A Combinational Underwater Aided Navigation Algorithm Based on TERCOM/ICCP and Kalman Filter

Yuan, Gannan; Zhang, Hongwei; Yuan, Kefei; Tao, Chunyan

doi:10.1109/cso.2011.23

Cited by 4 publications

(3 citation statements)

References 5 publications

(3 reference statements)

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“…We employed the Extend Kalman Filter (EKF) (Zhang et al, 2011; Yuan et al, 2011; Gyung and Hang, 2006) to integrate the matching solution and the INS position information. So there are three kinds of integrated navigation results, such as ICCP/INS, Triangle/INS (TRI/INS) and SITAN/INS, otherwise the integrated navigation result of GPS/INS is used as a reference.…”

Section: Methodsmentioning

confidence: 99%

A Triangle Matching Algorithm for Gravity-aided Navigation for Underwater Vehicles

2013

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In this paper, a triangle matching algorithm using local gravity field maps is proposed to bound the drift errors inherent in Strapdown Inertial Navigation Systems (SINS) in gravity-aided navigation. This triangle matching algorithm has two main stages, the first is the initial matching stage, which has a coarse phase and a fine phase to address the large unknown initial errors made by INS, and the other is the tracking matching stage, which mainly aims at tracking the matching solution with the vehicle running in real time. Simulations were carried out using data for the Bohai Sea and South China Sea areas, to assess the effects of different initial errors on the matching solutions. Finally some experiments were carried out to evaluate the proposed algorithm. The results show that the triangle matching algorithm has some compelling advantages, such as a capability to address the large unknown initial errors made by INS, and good real-time quality of matching the gravity measurements with the local gravity maps.

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Section: Methodsmentioning

confidence: 99%

A Triangle Matching Algorithm for Gravity-aided Navigation for Underwater Vehicles

2013

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“…Xu et al 13 proposed a new PSO-ICCP that effectively improved the matching accuracy. Yuan et al 14 combined the matching results of terrain contour matching (TERCOM) and ICCP. The difference between the two matching results was input to a Kalman filter to reduce the matching error.…”

Section: Introductionmentioning

confidence: 99%

Underwater terrain-matching algorithm based on improved iterative closest contour point algorithm

Wang,

Liu,

Ben

et al. 2024

Measurement and Control

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Although an autonomous underwater vehicle (AUV) is noted for its good autonomy, concealment and anti-interference ability, errors in its inertial navigation system (INS) inevitably increase over time, leading to positional failure during long-term voyages. Terrain-assisted navigation can help the INS to correct its position. The traditional iterative closest contour point (ICCP) achieves high matching accuracy when the initial position error of the INS is small, but is prone to mismatching when the initial error is large. This study combines ICCP with particle swarm optimization (PSO) to overcome this problem. First, the global optimization ability of PSO is improved by changing the acceleration factor and introducing an artificial bee colony (ABC) onlooker bee greedy search (ABC- ωAPSO). Second, the Euclidean distance of ICCP is replaced by the Mahalanobis distance to abate the influence of system error on the matching accuracy. Finally, the initial position error is reduced by rough matching using the ABC- ωAPSO, which has global optimization capability. Next, fine matching is performed by ICCP. This two-step process resolves the sensitivity problem of ICCP to the initial position error. The experimental results revealed a good matching effect after the double-matching procedure. When the initial INS errors were 0.55′ to the east and 0.55′ to the north, the matching error was reduced to 89.3 m, suggesting that the approach can realize autonomous passive navigation of AUVs.

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“…On the other hand, for enhancing the matching efficiency of TERCOM, Yuan et al [20] constructed a combined underwater aided navigation method by coupling TERCOM/ICCP with the Kalman filter, and they adopted the sliding window to improve its matching efficiency. Li et al [21] raised the searching model with a hierarchical neighborhood threshold by using the initial searching of four-grid intervals and the re-matching strategy of the neighborhood 24-grid points around its rough-optimal matching point; as a result, they were able to improve the efficiency of the point-by-point traversal search of TERCOM.…”

Section: Introductionmentioning

confidence: 99%

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

Zhao

Zheng

et al. 2022

Remote Sensing

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This paper mainly studies the improvement of the efficiency and out-of-domain reliability of gravity matching navigation for underwater vehicles. To overcome the traversal low-efficiency problem of the traditional terrain contour matching (TERCOM) algorithm and improve the positioning reliability of its out-of-domain mismatches, a novel soft-margin local semicircular-domain re-searching model (SLSR) is proposed by integrating the soft-margin circular grid matching (SCGM) mechanism and the local semicircular grid re-matching (LSGR) mechanism. SCGM uses three times the inertial navigation cumulative error and adds the unit grid resolution as the soft margin boundary to generate the soft-margin circular domain, which contributes to reducing in-domain matching grid points and enhancing the matching efficiency of algorithms. Then the optimal matching position in this soft-margin circular domain is obtained by using the optimization principle of matching indices. LSGR is triggered when the optimal matching position of SCGM is located near the soft-margin circular-domain boundary. It employs this optimal matching point as the center and the unit inertial navigation error as the radius to recreate the semicircular local re-searching matched grid domain (termed as semicircular domain). Moreover, the optimal matching point in this semicircular domain is obtained by the matching index optimization principle, and then it is compared and updated to obtain the final best matching position of SLSR. The simulation results show that SCGM and LSGR of the proposed SLSR method can effectively improve the matching efficiency and out-of-domain matching reliability of underwater navigation, respectively. Under the same testing conditions for the tracking starting points from three gravity regions, the number of out-of-domain mismatches of SLSR, compared with TERCOM, are lower up to 92.68%, 90.24% and 98.62%, while the average matching accuracies are relatively improved by 88.37%, 85.48% and 83.66%, which verifies the validity and feasibility of the proposed SLSR model on improving the efficiency and out-of-domain reliability of underwater gravity matching navigation.

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A Combinational Underwater Aided Navigation Algorithm Based on TERCOM/ICCP and Kalman Filter

Cited by 4 publications

References 5 publications

A Triangle Matching Algorithm for Gravity-aided Navigation for Underwater Vehicles

A Triangle Matching Algorithm for Gravity-aided Navigation for Underwater Vehicles

Underwater terrain-matching algorithm based on improved iterative closest contour point algorithm

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

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