A Novel Particle Filter Formulation with Application to Terrain-Aided Navigation

Teixeira, Francisco Curado; Pascoal, A.; Maurya, Pramod Kumar

doi:10.3182/20120410-3-pt-4028.00023

Cited by 29 publications

(16 citation statements)

References 8 publications

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“…In what follows we borrow from Teixeira et al (2012a) and Teixeira et al (2012b) the formulation of the process and measurement models which are reproduced here with minor adaptations. The models are simplified in the present case, since the problem does not include the estimation of the velocity bias introduced by oceanic currents; it is assumed that a DVL operating in bottom-lock supplies the velocities relative to the seabed.…”

Section: Stochastic Models and Filter Set-upmentioning

confidence: 99%

“…See filters configurations parameters in Table 3. A detailed analysis of the performance of different PF algorithms applied to the TAN problem, can be found in Teixeira (2007) and Teixeira et al (2012a).…”

Section: Tan Filters Configurationsmentioning

confidence: 99%

“…The solution proposed, be- sides using the Doppler-based velocity measurements for dead-reckoning, exploits the ability of the DVL to acquire periodically a set of altitude or slant-range measurements that are used by TAN algorithms for terrain matching. The implementation reported relies on a new particle filter algorithm, the Prior-correction Particle Filter (PPF) described in Teixeira et al (2012a) and uses a complementary filter (CF) to fuse the TAN estimates with the deadreckoning data. The paper shows the advantages of using the novel particle filter and the CF data fusion mechanism in TAN applications; the performance of the methods proposed is illustrated in computer simulations using real bathymetry and simulated DVL data.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

AUV Terrain-Aided Navigation using a Doppler Velocity Logger★

Teixeira¹,

Quintas²,

Pascoal³

2015

IFAC-PapersOnLine

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The potential of terrain-aided navigation (TAN) of autonomous underwater vehicles (AUVs) has been demonstrated in the last ten years in a series of trials where high localization accuracy was obtained during long periods of time in different types of terrain. Despite these recent advances, it is recognized by the navigation community that further research is necessary to transform TAN into a mature navigation methodology. The present paper describes the work developed towards the development of terrain navigation methods for small AUVs, relying on standard navigation sensors and dispensing with the need of dedicated sensors for terrain data acquisition. The research described addresses the problem of implementation of terrain navigation in underwater scenarios characterized by smooth sea-bottom topography and very shallow water, where the terrain information available for navigation is scarce. The TAN algorithms and the data fusion methods whose tests are documented in the paper build upon prior theoretical work published by the authors. The experimental results reported were obtained recently through trials in the water performed with an autonomous surface vehicle acting as a proxy to an AUV.

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Section: Stochastic Models and Filter Set-upmentioning

confidence: 99%

Section: Tan Filters Configurationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

AUV Terrain-Aided Navigation using a Doppler Velocity Logger★

Teixeira¹,

Quintas²,

Pascoal³

2015

IFAC-PapersOnLine

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“…The MPF has been employed in Nordlund and Gustafsson (2001), in an integrated navigation system of an aircraft with a state vector of more than 15 dimensions, and simulation results showed good performance with a much lower computational load. In the domain of underwater navigation, the authors in Teixeira et al (2012a) have shown the feasibility of applying the MPF for an AUV with the number of particles as low as 500 and manage to achieve good localization. The results have encouraged the application of MPF-based localization techniques in low-cost, limited computational-power AUVs.…”

Section: Bathymetry-based Localizationmentioning

confidence: 99%

“…This poses a challenge for the AUVs' limited computational power onboard. In order to alleviate this, a number of researchers have adopted an approach called the Marginalized Particle Filter (MPF), also referred to as Rao-Blackwellization (Doucet et al 2000;Nordlund and Gustafsson 2001;Schon et al 2005;Fairfield et al 2006;Barkby et al 2011;Teixeira et al 2012a). The idea behind the MPF is to marginalize the system states that exhibit linear dynamics, and to estimate the marginalized states using a Kalman Filter.…”

Section: Bathymetry-based Localizationmentioning

confidence: 99%

Cooperative bathymetry-based localization using low-cost autonomous underwater vehicles

Tan

Hover

2015

Auton Robot

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We present a cooperative bathymetry-based localization approach for a team of low-cost autonomous underwater vehicles (AUVs), each equipped only with a single-beam altimeter, a depth sensor and an acoustic modem. The localization of the individual AUV is achieved via fully decentralized particle filtering, with the local filter's measurement model driven by the AUV's altimeter measurements and ranging information obtained through inter-vehicle communication. We perform empirical analysis on the factors that affect the filter performance. Simulation studies using randomly generated trajectories as well as trajectories executed by the AUVs during field experiments successfully demonstrate the feasibility of the technique. The proposed cooperative localization technique has the potential to prolong AUV mission time, and thus open the door for long-term autonomy underwater.

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Robust particle filter formulations with application to terrain‐aided navigation

Teixeira

Quintas

Maurya

et al. 2016

Adaptive Control & Signal

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Summary The work described in this paper is motivated by the need to develop efficient and robust estimation filters with application to terrain‐aided navigation of underwater robotic vehicles. One of the main problems addressed is the development of navigation particle filters that can deal with the scarcity of landmarks and the terrain ambiguity that characterize vast regions of the ocean floor. As a contribution to solve this problem, the paper proposes three novel particle filter algorithms and assesses their estimation efficiency and robustness to non‐informative measurements using two well‐known benchmarking tests. The performance of the new filters in these tests demonstrates their potential to solve a class of nonlinear problems that include, but are not limited to, the type of underwater navigation problem that motivated the present work. Our study concludes by examining the performance of the filters in terms of determining the position and velocity of an autonomous underwater vehicle in the presence of unknown ocean currents. When applied to terrain‐aided navigation, the novel particle filter formulations formulations mitigate filter divergence issues frequently caused by terrain symmetries and are more robust than other well‐known versions when used in scenarios with poor terrain information. The theoretical developments presented and the results obtained in simulations are validated using real data acquired during tests with an autonomous marine robot. Copyright © 2016 John Wiley & Sons, Ltd.

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A Novel Particle Filter Formulation with Application to Terrain-Aided Navigation

Cited by 29 publications

References 8 publications

AUV Terrain-Aided Navigation using a Doppler Velocity Logger★

AUV Terrain-Aided Navigation using a Doppler Velocity Logger★

Cooperative bathymetry-based localization using low-cost autonomous underwater vehicles

Robust particle filter formulations with application to terrain‐aided navigation

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