many spectral efficient techniques like OFDMA and NOMA are introduced to manage the spectral scarcity. In 5G communication, various frameworks play a vital role in peer-to-peer communication and cloud-based network frameworks. It required errorless signal transmission to achieve the goal of a high-quality signal. The harmonic cancellation or reduction process is the critical task in the 5G communication application. Post error detection and correction is a highly complex process to reduce harmonic distortions. The real time interference problems due to harmonics still exist. With the introduction of 5G Mobile Communication there is an exponential increase of radiations from communication equipment (BTS, Mobiles) due to massive MIMO antennas, mixers, H-PA (High-Power-Amplifier). To reduce the above effects, a modified V-LMS linearizer has been introduced instead of the conventional non-linear filter methods (N-LMS, RLS). V-LMS not only estimates the gain error (priori) of the HPA, but also introduces very low additive error (posteriori). Look up table (LUT) is used to distribute the digital data to the arithmetic units of V-LMS. High speed operation can able to reduce the complexity of the overall system. Multilevel 2D-LUT can perform much faster in proposed V-LMS algorithm. Digital data process through LUT is used to perform accurate error estimation and it reduce the adaptive error in the proposed V-LMS. The proposed has been executed through the 64-QAM non-linear modulated signal with amplifier operating at 1.89–2.32 GHz.

A Big-data Inspired Precision Improvement Algorithm for Autonomous Navigation Based on Period Variable Stars

Periodic variable navigation can realize the integration of positioning, attitude determination, timing and other functions. As a novel autonomous navigation method, autonomous management and autonomous operation of spacecraft are the main direction of great significance to reduce the burden of ground measurement and control, to improve the viability of spacecraft, While, the precision of the navigation is very critical for the use of periodic variable navigation for the long range spacecraft. So, we propose a big-data inspired precision improvement algorithm in this paper. Period variable star phase time measurement is used as observation information, and accomplished by the orbital dynamics equation of spacecraft motion. According to the gathering of the sampling data and the sensor data, a self-learning system is trained with the parameters from the nonlinear filtering methods. Based on the Unscented Kalman Filtering, an autonomous navigation algorithm of period variable star is established to realize the navigation and positioning of spacecraft. Under the measurement conditions of a single sampling interval of 0.01s and the measurement precision of period variable star phase observation time of 10− 5s, It can be find that the navigation position determination precision can reach 400m, the speed determination precision can reach 0.3m/s, and the measurement precision can reach 10− 5s with our proposed algorithm.

Building Change Detection in Myanmar using Image Processing

Building change detection makes it is easy to locate buildings from a distance in the sky. They can also observe the development of rural, or urban areas between 10 decade and present. So, higher resolution satellite and aerial pictures are needed to detect buildings. Building shape varies from one to another over the world. Rural areas are sparsely populated, but densely and complexly populated in urban areas. And it is difficult to detect separate buildings from them. To solve obstacles, non-linear filter, line extracting and region thresholding method is used in this research. The test images from the last decade and images of current year are acquired by using google earth pro, and have different spatial resolutions. Detection area is Hlaingthaya Township, Yangon, Myanmar. This system is simulated with MATLAB programming language

Covariance matrix filtering with bootstrapped hierarchies.

Cleaning covariance matrices is a highly non-trivial problem, yet of central importance in the statistical inference of dependence between objects. We propose here a probabilistic hierarchical clustering method, named Bootstrapped Average Hierarchical Clustering (BAHC), that is particularly effective in the high-dimensional case, i.e., when there are more objects than features. When applied to DNA microarray, our method yields distinct hierarchical structures that cannot be accounted for by usual hierarchical clustering. We then use global minimum-variance risk management to test our method and find that BAHC leads to significantly smaller realized risk compared to state-of-the-art linear and nonlinear filtering methods in the high-dimensional case. Spectral decomposition shows that BAHC better captures the persistence of the dependence structure between asset price returns in the calibration and the test periods.

High order nonlinear filter methods for subsonic turbulence simulation with stochastic forcing

Numerical simulations of forced turbulence in compressible fluids are challenging due to the multi-scale nature of the problem and conflicting requirements for numerical methods to accurately resolve the small scales and, at the same time, to handle shock waves and other discontinuities without generating spurious oscillations. Minimizing nonlinear instability and aliasing error while maintaining high accuracy before the simulation reaches the statistically stationary stage is computationally intensive. The goal of this work is to employ an efficient class of high order finite difference nonlinear filter methods for subsonic turbulence simulation with stochastic forcing. The 3D Euler equations for subsonic turbulence with temporally varying stochastic forcing at rms Mach numbers up to 0.6 are numerically solved using the Strang operator splitting of the homogeneous part of the Euler equations and the forcing terms. It was shown in Yee et al. (2013) that the Strang operator splitting is more stable than solving the full Euler equation with the source term included. The spatially seventh-order nonlinear filter methods with adaptive dissipation control developed by Yee & Sjögreen (2007, 2011) are used to solve the homogeneous system and an ODE solver is used to solve the forcing source terms. The nonlinear filter method includes a full time step of a spatially eighth-order central base method, using a third-order TVD Runge-Kutta time integration. The solution computed with the central base method is then nonlinearly filtered by an adaptive flow sensor and the dissipative portion of a seventh-order WENO with the Roe Riemann solver. In order to improve nonlinear stability of the base method without added numerical dissipation, the central base method discretizes the skew-symmetric split form of the inviscid flux derivatives. Both Ducros et al. and Kennedy-Gruber skew-symmetric split forms are tested. Numerical stability, computational efficiency, and effective spectral bandwidth of the nonlinear filter schemes are compared with those of second-order TVD and fifth- and seventh-order WENO methods. It is shown that the nonlinear filter method for this application is substantially more efficient, accurate and yields a superior spectral bandwidth compared to the standard TVD and WENO methods. The nonlinear filter method also demonstrates robust long-time integration for moderately compressible, statistically stationary turbulence with large-scale solenoidal forcing, including small-scale quantities such as enstrophy and mean-square dilatation.

Target tracking methods based on a signal-to-noise ratio model

In traditional target tracking methods, the angle error and range error are often measured by the empirical value, while observation noise is a constant. In this paper, the angle error and range error are analyzed. They are influenced by the signal-to-noise ratio (SNR). Therefore, a model related to SNR has been established, in which the SNR information is applied for target tracking. Combined with an advanced nonlinear filter method, the extended Kalman filter method based on the SNR model (SNR-EKF) and the unscented Kalman filter method based on the SNR model (SNR-UKF) are proposed. There is little difference between the SNR-EKF and SNR-UKF methods in position precision, but the SNR-EKF method has advantages in computation time and the SNR-UKF method has advantages in velocity precision. Simulation results show that target tracking methods based on the SNR model can greatly improve the tracking performance compared with traditional tracking methods. The target tracking accuracy and convergence speed of the proposed methods have significant improvements.