A Real-Time Mismatch Detection Method for Underwater Database-Referenced Navigation

Abstract

Database-referenced navigation (DBRN) using geophysical information is often implemented on autonomous underwater vehicles (AUVs) to correct the positional errors of the inertial navigation system (INS). The matching algorithm is a pivotal technique in DBRN. However, it is impossible to completely eliminate mismatches in practical application. Therefore, it is necessary to perform a mismatch detection method on the outputs of DBRN. In this paper, we propose a real-time triple constraint mismatch detection method. The proposed detection method is divided into three modules: the model fitting detection module, the spatial structure detection module, and the distance ratio detection module. In the model fitting detection module, the navigation characteristics of AUVs are used to select the fitting model. In the spatial structure detection module, the proposed method performs the mismatch detection based on the affine transformation relationship between the INS-indicated trajectory and the corresponding matched trajectory. In the distance ratio detection module, we derive the distance ratio constraint between the INS-indicated trajectory and the corresponding matched trajectory. Simulations based on an actual geomagnetic anomaly base map have been performed for the validation of the proposed method.

A Pheromone-Inspired Monitoring Strategy Using a Swarm of Underwater Robots

“…Field experiments have verified that robots are able to exchange large volumes of data in a short time by visiting a communication node. It is worth mentioning that, as the positions of communication nodes are fixed, an underwater robot can be navigated to a node by affordable methods, such as TRN [23], DBRN [24] and GAN [25].…”

“…The network consists of a set of underwater communication nodes. There are various underwater navigation methods—such as Terrain-Referenced Navigation (TRN) [23], Database-Referenced Navigation (DBRN) [24] and Gravity Aided Navigation (GAN) [25]—for an underwater robot to periodically visit the nodes to exchange information and charge batteries if needed.We apply a pheromone-based controller to coordinate a swarm to monitor marine environment and search for static targets on the seafloor. The controller is composed of two layers: the layer of virtual pheromone and the layer of behavior laws.…”

“…The network consists of a set of underwater communication nodes. There are various underwater navigation methods—such as Terrain-Referenced Navigation (TRN) [23], Database-Referenced Navigation (DBRN) [24] and Gravity Aided Navigation (GAN) [25]—for an underwater robot to periodically visit the nodes to exchange information and charge batteries if needed.…”

Improving Matching Efficiency and Out-of-domain Reliability of Underwater Gravity Matching Navigation Based on a Novel Soft-margin Local Semicircular-domain Re-searching Model

“…Han et al [14] pointed out that TERCOM mismatching may be caused by large resolution from the reference map and the gravity anomaly distribution uncertainty and proposed a TERCOM mismatching diagnosis model with Restricted Spatial Order Constraints (RSOCs) algorithm. Dai et al [16] believed that TERCOM and other matching algorithms will have a high probability of mismatches when features are smooth. Wang et al [17] pointed out that the cross-correlation (COR) may lead to mismatching to a certain extent, while the mean square difference (MSD) is an effective correlation evaluation index.…”