Reinforced Lattice Kalman Filters: A Robust Nonlinear Estimation Strategy

Rahimnejad, Abolfazl; Enayati, Javad; Vanfretti, Luigi; Gadsden, S. Andrew; Al-Shabi, Mohammad

doi:10.1109/ojsp.2023.3298555

Cited by 14 publications

(2 citation statements)

References 33 publications

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“…The term "Monte Carlo method" is used to embrace a wide range of problem-solving techniques which use random numbers in input to produce the output statistics [32][33][34]. Examples of MC methods in science are classical MC (drawing samples from distributions to determine the desired output), quantum MC (used in physics), and simulation MC (algorithms that evolve configurations depending on predefined models).…”

Section: The Basic Principles Of Monte Carlo Simulationmentioning

confidence: 99%

A Monte Carlo-Based Iterative Extended Kalman Filter for Bearings-Only Tracking of Sea Targets

Edrisi,

Enayati,

Rahimnejad

et al. 2024

Sensors

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In this paper, a Monte Carlo (MC)-based extended Kalman filter is proposed for a two-dimensional bearings-only tracking problem (BOT). This problem addresses the processing of noise-corrupted bearing measurements from a sea acoustic source and estimates state vectors including position and velocity. Due to the nonlinearity and complex observability properties in the BOT problem, a wide area of research has been focused on improving its state estimation accuracy. The objective of this research is to present an accurate approach to estimate the relative position and velocity of the source with respect to the maneuvering observer. This approach is implemented using the iterated extended Kalman filter (IEKF) in an MC-based iterative structure (MC-IEKF). Re-linearizing dynamic and measurement equations using the IEKF along with the MC campaign applied to the initial conditions result in significantly improved accuracy in the estimation process. Furthermore, an observability analysis is conducted to show the effectiveness of the designed maneuver of the observer. A comparison with the widely used UKF algorithm is carried out to demonstrate the performance of the proposed method.

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Section: The Basic Principles Of Monte Carlo Simulationmentioning

confidence: 99%

A Monte Carlo-Based Iterative Extended Kalman Filter for Bearings-Only Tracking of Sea Targets

Edrisi,

Enayati,

Rahimnejad

et al. 2024

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Its prowess in managing uncertain and noisy data renders it indispensable in scenarios requiring dynamic system tracking and prediction [10][11][12][13][14][15][16][17][18][19][20]. The Kalman Filter's applications are diverse: it's pivotal in navigation systems for determining the position and velocity of moving objects, utilizing GPS and various sensors ; in robotics, it estimates robots' positions and orientations by integrating data from cameras and inertial units ; it refines weather forecasting by amalgamating data from satellites and meteorological stations [71][72][73][74][75][76][77][78][79][80][81][82][83][84][85][86][87][88]; and in financial markets, it forecasts asset prices by analyzing historical data and market trends [89][90][91][92][93][94][95]. This versatility and robustness underscore the Kalman Filter's essential role across numerous fields.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the extended Kalman filter's role in oceanic science

Obaideen,

AlShabi,

Bonny

2024

Ocean Sensing and Monitoring XVI

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This study delves into the Extended Kalman Filter's (EKF) use in ocean science through a detailed bibliometric and text mining examination. Tracing its roots back to the original Kalman Filter from the 1960s, the EKF has become crucial for managing nonlinear dynamics, especially in oceanography. Our analysis, drawing from Scopus data covering 1980-2023, delivers an extensive overview of the EKF's growth, applications, and cross-disciplinary influence in this area. We employed sophisticated bibliometric methods, including Biblioshiny, and text mining approaches via VOSviewer to dissect trends, and thematic groupings in EKF-related ocean science research. The results demonstrate a steady increase in EKF applications, particularly in autonomous underwater vehicle navigation, forecasting ocean currents, and modeling marine ecosystems. The bibliometric findings show its broad interdisciplinary appeal, while the text analysis underscores the EKF's integration with cutting-edge computational techniques and its significance in burgeoning oceanographic technologies. The paper highlights the EKF's indispensable role in ocean science, reflecting its historical importance and versatility in addressing contemporary challenges in marine technology. The study not only sheds light on the EKF's historical and current uses but also suggests potential future directions for research and innovation. It aims to offer crucial insights to researchers, academicians, and policy makers, underlining the EKF's significance in the dynamic, ever-changing realm of ocean science.

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Nonlinear filtering using the double exponential transformation

Butler,

Sicard,

Hilal

et al. 2024

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIII

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Nonlinear Gaussian filters have traditionally used cubature rules and/or Gaussian quadrature to compute multidimensional expectation integrals recursively, which provide mean and covariance estimates of the state and state error, respectively. Minimal and near minimal-point filters attain moderate accuracy while avoiding the so-called "curse of dimensionality", but their accuracy can diverge over time. Recent trends in cubature-based filtering have opted for more evaluation points to increase accuracy at the cost of higher computational overhead, while still avoiding the dreaded curse. These methods use more complex and higher-degree cubature rules. The present work, contrary to recent trends, uses a quadrature method other than that of the Gaussian variety. Double exponential quadrature is used to achieve high levels of relative accuracy with a moderate number of evaluation points, rivalling that of current state-of-the-art Gaussian filters and the best-in-class Gauss-Hermite filter.

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Reinforced Lattice Kalman Filters: A Robust Nonlinear Estimation Strategy

Cited by 14 publications

References 33 publications

A Monte Carlo-Based Iterative Extended Kalman Filter for Bearings-Only Tracking of Sea Targets

A Monte Carlo-Based Iterative Extended Kalman Filter for Bearings-Only Tracking of Sea Targets

Analysis of the extended Kalman filter's role in oceanic science

Nonlinear filtering using the double exponential transformation

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