Robust Adaptive Filtering Research Articles

RESEARCH POTENTIAL EFFECTIVENESS HIGHLIGHT THE IMAGE OF THE UNKNOWN OBJECT IN A LOW CONTRAST IMAGE USING ROBUST ADAPTIVE SPATIAL-SELECTIVE FILTER

By simulation we got assessment of the potential effectiveness highlight the image of a remote object of unknown shape (space object) on black-and-white picture with using robust adaptive spatial-selective filter. It is assumed that the data processing is carried out in conditions of a priori uncertainty regarding the form of the space object and the probabilistic description of the background of the situation. Examples of image processing in natural conditions in operation at the object 3006, and evaluation of the use of algorithms.

Robust Hammerstein Adaptive Filtering under Maximum Correntropy Criterion

The maximum correntropy criterion (MCC) has recently been successfully applied to adaptive filtering. Adaptive algorithms under MCC show strong robustness against large outliers. In this work, we apply the MCC criterion to develop a robust Hammerstein adaptive filter. Compared with the traditional Hammerstein adaptive filters, which are usually derived based on the well-known mean square error (MSE) criterion, the proposed algorithm can achieve better convergence performance especially in the presence of impulsive non-Gaussian (e.g., α-stable) noises. Additionally, some theoretical results concerning the convergence behavior are also obtained. Simulation examples are presented to confirm the superior performance of the new algorithm.

Impact of detector-element active-area shape and fill factor on super-resolution

In many undersampled imaging systems, spatial integration from the individual detector elements is the dominant component of the system point spread function (PSF). Conventional focal plane arrays (FPAs) utilize square detector elements with a nearly 100% fill factor, where fill factor is defined as the fraction of the detector element area that is active in light detection. A large fill factor is generally considered to be desirable because more photons are collected for a given pitch, and this leads to a higher signal-to-noise-ratio (SNR). However, the large active area works against super-resolution (SR) image restoration by acting as an additional low pass filter in the overall PSF when modeled on the SR sampling grid. A high fill factor also tends to increase blurring from pixel cross-talk. In this paper, we study the impact of FPA detector-element shape and fill factor on SR. A detailed modulation transfer function analysis is provided along with a number of experimental results with both simulated data and real data acquired with a midwave infrared (MWIR) imaging system. We demonstrate the potential advantage of low fill factor detector elements when combined with SR image restoration. Our results suggest that low fill factor circular detector elements may be the best choice. New video results are presented using robust adaptive Wiener filter SR processing applied to data from a commercial MWIR imaging system with both high and low detector element fill factors.

Design of adaptive robust square-root cubature Kalman filter with noise statistic estimator

To solve the problem that when the model uncertainties exists or the prior noise statistics are unknown, the accuracy of the cubature Kalman filter (CKF) will decline or diverge, an adaptive robust square-root CKF (ARSCKF) algorithm with the noise statistic estimator is proposed. Firstly, the square-root version of the CKF (SCKF) algorithm is given when the means of process and measurement Gaussian noise sequences are nonzero. Secondly, based on strong tracking filter principle, the robust SCKF is designed to accommodate modeling uncertainties. Thirdly, the suboptimal unbiased constant noise statistic estimator based on the principle of maximum a posterior (MAP) is derived; on this basis, the recursive formula of the time variant noise statistic estimator using exponential weighted method is provided and then the implementation process of the ARSCKF algorithm is constructed. Finally, the efficacy of the ARSCKF is demonstrated by the numerical experiments of a high-dimensional target tracking system.

Adaptive robust Kalman filtering for precise point positioning

The optimality of precise point postioning (PPP) solution using a Kalman filter is closely connected to the quality of the a priori information about the process noise and the updated mesurement noise, which are sometimes difficult to obtain. Also, the estimation enviroment in the case of dynamic or kinematic applications is not always fixed but is subject to change. To overcome these problems, an adaptive robust Kalman filtering algorithm, the main feature of which introduces an equivalent covariance matrix to resist the unexpected outliers and an adaptive factor to balance the contribution of observational information and predicted information from the system dynamic model, is applied for PPP processing. The basic models of PPP including the observation model, dynamic model and stochastic model are provided first. Then an adaptive robust Kalmam filter is developed for PPP. Compared with the conventional robust estimator, only the observation with largest standardized residual will be operated by the IGG III function in each iteration to avoid reducing the contribution of the normal observations or even filter divergence. Finally, tests carried out in both static and kinematic modes have confirmed that the adaptive robust Kalman filter outperforms the classic Kalman filter by turning either the equivalent variance matrix or the adaptive factor or both of them. This becomes evident when analyzing the positioning errors in flight tests at the turns due to the target maneuvering and unknown process/measurement noises.

A robust high-order lattice adaptive notch filter and its application to narrowband noise cancellation

Abstract In this paper, we propose a high-order lattice adaptive notch filter (LANF) that can robustly track multiple sinusoids. Unlike the conventional cascade structure, the proposed high-order LANF has robust tracking characteristics regardless of the frequencies of reference sinusoids and initial notch frequencies. The proposed high-order LANF is applied to a narrowband adaptive noise cancellation (ANC) to mitigate the effect of the broadband disturbance in the reference signal. By utilizing the gradient adaptive lattice (GAL) ANC algorithm and approximately combining it with the proposed high-order LANF, a computationally efficient narrowband ANC system is obtained. Experimental results demonstrate the robustness of the proposed high-order LANF and the effectiveness of the obtained narrowband ANC system.

Adaptive robust ultra‐tightly coupled global navigation satellite system/inertial navigation system based on global positioning system/BeiDou vector tracking loops

With the development of global navigation satellite system (GNSS), the GNSS/inertial navigation system (INS) integrated system offers the users better positioning or navigation performance. This paper proposes an adaptive robust ultra-tightly coupled GNSS/INS system based on a novel vector tracking strategy for combining both global positioning system (GPS) L1 and BeiDou B1 signals' tracking together. The inherent mechanism of the vector tracking approach has been analysed to describe the relationship between the replica signals and user's dynamic state. Then, an adaptive robust filter is used to gain the accurate estimates of vehicle states when the vehicle is under a weak-signal or large manoeuvring environment. Finally, the experimental platform is set up using a GPS/BeiDou signal simulator and an inertial measurement unit simulator and the test results show that the proposed ultra-tightly coupled system can keep the tracking loops from the high dynamic perturbations, which saves the cost time of signal reacquisition. Moreover, the presented adaptive robust ultra-tightly coupled system can obtain a higher accuracy than Kalman filtering in a simultaneous weak-signal and large manoeuvring environment.

A Blind Adaptive SOR/JGS Iterative Kalman MUD Algorithm for Multiple Access Communication System

Based on the fast stable convergence characteristics of successive over relaxation (SOR) iterative and Jacobi Gauss- Seidel (JGS) iterative, a blind adaptive SOR/JGS iterative Kalman multi-user detection (MUD) algorithm (SJK) is proposed for multiple access communication system as direct sequence spread spectrum code division multiple access (DS- CDMA) system with multi-path fading channel. The proposed combination of blind adaptive Kalman filtering theory, SOR and JGS iterative method can adaptively control the selection of relaxation parameters and damping parameter, and then effectively deal the problem as time-varying noise statistics estimation. Compared with traditional standard Kalman filter (SKF), fading Kalman filter (FKF) and robust adaptive Kalman filter (RAKF) algorithm, the proposed algorithm can effectively estimate unknown noise statistics characteristics on-line while conducting state filtering, totally track the time-varying channel, minimize the detection error diffusion, and thus effectively reduce multiple access interference (MAI). Simulation results show that the SJK algorithm is of better detection accuracy, convergence ability, dynamic tracking capability, and lower bit error rate (BER) performance.  Index Terms communication system is asynchronous or synchronous. When the distance between interference user and base station is closer than expected user, the received power of interference user would be much larger than expected user, then the correlation between spreading sequence and interference user would surely be much larger than that between spreading sequence and expected user. This would always cause a significant increase of MAI component in the traditional Multi-User Detection (MUD) receiver, and it may easily cause that the expected user signal is submerged by interference user signal. Namely, the Bit Error Rate (BER) of MUD receiver is very sensitive to the difference between expected user and interference user. Based on the above situation, the traditional MUD algorithm for DS-CDMA system such as Matched Filter (MF) and decorrelation detection receiver cannot effectively eliminate the impact of MAI and FNP. This makes SKF, Fading Kalman Filter (FKF) and Robust Adaptive Kalman Filter (RAKF) the focus of research.

Simultaneous Adaptation of the Process and Measurement Noise Covariances for the UKF Applied to Nanosatellite Attitude Estimation

A common technique for improving the estimation performance of the Kalman filter and making the filter robust against any kind of faults is to adapt its process and measurement noise covariance matrices. Although there are numerous approaches for the adaptation such as full estimation or scaling, simultaneous adaptation of these two matrices is an ongoing discussion. In this paper, firstly, two common problems for the attitude estimation of a nanosatellite are solved by adapting the process and noise covariance matrices. Then these two adaptation methods are integrated with an easy to apply scheme and the matrices are simultaneously adapted. The newly proposed filtering algorithm, which is named Robust Adaptive Unscented Kalman Filter, considerably increases the estimation performance and is fault tolerant against the sensor malfunctions.

Robust Adaptive Filter for Small Satellite Attitude Estimation Based on Magnetometer and Gyro

Based on magnetometer and gyro measurement, a sequential scheme is proposed to determine the orbit and attitude of small satellite simultaneously. In order to reduce the impact of orbital errors on attitude estimation, a robust adaptive Kalman filter is developed. It uses a scale factor and an adaptive factor, which are constructed by Huber function and innovation sequence, respectively, to adjust the covariance matrix of system state and observational noise, change the weights of predicted and measured parameters, get suitable Kalman filter gain and approximate optimal filtering results. Numerical simulations are carried out and the proposed filter is approved to be robust for the noise disturbance and parameter uncertainty and can provide higher accuracy attitude estimation.

Dorsal hand vein recognition based on 2D Gabor filters

Hand vein patterns are among the biometric traits being investigated today for identification purposes, attracting interest from both the research community and industry. A reliable and robust personal verification approach using dorsal hand vein patterns is presented in this paper. This approach needs less computational and memory requirements and has a higher recognition accuracy than similar methods. In our work, a near-infrared charge-coupled device camera is adopted as an input device for capturing dorsal hand vein images, due to its advantages of the low-cost and non-contact imaging. In the proposed approach, two finger-peaks are automatically selected to define the region of interest in the dorsal hand vein images. In order to obtain effective pattern of dorsal hand vein vascular, we proposed an innovative and robust adaptive Gabor filter method to extract the dorsal hand vein patterns and encode the vein features in bit string representation. The bit string representation, called VeinCode, offers speedy template matching and enables more effective template storage and retrieval. The similarity of two VeinCodes is measured by normalised Hamming distance. A total of 6160 dorsal hand vein images were collected from 308 persons to verify the validity of the proposed dorsal hand vein recognition approach. High accuracies (>99%) have been obtained by the proposed method, and the speed of the method (responding time <0·8 s) is rapid enough for real-time recognition. Experimental results demonstrate that our proposed approach is feasible and effective for dorsal hand vein recognition.

Rapid alignment method based on local observability analysis for strapdown inertial navigation system

This paper presents a new method for rapid transfer alignment of strapdown inertial navigation system (SINS) by combining the local observability concept with robust adaptive filtering. The system state and observation models are simplified to analyze the observability of the SINS attitude error. Based on the simplified models, the observability matrix and its condition number are constructed to determine the observability degree of SINS during the process of rapid transfer alignment. On the basis of the local obsersability analysis, a robust adaptive filter is developed to estimate the attitude angle error for SINS rapid transfer alignment. Simulation experiments and comparison analysis have been conducted, demonstrating that the proposed method cannot only effectively measure the local observability of SINS, but it can also enhance the performance of SINS rapid transfer alignment and reduce the alignment time.

Pipelined robust M-estimate adaptive second-order Volterra filter against impulsive noise

The main limits on adaptive Volterra filters are their computational complexity in practical implementation and significant performance degradation under the impulsive noise environment. In this paper, a low-complexity pipelined robust M-estimate second-order Volterra (PRMSOV) filter is proposed to reduce the computational burdens of the Volterra filter and enhance the robustness against impulsive noise. The PRMSOV filter consists of a number of extended second-order Volterra (SOV) modules without feedback input cascaded in a chained form. To apply to the modular architecture, the modified normalized least mean M-estimate (NLMM) algorithms are derived to suppress the effect of impulsive noise on the nonlinear and linear combiner subsections, respectively. Since the SOV-NLMM modules in the PRMSOV can operate simultaneously in a pipelined parallelism fashion, they can give a significant improvement of computational efficiency and robustness against impulsive noise. The stability and convergence on nonlinear and linear combiner subsections are also analyzed with the contaminated Gaussian (CG) noise model. Simulations on nonlinear system identification and speech prediction show the proposed PRMSOV filter has better performance than the conventional SOV filter and joint process pipelined SOV (JPPSOV) filter under impulsive noise environment. The initial convergence, steady-state error, robustness and computational complexity are also better than the SOV and JPPSOV filters.

Near Space Pseudolite Navigation System Design and High-Performance Filtering Algorithm

This paper analyzes the defects of satellite navigation systems that exist in positioning and precision-guided weapons and pointes out the advantages and military needs of pseudolite. The autonomous navigation nonlinear mathematical model of Near Space Pseudolite SINS/CNS/SAR autonomous navigation system is established. Based on the merits of fading filter, robust adaptive filtering and particle filter, we propose a fading adaptive Unscented Particle Filtering algorithm. The proposed filtering algorithm is applied to SINS/CNS/SAR autonomous navigation system and conducted simulation calculation with the Unscented Kalman filter and particle filter comparison. The results show that the new algorithm that is proposed meets the needs of pseudolite autonomous navigation, and the navigation accuracy is significantly higher than the Unscented Kalman filter and particle filter algorithm.

Adaptive robust Kalman filter for relative navigation using global position system

An adaptive robust Kalman filter algorithm is derived to account for both process noise and measurement noise uncertainty. The adaptive algorithm estimates process noise covariance based on the recursive minimisation of the difference between residual covariance matrix given by the filter and that calculated from time-averaging of the residual sequence generated by the filter at each time step. A recursive algorithm is proposed based on both Massachusetts Institute of Technology (MIT) rule and typical non-linear extended Kalman filter equations for minimising the difference. The measurement update using a robust technique to minimise a criterion function originated from Huber filter. The proposed adaptive robust Kalman filter has been successfully implemented in relative navigation using global position system for spacecraft formation flying in low earth orbit, with real-orbit perturbations and non-Gaussian random measurement errors. The numerical simulation results indicate that the proposed adaptive robust filter can provide better relative navigation performance in terms of accuracy and robustness as compared with previous filter algorithms.

Robust Adaptive Unscented Particle Filter

This paper presents a new robust adaptive unscented particle filtering algorithm by adopting the concept of robust adaptive filtering to the unscented particle filter. In order to prevent particles from degeneracy, this algorithm adaptively determines the equivalent weight function according to robust estimation and adaptively adjusts the adaptive factor constructed from predicted residuals to resist the disturbances of singular observations and the kinematic model noise. It also uses the unscented transformation to improve the accuracy of particle filtering, thus providing the reliable state estimation for improving the performance of robust adaptive filtering. Experiments and comparison analysis demonstrate that the proposed filtering algorithm can effectively resist disturbances due to system state noise and observation noise, leading to the improved filtering accuracy.

Robust adaptive unscented Kalman filter for attitude estimation of pico satellites

SUMMARYUnscented Kalman filter (UKF) is a filtering algorithm that gives sufficiently good estimation results for the estimation problems of nonlinear systems even when high nonlinearity is in question. However, in case of system uncertainty or measurement malfunctions, the UKF becomes inaccurate and diverges by time. This study introduces a fault‐tolerant attitude estimation algorithm for pico satellites. The algorithm uses a robust adaptive UKF, which performs correction for the process noise covariance (Q‐adaptation) or measurement noise covariance (R‐adaptation) depending on the type of the fault. By the use of a newly proposed adaptation scheme for the conventional UKF algorithm, the fault is detected and isolated, and the essential adaptation procedure is followed in accordance with the fault type. The proposed algorithm is tested as a part of the attitude estimation algorithm of a pico satellite. Copyright © 2013 John Wiley &amp; Sons, Ltd.

A new fusion scheme for accuracy enhancement and error modification in GPS/INS tight integrated navigation

In tight integrated navigation based on pseudoranges and Doppler observations, the positioning precision is poor and the modification of the attitude angles are not accurate for poor precision of pseudorange observations. The outliers and the dynamic errors can easily influence the precision of adaptive filtering, because the number of measurements is usually less than that of the state parameters. Therefore, a new fusion scheme of two-step adaptive robust filtering based on the observability of the parameters is presented for accuracy enhancement and error modification. First of all, the process of the adaptive Kalman filtering of tight integration based on the predicted residuals is given and the limitation is analysed. Then, the formulas and the new fusion algorithms are deduced and analysed. In the first-step filtering of the new algorithms, an adaptive factor is used to control the influence of the dynamic errors and modify the attitude errors. In the second-step filtering, robust equivalent weight is adopted to resist the bad influence of the outliers. Finally, an actual calculation is given. First, the results show that precise random models and direct estimates can give correct modification, while poor direct estimates will lead to incorrect modification. Second, they also show that the new fusion scheme can control the disturbances of the state and the outliers of the observations compared with the adaptive Kalman filtering of tight integration based on the predicted residuals. And the navigation precision does not decrease, while the integration period becomes longer and the INS noise become higher. The INS errors can be accurately estimated and the precision of attitude angles is improved.

Palm vein recognition using adaptive Gabor filter

Vein pattern recognition is one of the newest biometric techniques researched today. One of the reliable and robust personal identification authentication approaches using palm vein patterns is presented in this paper. In our work, we consider the palm vein as a piece of texture and apply texture-based feature extraction techniques to palm vein authentication. A Gabor filter provides the optimized resolution in both the spatial and frequency domains, thus it is a basis for extracting local features in the palm vein recognition. However, Gabor filter has many potential parameter combinations to use, and it is a common practice now to use multiple Gabor filters or to determine desired single combination by experience. The overall aim of this work is to discuss the optimization algorithm that determines the best parameter values of a single Gabor filter for palm vein recognition. In order to obtain effective pattern of palm vascular, we proposed an innovative and robust adaptive Gabor filter method to encode the palm vein features in bit string representation. The bit string representation, called VeinCode, offers speedy template matching and enables more effective template storage and retrieval. The similarity of two VeinCodes is measured by normalized Hamming distance. A total of 4140 palm vein images were collected form 207 persons to verify the validity of the proposed palm vein recognition approach. High accuracy has been obtained by the proposed method and the speed of this method is rapid enough for real-time palm vein recognition. Experimental results demonstrate that our proposed approach is feasible and effective in palm vein recognition.

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 signal. 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.