Intelligent self-homing positioning and orientation algorithm based on maximum gradient vector

Abstract

The invention relates to an intelligent self-homing positioning and orientation algorithm based on a maximum gradient vector, which comprises the following steps of: 1, acquiring physical parameters of a gas sampling point to be measured by adopting a space N point (N is greater than or equal to 4), and calculating a maximum gradient vector direction according to the physical parameters of the N point; 2, analyzing the spatial gradient characteristics of the physical parameters of the N points, and determining the adjustment stride by combining the spatial-temporal marginal characteristics of the development in the maximum gradient vector direction in the time dimension; and step 3, until the physical parameters of the N points are equal, verifying whether the physical parameters of the N points are the same in all directions by changing the angle and the direction, so as to realize the autonomous searching of the three-dimensional space physical parameter source point. According to the method, for unknown physical field parameters with distributed potential fields, a reverse or forward trajectory can be propagated really along the maximum gradient, and a potential field generation source or an optimal trajectory separated from the potential field source can be searched autonomously; the spatial-temporal distribution condition of the whole physical parameters does not need to be known in advance, and low-cost rapid intelligent searching is achieved.

Description

technical field [0001] The present invention relates to a technical method for finding the source location of unknown physical space of a specific gas component. Through the multi-point sampling and analysis method with fixed relative positions, the characteristic of gas component diffusion along the maximum gradient is used to determine the best vector direction and its self-seeking method. amount of movement. Background technique [0002] In the physical parameter distribution space system with gradient field characteristics, such as temperature field, electric potential field, pressure field, gas phase component concentration field, etc., the distribution of physical parameters depends on the release source (also called the target source) that generates the gradient field. The location, such as the gas phase component concentration field, depends on the release point of the gas phase component. If the location of the target source is unknown, it is necessary to quickly f...

AI Technical Summary

Problems solved by technology

[0009] In short, at present, there is no fast self-seeking orientation technology at home and abroad under the condition that the physical parameters and their gradient spatial distribution are unknown.

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