Unbiased Finite Impulse Response Research Articles

Improving Visual Object Detection using General UFIR Filtering

Object detection is a fundamental task in computer vision, which involves the identification and localization of objects within image frames or video sequences. The problem is complicated by large variations in the video camera bounding box, which can be thought of as colored measurement noise (CMN). In this paper, we use the general unbiased finite impulse response (GUFIR) approach to improve detection performance under CMN. The results are compared to the general Kalman filter (GKF) and two detection methods: “Faster-RCNN” and “Tensorflow PASCAL Visual Object Classes (VOC)”. Experimental testing is carried out using the benchmark data ”Car4”. It is shown that GUFIR significantly improves the detection accuracy and demonstrates the properties of the effective tool for visual object tracking.

A VISION-BASED SYSTEM DESIGN AND IMPLEMENTATION FOR ACCIDENT DETECTION AND ANALYSIS VIA TRAFFIC SURVEILLANCE VIDEO

This study aims to test the entire framework on an AI demo board and investigate the issue of automatically and effectively recognizing and analyzing traffic incidents captured by surveillance cameras. To begin, the motion interaction field (MIF) method, which is capable of detecting collisions in video, is used to locate damaged automobiles based on the interactions of various moving objects. Second, the location of the destroyed vehicles is determined using the YOLO v3 model. Using a hierarchical clustering method, the vehicle trajectories prior to the collision are recovered, and the related trajectories are reconstructed. Finally, a perspective transformation is used to project the trajectory into a vertical view to help traffic cops make better decisions. The unbiased finite impulse response (UFIR) method, which does not require statistical information about the external noise, is used to estimate the vehicle's velocity. The estimated velocity and impact angle from the vertical view can then be used to investigate the traffic accident. Finally, a Huawei AI demo board called HiKey970, which was used to code all of the aforementioned algorithms, is used in an experiment to show how useful and effective the proposed method is in practice. A few mishap observation recordings are sent onto the demo board. Mishaps are recognized and the proper vehicle directions are gathered.

Off-axis telescope misalignment correction based on defocus spot moment features

Optical mirror misalignments, which are caused by assembly mistakes and changes in the surrounding environment (such as gravity, temperature, and atmosphere), degrade the system’s imaging performance. Therefore, active misalignment correction is essential for ensuring the image quality of the off-axis telescope. In this paper, a novel misalignment correction method without wavefront sensors is proposed. The point spread functions (PSFs) of the system are analytically related to the optical mirror misalignments. On this basis, a fully connected neural network (FCNN) is used to establish the mapping relationship between the misalignments and the discrete orthogonal unbiased finite impulse response (UFIR) moment features, which can effectively characterize changes of intensity and geometric of the spot image. The simulation and experimental results in this paper justify the effectiveness and practicality of the proposed method. This approach offers a low-cost and straightforward technical method for achieving high imaging quality throughout the alignment and observation phases. This approach can prevent the accumulation of errors caused by wavefront detection and the high delay of multiple iterations.

Denoising ECG Signals using Weighted Iterative UFIR Filtering

The electrocardiogram (ECG) holds paramount importance in diagnosing heart disease, and as it persists leading cause of global mortality. Over the past decades, diverse techniques have emerged for processing ECG signals, with denoising taking a prominent role in enhancing feature extraction. Nonetheless, achieving heightened accuracy remains an enduring challenge. In this study, we introduce an innovative approach involving the application of a weighted unbiased finite impulse response (UFIR) filter. Under the same noise conditions and in terms of the root mean square error (RMSE) and signal-to-noise ratio (SNR), our proposed method showcases worthy performance in comparison to the weighted Savitzky-Golay (SG) filter. This research contributes to the progressive evolution of ECG signal processing, offering the potential for more precise and dependable detection of cardiac diseases.

A Novel Approach for Car Accident Detection and Analysis Using AIML Techniques

Abstract: In the context of escalating concerns regarding road safety, this study introduces an automated traffic accident detection and analysis framework that utilizes surveillance footage. The approach synergistically combines the Motion Interaction Field (MIF) model and YOLO v5 algorithm to achieve accurate crash detection and precise vehicle classification. The primary objective is to develop an autonomous system for identifying and evaluating traffic events captured by security cameras. The MIF model extracts valuable insights from interactions among moving objects within images and videos, tailored explicitly for detecting damaged vehicles. Localization of damaged vehicles is achieved using the YOLO v5 algorithm, complemented by perspective transformation to enable vertical image representation. The unbiased finite impulse response (UFIR) technique, notable for its insensitivity to statistical noise data, effectively determines vehicle motion. Furthermore, this work delves into an in-depth analysis of car crashes, estimating impact speed and point of collision from an aerial perspective. This work encompasses meticulous accident clip analysis, precise car direction determination, and error identification. The outcomes underscore the potential of the proposed framework to revolutionize accident detection and analysis, contributing to heightened road safety and streamlined traffic management strategies.

Localization of underground pipe jacking machinery: A reliable, real-time and robust INS/OD solution

Accurate positioning is important for guidance control in pipeline construction, but traditional approaches fail in GPS-denied environments. This paper addresses the underground localization problem for the pipe jacking machine by presenting an Inertial Navigation System (INS)/ODometry (OD) scheme. It is a loosely coupled navigation system that combines an INS and an OD to provide reliable, real-time, and robust location estimation in an enclosed and restricted space. The combination is achieved by a multi-sensor fusion algorithm to avoid the divergence of standalone INS. Compared with the traditional localization technologies, the advantage of the INS/OD scheme is that there is no performance degradation or service failure despite severe blockage of the thick soil layer due to the selection of dead reckoning-based sensors. Moreover, an Unbiased Finite Impulse Response (UFIR) filter serves as the fusion mechanism to improve the robustness against a prior knowledge of noise, considering unknown and time-varying noise statistics in practice. The INS/OD scheme is verified through a numerical simulation and employed on-site in a real underground sewage pipeline construction project. Despite the construction site being more than 10 meters deep underground, the INS/OD scheme continues to perform reliably and robustly in real-time, significantly benefiting the engineering guidance enterprise.

Robust GNSS Positioning Using Unbiased Finite Impulse Response Filter

In a typical GNSS receiver, pseudorange and pseudorange rate measurements are generated through the code and carrier tracking loops, respectively. These measurements are then employed to calculate the user’s position and velocity (PV) solutions, which is typically achieved using a Kalman filter (KF) or the least squares (LS) algorithm. However, the LS method only uses the current observation without error analysis. The positioning result is greatly affected by the errors in the observed data. In KF, by using an iterative approach that combines predictions and measurements of PV information, more accurate estimates can be obtained because the PV information is time-correlated. Meanwhile, its optimal estimate requires that both the model and noise statistics are exactly known. Otherwise, achieving optimality cannot be guaranteed. To address this issue, this paper proposes and implements a novel GNSS solution method based on an unbiased finite impulse response (UFIR) filter. Two different field tests were conducted. The position results of UFIR are compared with those from the LS and KF methods, and the horizon positioning mean error is improved by 44% and 29%, respectively, which highlights its efficacy. The method offers two primary benefits: it is robust to noise uncertainty, and it leverages historical data within the UFIR framework to provide a more accurate estimate of the current state.

Extended Kalman/UFIR Filters for UWB-Based Indoor Robot Localization Under Time-Varying Colored Measurement Noise

In indoor robot localization by using Ultra-Wide Band (UWB), the extended Kalman filter (EKF)-based algorithms suffer from the Colored Measurement Noise (CMN) that degrades the localization accuracy and causes the divergence. To overcome this issue, we develop a hybrid colored EKF and colored extended unbiased Finite Impulse Response (EFIR) filter (cEKF/EFIR filter) employing measurement differences. We also develop this algorithm using a filter bank on merged averaging horizons to be adaptive to time-varying CMN and call it the aEKF/EFIR filter. Experimental testing is provided in UWB-based indoor mobile robot localization environments. It is shown that the end-to-end cEKF/EFIR and aEKF/EFIR filtering algorithms have better performances than the EKF, EFIR filter, and their modifications for CMN.

A novel fusion maximum correntropy Kalman/UFIR filter for state estimation with uncertain non-Gaussian noise statistics

Despite the higher estimation accuracy of the maximum correntropy Kalman filter (MCKF) algorithm in system with non-Gaussian noise, its estimation performance will decrease when the noise source changes due to disturbances from external uncertainties. Fortunately, the unbiased finite impulse response predictor (UFIR) can overcome the MCKF problems by automatically ignoring the noise related to both the process and measurements. Meanwhile, the MCKF could resolve the UFIR problems, such as non-optimal estimation performance and risk of dead zones. Therefore, a novel fusion maximum correntropy Kalman/UFIR filter is proposed to achieve a more advantageous estimation effect, in which specific probability weights are assigned to the two filters to implement the fusion of two filters. The simulation results demonstrate the superiority of the presented fusion filter algorithm when compared to the fusion Kalman/UFIR filter regarding the estimation performance under the non-Gaussian noise system.

A Performance of AFIRO among Asynchronous Iteration Strategy Metaheuristic Algorithms

Asynchronous Finite Impulse Response Optimizer (AFIRO) is a metaheuristic algorithm that has been developed as a population-based solution with an asynchronous update mechanism. AFIRO is inspired by the Ultimate Unbiased Finite Impulse Response filter framework. AFIRO works with a group of agents where each agent performs the iteration update asynchronously. In the original paper, AFIRO was compared with the Particle Swarm Optimisation algorithm, Genetic Algorithm, and Grey Wolf Optimizer. Although AFIRO shows a better performance, the comparison seems unfair since the iteration strategy of AFIRO is different from those compared algorithms. Hence, this article further investigates the potential of AFIRO against three existent metaheuristic algorithms with the same iteration strategy, namely Asynchronous PSO (A-PSO), Asynchronous Gravitational Search Algorithm (A-GSA), and Asynchronous Simulated Kalman Filter (A-SKF). The CEC2014 test suite was applied to evaluate the performance, where the results revealed that AFIRO leads 18 out of 30 functions. The Holm post hoc showed that AFIRO performs significantly better than A-SKF and A-GSA while having the same performance as A- PSO. Moreover, the Friedman test disclosed that AFIRO has the highest ranking than A-PSO, A-SKF, and A-GSA. Therefore, it can be concluded that AFIRO performs well in the same iteration strategy category.

H∞‐FIR filtering of disturbed systems using LMI under measurement and initial errors

SummaryFinite impulse response (FIR) filtering is known to be more robust than Kalman filtering. In this article, we derive a discrete convolution‐based ‐FIR observer for disturbed systems under measurement and initial errors. The gain for the ‐FIR observer is obtained numerically by solving a linear matrix inequality (LMI). Since the LMI has a term that is quadratic with respect to the filter gain, we modify and constrain LMI by introducing an additional variable and proving a theorem. It is shown numerically and experimentally that for disturbed systems operating under measurement and initial errors, the developed ‐FIR observer surpasses the optimal FIR and Kalman filters in accuracy and has almost the same robustness as a robust unbiased FIR filter.

Batch Optimal FIR Smoothing: Increasing State Informativity in Nonwhite Measurement Noise Environments

Strictly nonwhite measurement noise (NMN) is observed in many industrial processes. Therefore, effective smoothing is often required to extract useful information about the process state with maximum accuracy. This paper proposes a batch <inline-formula><tex-math notation="LaTeX">$q$</tex-math></inline-formula>-lag optimal finite impulse response (OFIR) smoother, operating under NMN with full block covariance matrices. It is shown that the OFIR smoother significantly outperforms the Rauch-Tung-Striebel (RTS) smoother and the unbiased FIR (UFIR) smoother. Testing is provided based on object tracking. The results are validated by a practical example of a three degree-of-freedom helicopter system, which confirms that OFIR smoothing provides better noise reduction than UFIR smoothing, RTS smoothing, and modified RTS smoothing using state augmentation and measurement differencing.

Moving Vehicle Tracking under Measurement Uncertainties, Multi-step Random Delays, and Packet Dropouts

The unbiased finite impulse response (UFIR) filter, Kalman filter (KF), and game theory <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty} $ </tex-math></inline-formula> filter are developed for vehicle tracking in wireless sensor networks (WSNs) with multi-step random delays and multiple dropouts. The problem with data delays is solved for given delay probabilities by converting a model with delays to another without delays. The dropouts are detected at the receiver by a data sensor, and lost data are compensated for by prediction. The filters developed are experimentally tested for accuracy and robustness using the Global Positioning System-based vehicle tracking database, where the measured coordinates are transmitted over WSN with delays. It is shown that the UFIR filter is the most robust to uncertain delays and the KF is the most accurate when the delay probabilities are known exactly. The game theory <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> filter is the most accurate under ideal conditions and prone to divergency otherwise.

TWOR: Improving Modeling and Self-Localization in RFID-Tag Networks Under Colored Noise

This paper discusses the three-wheeled omnidirectional robot (TWOR) self-localization in radio frequency identification (RFID) tag environments. The nonlinear TWOR model is significantly improved by using geometric interpretation and incremental time representation in discrete time. The TWOR position and heading are self-estimated using distance measurements to RFID tags and a digital gyroscope in the presence of typical colored measurement noise (CMN). The extended unbiased finite impulse response (EFIR) is developed along with the extended Kalman filter (EKF) and their versions, cEKF and cEFIR, modified for Gauss-Markov CMN. A particle filter is used as a benchmark. It is shown that the cEFIR filter is more robust than the cEKF and almost as robust as the particle filter, although the latter is less accurate in real time.

Estimation of States Under Colored Measurement Noise (CMN) Using UFIR and Kalman Filters Modified

The estimate process of a moving target trajectory is a well-known problem, where the main objective is to improve the estimation of object position. During the tracking are presented errors or variations between the true position and the estimated. In this paper, we treat such variations as a Gauss-Markov colored measurement noise (CMN). The estimation process is performed in predict and update, where the prediction indicates the next position of the bounding box, and the update is a correction step, which includes the new measurement of the tracking model and helps to improve the estimation. Looking for this improvement we use Kalman and Unbiased Finite Impulse Response filters in the standard version and modified for CMN to demonstrate the filter with the best performance. To test the most robust filter we use a high coloredness factor. The tests were carried out with simulated data (ideal and no ideal conditions) and with benchmark data (no ideal conditions). The UFIR modified for the CMN algorithm showed favorable results with high precision and low RMSE in the object tracking process with benchmark data and under no ideal conditions. While KF CMN showed better results under ideal conditions.

Continuous‐time optimal unbiased FIR filter for input‐delayed systems

ABSTRACTMost existing finite impulse response (FIR) filters are restricted to models without delays. This paper proposes an continuous‐time optimal unbiased FIR filter for input‐delayed systems (CTOUFFID). A new integral transformation relation was introduced to derive the FIR filter. By applying this relation, the CTOUFFID problem is represented as an optimal control problem with zero terminal state. The filter gain function is obtained by solving two coupled matrix differential equations using initial conditions. The paper also offers discussion on the horizon size and a few special cases. The main benefit of the proposed solution is that it provides the maximum likelihood estimate with no requirements for the initial values. Finally, an application with the ‐404 turbofan engine model is presented to demonstrate the highly robust nature of the proposed FIR filer against incomplete noise information and unspecified model uncertainties.

Terrestrial Paths Estimation by using Satellite Tracking with lost data

Autonomous vehicles navigation requires previously plotted paths to avoid deviations or collisions with other objects. One of the main problems is the loss of location data when specific paths are designed due to disturbances in the acquisition devices. The most used technology for geo-position registration is the Global Positioning System (GPS) due to its great precision and low cost. However, the advantages of this technique for designing navigation paths are overshadowed if there is a significant loss of information. In this work, it is proposed to use a modified Iterative Unbiased Finite Impulse Response (I-UFIR) algorithm to estimate the GPS data lost. The satellite measurements are obtained with the G28U7FTTL receptor, and the lost measurements are estimated in different sections of a specific path in an urban environment. The results are corroborated by using a real map and the mean square error based on a referenced path.

P-step Kalman-like unbiased FIR algorithm for detecting anomalies of atomic clocks

An extended measurement model anomaly detection algorithm for atomic clocks is proposed based on the p-step Kalman-like iterative Unbiased finite impulse response (UFIR) algorithm. The novel of the proposed algorithm is that it breaks the disadvantage of initializing the noise matrix in the traditional Kalman filter improved algorithms. By accumulating the prediction residuals generated by the process of p-step Kalman-like iterative, the detection statistics are constructed to realize the weaker phase and frequency jump detection of atomic clocks. Simulation results show the effectiveness of the proposed algorithm, and Monte-Carlo (M-C) experiment is used to verify the theoretical calculation of detection probability. We also analyze the measured data of a cesium atomic clock in our timekeeping laboratory, and the results show that the new method is more sensitive to weak frequency jump than the improved algorithm based on the Kalman filter.

Comparison of the Kalman Filter and the Unbiased FIR Filter for Network Systems with Multiples Output Delays and Lost Data

In this article, a comparison of the UFIR and Kalman filter to estimate a tracking vehicle system variables is developed considering two possible observation output models. The time stamp approach and the predictive compensation are used to analyze the problem from multiple perturbations, which produces random delayed data and losses during transmissions. For the estimation, a transformation model and a decorrelation covariance matrices are developed with the aim of assure optimal conditions and minimizing the estimation error. Finally, several real situations, miss modeling, uncertain noise covariances, and uncertain probabilities are proposed to demonstrate the effectiveness and robustness of the filter proposed.

Uncertain disturbed systems: Robust H2‐OUFIR filtering under measurement errors

SummaryThis article considers uncertain disturbed systems represented under initial and measurement errors in linear discrete‐time state‐space. A novel bias‐constrained a posteriori optimal unbiased finite impulse response (OUFIR) state observer is derived, in which the requirements of the initial state is removed by embedding the unbiasedness constraint. A suboptimal bias‐constrained ‐FIR filtering algorithms using linear matrix inequality is also obtained. Based on an example of uncertain system and error matrices, it is shown that the ‐OUFIR state observer offers a better tradeoff between the accuracy and robustness than the OFIR filter, maximum likelihood FIR filter, and Kalman filter. It loses in robustness to the unbiased FIR filter, but provides a better accuracy. The performance of the proposed filter is verified experimentally based on daily glucose measurements in diabetic persons with timing jitter.