Traditional Filtering Algorithm Research Articles

Toward Accurate Position Estimation Using Learning to Prediction Algorithm in Indoor Navigation.

Internet of Things is advancing, and the augmented role of smart navigation in automating processes is at its vanguard. Smart navigation and location tracking systems are finding increasing use in the area of the mission-critical indoor scenario, logistics, medicine, and security. A demanding emerging area is an Indoor Localization due to the increased fascination towards location-based services. Numerous inertial assessments unit-based indoor localization mechanisms have been suggested in this regard. However, these methods have many shortcomings pertaining to accuracy and consistency. In this study, we propose a novel position estimation system based on learning to the prediction model to address the above challenges. The designed system consists of two modules; learning to prediction module and position estimation using sensor fusion in an indoor environment. The prediction algorithm is attached to the learning module. Moreover, the learning module continuously controls, observes, and enhances the efficiency of the prediction algorithm by evaluating the output and taking into account the exogenous factors that may have an impact on its outcome. On top of that, we reckon a situation where the prediction algorithm can be applied to anticipate the accurate gyroscope and accelerometer reading from the noisy sensor readings. In the designed system, we consider a scenario where the learning module, based on Artificial Neural Network, and Kalman filter are used as a prediction algorithm to predict the actual accelerometer and gyroscope reading from the noisy sensor reading. Moreover, to acquire data, we use the next-generation inertial measurement unit, which contains a 3-axis accelerometer and gyroscope data. Finally, for the performance and accuracy of the proposed system, we carried out numbers of experiments, and we observed that the proposed Kalman filter with learning module performed better than the traditional Kalman filter algorithm in terms of root mean square error metric.

Adaptive Morphological Contrast Enhancement Based on Quantum Genetic Algorithm for Point Target Detection

Robust point target detection of infrared clutter background has drawn great interest of scholars. Recently, morphological filter is playing a significant role in detecting infrared point target. Generally, the background clutter and targets are diverse in the case of each image. Traditional fixed structural elements and dimensions cannot be adjusted adaptively to acquire to successful point target detection in different complex backgrounds. Therefore, a new method is introduced based on quantum genetic algorithm to optimize and obtain structural element which is used as morphological filter for small target detection in original infrared images.Then,morphological contrast enhancement is further proposed to enhance energy of point targets after the filtered image is obtained.Thus, an enormous background clutter and noise are suppressed and the contrast between target and background are observably increased. Finally, by setting proper threshold, the point targets can be detected perfectly. Experimental evaluation results show that the proposed adaptive morphological contrast enhancement based on quantum genetic algorithm is effective and robust with respect to detection accuracy compared with the traditional morphological filter and other filtering algorithms.

Adaptive Kernel Correlation Filter Tracking Algorithm in Complex Scenes

The traditional kernel correlation filter (KCF) algorithm has poor tracking results in complex scenes with severe occlusion, deformation, and low resolution and cannot achieve long-term tracking. To improve the accuracy of the tracking algorithm in complex scenes, an adaptive kernel correlation filter algorithm is proposed. First, a multifeature complementary scheme is proposed that linearly weights the responses of the histogram of oriented gradient (HOG) features and color features and learns a target position estimation model to realize target position estimation. Then, an adaptive scale model for estimating the scale transformation of the target is learned by extracting the HOG features of the object. Finally, according to occlusion judgment criteria, the Kalman filter is introduced to correct the position of the tracking target. The accuracy and success rate of the proposed algorithm are verified by simulation analysis on TC-128/OTB2015 benchmarks. Extensive experimental results illustrate that the proposed tracker achieves competitive performance compared with state-of-the-art trackers. The distance precision rate and overlap success rate of the proposed algorithm on OTB2015 are 0.899 and 0.635, respectively. The proposed algorithm effectively solves the long-term object tracking problem in complex scenes. This study provides references for computer vision processing, such as image retrieval, behavior analysis, and intelligent driving.

Spline adaptive filter with arctangent-momentum strategy for nonlinear system identification

In order to mitigate the interference of impulsive noises in the identification of Wiener-type nonlinear systems using traditional spline adaptive filter (SAF) algorithm, an enhanced SAF, named SAF-ARC-MMSGD, is proposed in this paper. Two improvements have been made in the proposed algorithm. First, the arctangent (ARC) function which is insensitive to large outliers is adopted to construct the cost function, so that the robustness of SAF against impulsive noises is improved. In addition, a modified momentum stochastic gradient descent (MMSGD) method containing two modifications, which are decay technique and selective strategy, is put forward in order to further improve the convergence speed and decrease the steady-state error during iteration. The convergence property of the proposed algorithm has been theoretically analyzed. The results of the numerical simulations have confirmed that the proposed algorithm has superior performance compared with the existing SAF related algorithms.

OCT image speckle sparse noise reduction based on dictionary algorithm

As a new non-invasive and high-resolution scanning method, optical coherence tomography (OCT) has been widely used in clinical practice, but OCT images have serious speckle noise, which greatly affects the diagnosis of diseases. Two original dictionary noise reduction algorithms have been improved for multiplicative speckle noise in OCT. The algorithm first performs logarithmic transformation on OCT images, uses orthogonal matching pursuit algorithm for sparse coding, and K singular value decomposition learning algorithm to update adaptive dictionary. Finally, it returns to the space domain through weighted average and exponential transformation. The experimental results show that the improved two dictionary algorithms can effectively reduce the speckle noise in OCT images and obtain good visual effects. The noise reduction effect is evaluated by four factors: mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and edge-preserving index (EPI). Compared with the two original dictionary noise reduction algorithms and the traditional filtering algorithms, the noise reduction effect of the two improved dictionary algorithms is better than that of other algorithms, and the improved adaptive dictionary algorithm performs better.

SOC estimation for batteries using MS-AUKF and neural network

In this paper, we present a new method for state of charge (SOC) estimation, which was based on a neural network and the master-slave adaptive unscented Kalman filter algorithm. First, a second-order Thevenin model of batteries was established. In order to improve the fitting accuracy between the open circuit voltage and the SOC of the battery, a neural network model was used to fit the nonlinear relationship, instead of the frequently used polynomial model. To solve the problems that the noise variance is fixed and the estimation accuracy is not high in traditional extended Kalman filter and unscented Kalman filter methods, the master slave-adaptive unscented Kalman filter algorithm was adopted. The main unscented Kalman filter of the algorithm was used to estimate the system state, and the slave unscented Kalman filter was used to estimate the noise variance matrix correspondingly. The update of noise variance of the system model was accompanied by the iteration of the algorithm to get rid of the shortcoming of the traditional Kalman filter algorithm which may cause filter divergence while setting the initial noise variance by experience. Simulation results demonstrate that the master slave-adaptive unscented Kalman filter algorithm shows higher accuracy and faster convergence speed when estimating the SOC of batteries compared with extended Kalman filter and unscented Kalman filter algorithms.

Tracking Algorithms Aided by the Pose of Target

The traditional target tracking algorithms have utilized the information on the target position. With the development of radar high-resolution technology, it is possible to obtain the pose of target. In this paper, two target tracking algorithms aided by the pose of target are proposed. First, the pose of the target is estimated in the real time by the high-resolution range profile, and then, the pose is added to the target measurement equation. Because the relationship between the pose and the motion parameters of the targets is nonlinear, the extended Kalman filter algorithm aided by the pose of target (Pose-EKF) and the unscented Kalman filter algorithm aided by the pose of target (Pose-UKF) are proposed. The results of simulation demonstrate that compared with the traditional extended Kalman filter algorithm (EKF) and the traditional Unscented Kalman filter algorithm (UKF), the proposed algorithm can greatly improve the target tracking accuracy (position precision and velocity precision) and the convergence speed. The pose measurement error has a little effect on the tracking performance. The difference in the tracking accuracy between Pose-EKF and Pose-UKF is very little. But the Pose-EKF is better than Pose-UKF in terms of computation time, but Pose-EKF fails and Pose-UKF is effective when the pose is critical.

Ameliorated mean adaptive median filtering algorithm

Aiming at the problem that the quality of infrared image decreases due to the large amount of random noise in the process of collection and transmission of infrared image of electrical equipment, and the accuracy of automatic detection of electrical equipment decreases, based on the traditional adaptive median filter algorithm, the adaptive median filter is analyzed, which can filter only the salt and pepper noise below 25%. An improved mean adaptive median filtering algorithm is proposed to overcome the shortcomings of wave effect. Firstly, the filtering window is selected according to the decision setting condition, and then it is judged whether the K-mean value near the center point is a noise point, and if so, the window is increased, otherwise the average value is output. Finally, it is judged whether the value of the current pixel point is noise, and if so, the average value is output, otherwise, the current pixel value is output. The experimental results show that the algorithm can effectively filter salt and pepper noise and Gauss noise, while maintaining the image sharpness, and has good filtering performance on PSNR and MSE indicators.

A Novel Laser ranging method based on adaptive Kalman filter technology

AbstractAiming at the problem of ranging accuracy of laser radar, a laser ranging method based on adaptive Kalman filter is proposed. According to the pulsed laser ranging method and acceleration model based on Wiener process, the standard Kalman filter algorithm is established. In order to solve the problem that the statistical characteristics of laser ranging noise are inconsistent with the actual noise, the autocovariance least squares method is used to estimate the noise parameters. The Monte Carlo method is used to simulate ranging results of laser radar and evaluate the performance of different methods. The adaptive Kalman filtering algorithm using autocovariance least squares method can better adjust the noise parameters and improve the distance measurement accuracy than the traditional Kalman filter algorithm. The experiment of the actual static ranging is carried out. The experiment results show that the SD of laser ranging is reduced from 10.9 to 4.8 mm by using proposed method.

The Power Plant Operating Data Based on Real-time Digital Filtration Technology

Real-time monitoring of the data of the thermal power plant was the basis of accurate analyzing thermal economy and accurate reconstruction of the operating state. Due to noise interference was inevitable; we need real-time monitoring data filtering to get accurate information of the units and equipment operating data of the thermal power plant. Real-time filtering algorithm couldn’t be used to correct the current data with future data. Compared with traditional filtering algorithm, there were a lot of constraints. First-order lag filtering method and weighted recursive average filtering method could be used for real-time filtering. This paper analyzes the characteristics of the two filtering methods and applications for real-time processing of the positive spin simulation data, and the thermal power plant operating data. The analysis was revealed that the weighted recursive average filtering method applied to the simulation and real-time plant data filtering achieved very good results.

A neutrosophic filter for high-density Salt and Pepper noise based on pixel-wise adaptive smoothing parameter

Abstract Image indeterminacy has been neglected in most traditional filtering algorithms. This paper proposes a pixel-wise adaptive neutrosophic filter based on neutrosophic indeterminacy feature to remove high-level Salt-and-Pepper noise. In the proposed algorithm, the indeterminacy of a pixel is quantified by a Neutrosophic Set and innovatively exploited as an efficient characteristic of measuring the similarity of pixels. In order to adjust the smoothing parameter of the weight function pixel-wise adaptively, the uncertainty of a pixel is utilized as an indicator of image contents. Extensive experiments on numerous images demonstrate that with a 3 × 3 window, our method outperforms many existing denoising methods in terms of noise suppression and detail preservation.

Adaptive sigma Kalman filter method for state-of-charge estimation based on the optimized battery model

Taking into account the equivalent coulomb efficiency and polarization effect, the nonlinear optimal compensative state and observation models were applied for state of charge (SoC) estimation of the lithium iron phosphate battery. On the basis of battery's parameter identification, an adaptive sigma Kalman filter algorithm was proposed to improve the accuracy of the SoC estimation and reduce the computational complexity of the traditional extended Kalman filter algorithm. The square root of state estimation error covariance was introduced to improve the positive semi-definition of the state covariance. Meanwhile, the estimated state variable and the observed variable were updated based on the iterative minimum mean square error estimation to achieve a precise estimate of the battery's SoC. Experiments were built, and the results indicate that the proposed optimized battery model and the SoC estimation algorithm are accurate and effective.

An Improved Item-based Collaborative Filtering Recommendation System

Today E-commerce is very popular, recommendation systems are very widely applied to various sites [1]. However, there still remains many problems in current recommendation system to be resolved. Given the data sparse problem in the traditional collaborative filter algorithm, we will introduce the relationship of the trust between the items, and transmit the similarity throughout it. In this paper, Experiment shows that the accuracy and coverage rate of the algorithm have been improved.

Extraction of digital terrain model based on regular mesh generation in mountainous areas

Airborne LiDAR technology is a popular technique to quickly acquire high-precision information of the ground and the objects above it. Moreover, filtering the data is one of the necessary core processing steps. In recent years, many algorithms have been derived from traditional filtering algorithms. However, when applied to LiDAR data obtained from mountainous regions, most algorithms generate numerous omissions and errors when attempting to retain steep terrain features and filter vegetation information. This paper aims to quickly and accurately extract the digital terrain model (DTM) in mountainous regions. To accomplish this goal, a filtering algorithm based on a regular mesh generation strategy is proposed. The proposed algorithm initially divides the huge amount of point cloud data into strips and selects the appropriate spacing to subdivide each strip into equidistant grid data. The grid data is used as the input to an iterative polynomial fitting process, after which the point cloud is classified based on a controlled threshold. The experimental results show that the proposed method can quickly and efficiently classify data of mountains with different characteristics while retaining terrain feature information better than other algorithms. The average accuracy of recognition is greater than 92%. In addition, this algorithm also applies to mountains with lush vegetation.

A personalized recommendation model for online apparel shopping based on Kansei engineering

PurposeToday clothing has become the largest category in online shopping in China, and even in Asia-Pacific. The satisfaction degree of apparel online shopping can be improved by effective personalized recommendation. The purpose of this paper is to propose a personalized recommendation model and algorithm based on Kansei engineering, traditional filtering algorithm and the knowledge relating to apparel.Design/methodology/approachUsers’ perceptual image and the design elements of apparel based on Kansei engineering are discussed to build the mapping relation between the design elements and user ratings employing verbal protocol, semantic differential and partial least squares. The implicit knowledge and emotional needs pertaining to users are accessed using analytic hierarchy process. A personalized recommendation model for apparel online shopping is established and the algorithm for the personalized recommendation process is proposed. To present the personalized recommendation model, men’s plaid shirts are taken as the example, and the recommendations of apparel for online shopping were implemented and ranked in the context of differing users’ emotional needs. A comparison between the traditional model and this model is made to verify the effectiveness.FindingsThe recommendation model is capable of analyzing data and information effectively, and providing fast, personalized apparel recommendation services in accordance with users’ emotional needs. The experimental results suggest that the model is effective.Originality/valueSimilar researches of recommendation mainly focus on the field of computer science, the basic idea of which is using users’ history accessing records or the preferences of other similar users for determination of users’ preferences. Since the attributes of apparel products are not factored in the approach referred above, the issue of personalized recommendation cannot be solved in a really effective way. Combining Kansei engineering and recommendation algorithm, a framework for apparel product recommendation is presented and it is a new way for improvement of recommendations for apparel products on shopping sites.

Denoising of sparse images in impulsive disturbance environment

The paper presents a method for denoising and reconstruction of sparse images based on a gradient-descent algorithm. It is assumed that the original (non-noisy) image is sparse in the two-dimensional Discrete Cosine Transform (2D-DCT) domain. It is also assumed that a number of image pixels is corrupted by a salt and pepper noise. In addition, we assume that there are pixels corrupted by a noise of any value. In this paper we introduce a method to find the positions of the corrupted pixels when the noise is not of the salt and pepper form. The proposed algorithm for noisy pixels detection and reconstruction works blindly. It does not require the knowledge about the positions of corrupted pixels. The only assumption is that the image is sparse and that the noise degrades this property. The advantage of this reconstruction algorithm is that we do not change the uncorrupted pixels in the process of the reconstruction, unlike common reconstruction methods. Corrupted pixels are detected and removed iteratively using the gradient of sparsity measure as a criterion for detection. After the corrupted pixels are detected and removed, the gradient algorithm is employed to reconstruct the image. The algorithm is tested on both grayscale and color images. Additionally, the case when both salt and pepper noise and a random noise, within the pixel values range, are combined is considered. The proposed method can be used without explicitly imposing the image sparsity in a strict sense. Quality of the reconstructed image is measured for different sparsity and noise levels using the structural similarity index, the mean absolute error, mean-square error and peak signal-to-noise ratio and compared to the traditional median filter and recent algorithms, one based on the total-variations reconstruction and a two-stage adaptive algorithm.

A neutrosophic filter for high-density Salt and Pepper noise based on pixel-wise adaptive smoothing parameter

Image indeterminacy has been neglected in most traditional filtering algorithms. This paper proposes a pixel-wise adaptive neutrosophic filter based on neutrosophic indeterminacy feature to remove high-level Salt-and-Pepper noise. In the proposed algorithm, the indeterminacy of a pixel is quantified by a Neutrosophic Set and innovatively exploited as an efficient characteristic of measuring the similarity of pixels. In order to adjust the smoothing parameter of the weight function pixel-wise adaptively, the uncertainty of a pixel is utilized as an indicator of image contents. Extensive experiments on numerous images demonstrate that with a 3×3 window, our method outperforms many existing denoising methods in terms of noise suppression and detail preservation.

Fine feature retention of magnetic resonance images using intensity inhomogeneity weighted non-local filtering

Electronic noise in the presence of intensity inhomogeneity severely inhibits the robustness of many traditional filtering algorithms. We propose an adaptive filter that utilises the intensity inhomogeneity bias field often observed in magnetic resonance imaging as a map for directing the magnitude of smoothing. This adaptive filter allows for controlled filtering of images where high signal-to-noise (SNR) regions are smoothed less, while low SNR regions are smoothed more intensely. We validated our method with a phantom and MRIs of the lumbar spine. Results showed that the proposed algorithm improved filtering of noise in the presence of intensity inhomogeneity.

Research and Realization on Image Preprocessing Technology Based on Improved Median Filter

Aiming at the problem that traditional median filter algorithm cannot process collected images quickly and efficiently, this paper adopts improved median filter and makes use of advantages, such as fast running speed, parallel running of inner program, to design an image preprocessing system with high real time ability and high flexibility. At last, compared with MATLAB median filter simulation figure and multilevel median filter, it has shown that using FPGA to process and improve median filter can not only conduct median filter to images successfully, but also has the ability of fast operation speed and low energy consumption.

A Denoising Method for LiDAR Full-Waveform Data

Decomposition of LiDAR full-waveform data can not only enhance the density and positioning accuracy of a point cloud, but also provide other useful parameters, such as pulse width, peak amplitude, and peak position which are important information for subsequent processing. Full-waveform data usually contain some random noises. Traditional filtering algorithms always cause distortion in the waveform.λ/μfiltering algorithm is based on Mean Shift method. It can smooth the signal iteratively and will not cause any distortion in the waveform. In this paper, an improvedλ/μfiltering algorithm is proposed, and several experiments on both simulated waveform data and real waveform data are implemented to prove the effectiveness of the proposed algorithm.