GPS latency identification by Kalman filtering

In this paper, we present a novel evidence theoretic fusion filter, and its application to the Unmanned Ground Vehicle (UGV) localization problem. The various components of the sensor fusion framework such as the adaptive pre-processing unit, the evidence extraction and combination unit, and the extended Kalman filter are described in detail. The crux of this architecture is the evidence extraction and combination unit that employs a twopronged approach, one to switch between parametric models, and another to adaptively vary the measurement noise covariance matrix. The process of evidence extraction using fuzzy-type or rule-based techniques, and their subsequent combination using the Dempster's rule for combination are detailed. An experiment is conducted to demonstrate the merits of this UGV localization approach. Finally, we conclude with a brief summary of the results.

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“…(24) Then we validate the measurement using the squared Mahalanobis distance ( M d k ) criterion [4] as given below:…”

Section: Extended Kalman Filteringmentioning

confidence: 99%

¿-Filter: An Evidence Theoretic Approach to Unmanned Ground Vehicle Localization

Vibeeshanan¹,

Subbarao

Huff³

2007

2007 International Symposium on Computational Intelligence in Robotics and Automation

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“…Yet, even with GPS, x success rate can drop dramatically and locational accuracy can easily vary by a few metres or more, depending on the propagation of signal quality and/or receiver reception capability [29,30]. In addition, these units can be subject to latency delays by up to ~ 5 s [31,32], whilst most commercial loggers are only precise to around 1 m [22] and so, irrespective of x accuracy, time-based positional error can accumulate (as a function of sampling rate) when the spatial resolution of animal movement is less than the precision error radius between consecutive readings.…”

Section: Introductionmentioning

confidence: 99%

How Often Should Dead-Reckoned Animal Movement Paths be Corrected for Drift?

Gunner

Holton

Scantlebury

et al. 2021

Preprint

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BackgroundUnderstanding what animals do in time and space is important for a range of ecological questions, however accurate estimates of how animals use space is challenging. Within the use of animal-attached tags, radio telemetry (including the Global Positioning System (GPS)) is typically used to verify an animal’s location periodically. Straight lines are typically drawn between these ‘Verified Positions’ (VPs) so the interpolation of space-use is limited by the temporal- and spatial resolution of the system’s measurement. As such, parameters such as route-taken and distance travelled can be poorly represented when using VP systems alone. Dead-reckoning has been suggested as a technique to improve the accuracy and resolution of reconstructed movement paths, whilst maximising battery life of VP systems. This typically involves deriving travel vectors from motion sensor systems and periodically correcting path dimensions for drift with simultaneously deployed VP systems. How often paths should be corrected for drift, however, has remained unclear.Methods & ResultsHere, we review the utility of dead-reckoning across four contrasting model species using different forms of locomotion (the African lion Panthera leo, the Red-tailed tropicbird Phaethon rubricauda, the Magellanic penguin Spheniscus magellanicus, and the Imperial cormorant Leucocarbo atriceps). Simulations were performed to examine the extent of dead-reckoning error, relative to VPs, as a function of Verified Position correction (VP correction) rate and the effect of this on estimates of distance moved. Dead-reckoning error was greatest for animals travelling within air and water. We demonstrate how sources of measurement error can arise within VP-corrected dead-reckoned tracks and propose advancements to this procedure to maximise dead-reckoning accuracy.ConclusionsWe review the utility of VP-corrected dead-reckoning according to movement type and consider a range of ecological questions that would benefit from dead-reckoning, primarily concerning animal-barrier interactions and foraging strategies.

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“…Examples of these fields include the process control industry, 9 the visual tracking applications 27,28 and the autonomous localization and navigation. 10,12,13 The paper is organized as follows. Section 2 presents the mathematical background on which the KF-based and PF-based filtering approaches rely.…”

Section: Introductionmentioning

confidence: 99%

A comparison of Bayesian prediction techniques for mobile robot trajectory tracking

2008

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SUMMARYThis paper presents a performance comparison of different estimation and prediction techniques applied to the problem of tracking multiple robots. The main performance criteria are the magnitude of the estimation or prediction error, the computational effort and the robustness of each method to non-Gaussian noise. Among the different techniques compared are the well-known Kalman filters and their different variants (e.g. extended and unscented), and the more recent techniques relying on Sequential Monte Carlo Sampling methods, such as particle filters and Gaussian Mixture Sigma Point Particle Filter.

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GPS latency identification by Kalman filtering

Cited by 10 publications

References 9 publications

¿-Filter: An Evidence Theoretic Approach to Unmanned Ground Vehicle Localization

¿-Filter: An Evidence Theoretic Approach to Unmanned Ground Vehicle Localization

How Often Should Dead-Reckoned Animal Movement Paths be Corrected for Drift?

A comparison of Bayesian prediction techniques for mobile robot trajectory tracking

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