Efficient Point-Mass Filter Design for Terrain-Aided Navigation

Temel Kavramlar

Efficient point-mass filter design for terrain-aided navigation improves accuracy and reduces computational complexity.

Özet

The content discusses the design of an efficient point-mass filter for state estimation in stochastic models with linear dynamics. It introduces a novel efficient PMF (ePMF) estimator that unifies continuous and discrete approaches, reducing time complexity significantly without compromising accuracy. The paper also provides MATLAB® code for the ePMF implementation. The article covers various models, grid movements, and algorithms to enhance the efficiency of the point-mass filter.

I. Introduction

• State estimation of stochastic dynamic systems from noisy measurements.
• Bayesian recursive relations computing probability density functions.
• Approximate filtering methods divided into global and local filters.

II. Terrain Aided Navigation and State Estimation

• Utilization of terrain altitude measurements for navigation.
• Presentation of continuous and discrete dynamics models.

III. Point-Mass Filter

• Approximation of conditional PDF by piece-wise constant point-mass density.
• Basic algorithm steps for the Point-Mass Filter.

IV. Efficient Point-Mass Prediction

• Transformation of time-update to convolution for computational efficiency.
• Use of Fast Fourier Transform (FFT) and Fast Sine Transform (FST) based solutions.

V. Efficient Point-Mass Filter Design

• Grid movement compensation to address degeneracy issues.
• Design considerations for continuous ePMF with non-diagonal noise.

VI. Enclosed MATLAB® Codes

• Availability of MATLAB® codes on IDM research team's website.

VII. Numerical Results

Two-Dimensional Estimation Results:

• Comparison between PMF, FFT-based PMF, FST-based PMF, and PF bootstrap.

Four-Dimensional Estimation Results:

• Comparison between FFT-based PMF and PF bootstrap in four dimensions.

VIII. Concluding Remarks

• Proposal of an efficient point-mass filter design for terrain-aided navigation.

İstatistikler

The proposed ePMF can lead to a time complexity reduction exceeding 99.9% without compromising accuracy.

Alıntılar

"The proposed ePMF can now compete with PF in both accuracy and computational complexity."

Önemli Bilgiler Şuradan Elde Edildi

Design of Efficient Point-Mass Filter with Application in Terrain Aided Navigation

by J. M... : arxiv.org 03-21-2024

https://arxiv.org/pdf/2303.05100.pdf

Daha Derin Sorular

How does the proposed ePMF algorithm compare to other state estimation methods

The proposed ePMF algorithm offers significant improvements in computational efficiency compared to other state estimation methods. By transforming the time-update step into a convolution operation, the ePMF algorithm reduces the computational complexity of the point-mass filter, making it more competitive with particle filters (PF) in terms of accuracy and speed. The use of fast Fourier transform (FFT) and fast sine transform (FST) allows for efficient calculation of predictive weights, leading to faster processing times without compromising accuracy.

What are the implications of ignoring state noise influence in grid movement compensation

Ignoring state noise influence in grid movement compensation can lead to issues related to degeneracy in the filtering process. Grid movements based solely on state dynamics may not adequately account for uncertainties introduced by state noise, potentially resulting in suboptimal estimates. This can affect the robustness and reliability of the estimation process, as inaccuracies in grid placement may lead to biased or inconsistent results.

How can the efficiency improvements in the point-mass filter impact real-world applications beyond navigation

The efficiency improvements achieved through the ePMF algorithm have broad implications for real-world applications beyond navigation. In fields such as autonomous systems, robotics, finance, and healthcare where accurate state estimation is crucial, faster and more computationally efficient algorithms like ePMF can enhance decision-making processes. For example, in autonomous vehicles or robotic systems requiring real-time localization and mapping capabilities, efficient point-mass filtering can improve responsiveness and overall system performance. Similarly, in financial modeling or medical diagnostics where timely and accurate predictions are essential, faster state estimation methods can streamline analysis workflows and improve outcomes.