Gravity-assisted navigation route planning method

Abstract

The invention relates to a gravity-assisted navigation route planning method. The gravity-assisted navigation route planning method is technically characterized by comprising the following steps: step1, acquiring a gravity anomaly background image of an adaptive region and an error standard deviation gm thereof through investigation and survey; investigating and surveying performance parameters of all configuration equipment of an inertia / gravity combined navigation system; step 2, finishing configuration and traversing of various possible random routes by utilizing a searching-traversing route algorithm; step 3, calculating a gravity measurement error standard deviation gc and a matching duration inertial guidance positioning error diffusion value delta of all alternative routes; step 4,calculating pre-estimating matching and positioning precision of all the alternative routes, wherein the alternative route with the highest matching and positioning precision is used as a planned route in the adaptive region. According to the gravity-assisted navigation route planning method provided by the invention, an underwater gravity measurement process is simulated by utilizing a gravity measurement error model, and a working condition of underwater navigation of a carrier is stimulated by utilizing an inertial guidance positioning error resolving method; an underwater matching workingcondition can be realized, so that the reliability of the screened paths is ensured.

Description

technical field [0001] The invention belongs to the technical field of inertial / gravity combined navigation, and relates to a track planning method, in particular to a gravity-assisted navigation track planning method. Background technique [0002] Gravity-assisted navigation track planning refers to selecting a track suitable for inertial / gravity integrated navigation in the ocean, and the integrated navigation system performs matching and positioning calculations on this track, which can give high-precision position information, thereby Realize the readjustment and correction of inertial navigation. [0003] At present, commonly used track planning methods such as the A* algorithm mostly use water depth data and gravity characteristic statistics as evaluation criteria for track selection. Most of these algorithms use artificially set empirical indicators as the thresholds of evaluation criteria, which are highly random and cannot guarantee the matching and positioning acc...

AI Technical Summary

Problems solved by technology

Most of these algorithms use artificially set empirical indicators as the threshold of evaluation criteria, which are highly random and cannot guarantee the matching and positioning accuracy of the screened tracks, resulting in poor reliability.

Moreover, the quantitative relationship between the matching positioning accuracy and the gravity characteristic statistics cannot be given, and the intuition is poor

Images