Ocean Sensor Node Positioning with Selective Particle Filtering
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Solution Overview
Problem
Existing particle filter methods for ocean sensor networks face challenges in balancing positioning accuracy and efficiency, leading to high resource consumption and reduced network lifespan due to energy depletion in nodes deployed in dynamic and uncertain marine environments.
Innovation Solution
An efficient particle filter method that includes establishing a state model based on ocean motion characteristics, using signal intensity attenuation models, and applying Taylor series expansion with multidimensional Richardson extrapolation to improve accuracy and efficiency, along with resampling and linear interpolation to modify particle weights and reduce energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of particles is increased to improve positioning accuracy, then positioning accuracy is improved, but positioning efficiency is greatly reduced and energy consumption increases
Solution Approach 1:
The patent extracts and processes only the most critical particles (large-weight particles) while simplifying or eliminating processing of less important particles (small-weight particles). This selective processing approach maintains positioning accuracy by preserving the most informative particles while reducing overall computational load and energy consumption.
Solution Approach 2:
The patent applies different processing strategies to different subsets of particles based on their weight characteristics. Large-weight particles receive full processing attention to maintain accuracy, while small-weight particles receive simplified processing. This local differentiation resolves the contradiction by optimizing resources according to the actual contribution of each particle subset.
2Measurement precision
If the number of particles is increased to improve positioning accuracy, then positioning accuracy is improved, but computing resource consumption increases
Solution Approach 1:
The patent extracts and processes only the most critical particles (large-weight particles) while simplifying or eliminating processing of less important particles (small-weight particles). This selective processing approach maintains positioning accuracy by preserving the most informative particles while reducing overall computational load and energy consumption.
Solution Approach 2:
The patent applies different processing strategies to different subsets of particles based on their weight characteristics. Large-weight particles receive full processing attention to maintain accuracy, while small-weight particles receive simplified processing. This local differentiation resolves the contradiction by optimizing resources according to the actual contribution of each particle subset.
3Measurement precision
If the number of particles is increased to improve positioning accuracy, then positioning accuracy is improved, but energy consumption increases
Solution Approach 1:
The patent extracts and processes only the most critical particles (large-weight particles) while simplifying or eliminating processing of less important particles (small-weight particles). This selective processing approach maintains positioning accuracy by preserving the most informative particles while reducing overall computational load and energy consumption.
Solution Approach 2:
The patent applies different processing strategies to different subsets of particles based on their weight characteristics. Large-weight particles receive full processing attention to maintain accuracy, while small-weight particles receive simplified processing. This local differentiation resolves the contradiction by optimizing resources according to the actual contribution of each particle subset.
Data Source
AI summary
The application relates to an efficient particle filter node positioning method for ocean sensor networks, including the following steps: 1) obtaining a first state vector of mean value of posterior probability at k−1 moment; 2) establishing the state model of the nonlinear system; 3) obtaining the observation model of the nonlinear system; 4) propagating particles, and calculating the state vector ū+k at the next moment according to the particle vector at k−1 moment; 5) obtaining the observed value and calculating the weight; 6) judging whether it is degraded, if not, the weight is used to calculate the position expectation, otherwise, 7) is executed; 7) modifying small-weight particles; 8) resampling the improved residual and normalizing the weights; and 9) using the weight of 8) to calculate the position expectation and realize node positioning.