A review of Kalman filter with artificial intelligence techniques

Kalman filter (KF) is a widely used estimation algorithm for many applications. However, in many cases, it is not easy to estimate the exact state of the system due to many reasons such as an imperfect mathematical model, dynamic environments, or inaccurate parameters of KF. Artificial intelligence (AI) techniques have been applied to many estimation algorithms thanks to the advantage of AI techniques that have the ability of mapping between the input and the output, the so-called "black box". In this paper, we found and reviewed 55 papers that proposed KF with AI techniques to improve its performance. Based on the review, we categorised papers into four groups according to the role of AI as follows: 1) Methods tuning parameters of KF, 2) Methods compensating errors in KF, 3) Methods updating state vector or measurements of KF, and 4) Methods estimating pseudo-measurements of KF. In the concluding section of this paper, we pointed out the directions for future research that suggestion to focus on more research for combining the categorised groups. In addition, we presented the suggestion of beneficial approaches for representative applications

Multi-Sensor Fusion for Underwater Vehicle Localization by Augmentation of RBF Neural Network and Error-State Kalman Filter

The Kalman filter variants extended Kalman filter (EKF) and error-state Kalman filter (ESKF) are widely used in underwater multi-sensor fusion applications for localization and navigation. Since these filters are designed by employing first-order Taylor series approximation in the error covariance matrix, they result in a decrease in estimation accuracy under high nonlinearity. In order to address this problem, we proposed a novel multi-sensor fusion algorithm for underwater vehicle localization that improves state estimation by augmentation of the radial basis function (RBF) neural network with ESKF. In the proposed algorithm, the RBF neural network is utilized to compensate the lack of ESKF performance by improving the innovation error term. The weights and centers of the RBF neural network are designed by minimizing the estimation mean square error (MSE) using the steepest descent optimization approach. To test the performance, the proposed RBF-augmented ESKF multi-sensor fusion was compared with the conventional ESKF under three different realistic scenarios using Monte Carlo simulations. We found that our proposed method provides better navigation and localization results despite high nonlinearity, modeling uncertainty, and external disturbances.This research was partially funded by the Campus de Excelencia Internacional Andalucia Tech, University of Malaga, Malaga, Spain. Partial funding for open access charge: Universidad de Málag

Integrating GRU with a Kalman filter to enhance visual inertial odometry performance in complex environments

To enhance system reliability and mitigate the vulnerabilities of the Global Navigation Satellite Systems (GNSS), it is common to fuse the Inertial Measurement Unit (IMU) and visual sensors with the GNSS receiver in the navigation system design, effectively enabling compensations with absolute positions and reducing data gaps. To address the shortcomings of a traditional Kalman Filter (KF), such as sensor errors, an imperfect non-linear system model, and KF estimation errors, a GRU-aided ESKF architecture is proposed to enhance the positioning performance. This study conducts Failure Mode and Effect Analysis (FMEA) to prioritize and identify the potential faults in the urban environment, facilitating the design of improved fault-tolerant system architecture. The identified primary fault events are data association errors and navigation environment errors during fault conditions of feature mismatch, especially in the presence of multiple failure modes. A hybrid federated navigation system architecture is employed using a Gated Recurrent Unit (GRU) to predict state increments for updating the state vector in the Error Estate Kalman Filter (ESKF) measurement step. The proposed algorithm’s performance is evaluated in a simulation environment in MATLAB under multiple visually degraded conditions. Comparative results provide evidence that the GRU-aided ESKF outperforms standard ESKF and state-of-the-art solutions like VINS-Mono, End-to-End VIO, and Self-Supervised VIO, exhibiting accuracy improvement in complex environments in terms of root mean square errors (RMSEs) and maximum errors

Caracterización de algoritmos para la predicción de la localización en sistemas empotrados

Proyecto de Graduación (Maestría en Ingeniería en Electrónica) Instituto Tecnológico de Costa Rica, Escuela de Ingeniería Electrónica, 2017.Caracterizar el desempeño de diversos algoritmos para la predicción de la ubicación en sistemas empotrados de bajo costo. La presente investigación tiene como objetivo caracterizar el desempeño de diversos algoritmos para la predicción de la ubicación en sistemas empotrados de bajo costo utilizados para mejorar la precisión de las lecturas de sensores de posicionamiento global. Estos algoritmos son necesarios para minimizar los errores generados por los cambios en la calidad de la señal de los satélites. El análisis de los algoritmos se enfocará en determinar los siguientes factores: Eficacia del algoritmo para reducir el error en la lectura de los sensores Ajuste del algoritmo para un sistema empotrado de bajo costo Para medir la eficacia de los algoritmos se utiliza la diferencia absoluta de la posición leída del GPS, contra la calculada para cada instante del recorrido. En cuanto al análisis de que tan apto es cada algoritmo para su uso en un sistema empotrado de bajo costo se analizan la utilización del CPU, tiempo de respuesta y uso de memoria. Se optimizan los algoritmos mediante la división de los datos por dimensión para reducir el desperdicio de recursos y aislar los ejes para ser implementados de acuerdo con las necesidades de futuros proyectos empotrados. Además, se reduce la carga computacional mediante el uso de modelos matemáticos aproximados para el desplazamiento tal como el movimiento rectilíneo uniformemente acelerado. Así mismo, se define además una interfaz común para que los algoritmos puedan ser comparados de manera correcta y nuevos algoritmos puedan ser estudiados más adelante. Se obtiene una aplicación empotrada que utilizando el filtro de extendido de Kalman logra tener el error por debajo de 1345.93 metros de error para cinco segundos de falla. Este cálculo se realiza en menos de 118s en un Raspberry Pi 2 con una imagen de tamaño mínimo. La misma plataforma logra entrenar 100 iteraciones de tres perceptrones multi capa con 34 neuronas cada uno en un tiempo de 5.45ms. Posterior al entrenamiento se somete el algoritmo a la predicción de la posición, consiguiendo una respuesta en un tiempo no mayor a 250s. La error de la posición calculada promedio para una falla de 5s fue de 73.32 metros.The main objective of this thesis is to characterize m ultiple algorithms for location prediction to improve the precision of global positioning sensors readings. This algorithms are needed to compensate bad signal quality of the necessary satellites. The analysis will be focus on: Effectiveness of the algorithm on reducing the reading error on the positioning sensors Fit-ability of the algorithm for a embedded system In order to measure the e ectiveness of the implemented methods the absolute di erence between the real GPS and the calculated value for each instant of the track. In order the get the t-ability for embedded system of each algorithm CPU usage, response time, and memory usage is determine for them. Every method is optimize by dividing the data per dimension, in order to decrease the resources foot-print of the program to the minimum, and so they could scale correctly for di erent requirements. Also the CPU load is reduced by implementing approximated mathematical models like the uniformly accelerated motion when possible. Lastly an interface is de ned to isolate the algorithm from the main program, this way there is a clean comparison between algorithms and new ones could be study later. Using Extended Kalman lter the embedded application created was able to keep the error below 1345.93 meters for ve seconds GPS failure. Each of this predictions has taken less than 118 s on a Raspberry Pi 2 with a custom OS. The same platform is able to train, over 100 times on the same data, three Multi-Layer perceptron with 34 neurons each under 5.45ms. Once trained the MLP is able to predict within 250 s a position with an average error of 73.32 meters, all this in a ve seconds failure of the GP