Single Range Localization in 3-D: Observability and Robustness Issues

Indiveri, Giovanni; Palma, Daniela De; Parlangeli, Gianfranco

doi:10.1109/tcst.2015.2512879

Cited by 35 publications

(21 citation statements)

References 32 publications

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“…Numerical simulations have shown that the LELbased estimator can be remarkably effective in this context. Specific results are reported in [13].…”

Section: Resultsmentioning

confidence: 99%

“…This paper describes a robust state estimation solution building on a prediction corrector architecture based on the minimization of a suitably defined non-linear cost function. The proposed technique is being employed for underwater robot localization issues as described in [13] where a preliminary description of the filter is provided. The aim of this paper is to thoroughly analyze the filter both from a theoretical and numerical point of view.…”

Section: Introductionmentioning

confidence: 99%

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Output outlier robust state estimation

Palma

Indiveri

2016

Adaptive Control & Signal

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This work addresses state estimation in presence of outliers in observed data. Outlying data and measurements have a most relevant impact in many control and signal processing applications including marine systems related ones: underwater navigation systems exploiting acoustic data, for example, are frequently affected by outlying measurements. Other on-board sensors and devices are likely to produce measurements contaminated by outlier because of the harsh operating conditions of marine systems. Given the general interest for dealing with measurement outliers in a number of applications, this paper describes a state estimation solution exhibiting robustness to output outliers. The system model is assumed to be linear (either time varying or time invariant) discrete time. The proposed observer is designed by extending an outlier robust static parameter identification algorithm to the case of a linear dynamic plant. The designed estimator has a predictor/corrector structure like the Kalman filter and the Luenberger observer. Simulation and experimental results are provided illustrating the robustness of the derived solution to measurement outliers as compared with the Kalman filter. The proposed solution is also compared with alternative outlier robust state estimation filters showing its effectiveness, in particular, in the presence of measurements outliers occurring in a consecutive sequence. Because of its deterministic execution time and limited numerical complexity, the proposed state estimator can be readily applied in real-time applications. Figure 4. Noisy output with outliers (depicted as blue stars, D 0:2) and non-outliers in grey generated through the model in (41). [Colour figure can be viewed at wileyonlinelibrary.com] The LEL Filter column refers to the interval of singular values over 3000 Monte Carlo runs assuming outliers generated through the model in (41). LEL, least entropy like.with synthetic and real data have been discussed showing the potentiality of the proposed filter as compared with the standard KF. Comparison with alternative robust filtering approaches have shown its robustness to sequences of successive measurement outliers as well. The proposed robust LEL filter may be exploited in real-time applications because of its limited and predictable (i.e. deterministic) computational effort.

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“…Numerical simulations have shown that the LELbased estimator can be remarkably effective in this context. Specific results are reported in [13].…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Output outlier robust state estimation

Palma

Indiveri

2016

Adaptive Control & Signal

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“…Let V K denote the bounded Volterra linear integral operator induced by a given bivariate kernel function K(•, •) mapping within the Hilbert space L 2 loc (R ≥0 ) of locally square-integrable functions with domain R ≥0 and range R. 1…”

Section: Non-asymptotic Volterra Operators Algebramentioning

confidence: 99%

Deadbeat source localization from range-only measurements: A robust kernel-based approach

Pin¹,

Parisini

Fedele

2016

2016 American Control Conference (ACC)

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This paper presents a novel framework for the problem of target localization based on the range information collected by a single mobile agent. The proposed methodology exploits the algebra of Volterra integral operators to annihilate the influence of initial conditions on the transient phase, thus achieving a deadbeat performance. The robustness properties against additive measurement perturbations are analyzed and the bias caused by the time-discretization is characterized as well. Extensive simulation results and comparisons are provided showing the effectiveness of the proposed technique in coping with both stationary and drifting targets.

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“…Therefore, in the following only a partial overview will be given. For an expanded literature review, the reader is referred, for instance, to the important recent paper [1].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the measurement of velocity is required introducing additional difficulties of implementation and possibly extra measurement errors. In [1], an alternative LTI system with time-varying output mapping is formulated with comprehensive observability analysis. Moreover, a robust correctionbased state estimator is designed with the consideration of disturbances on both the distance measurement and velocity.…”

Section: Introductionmentioning

confidence: 99%

Deadbeat Source Localization From Range-Only Measurements: A Robust Kernel-Based Approach

Fedele

et al. 2019

IEEE Trans. Contr. Syst. Technol.

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Abstract-This paper presents a novel framework for the problem of target localization based on the range information collected by a single mobile agent. The proposed methodology exploits the algebra of Volterra integral operators to annihilate the influence of initial conditions on the transient phase, thus achieving a deadbeat performance. The robustness properties against additive measurement perturbations are analyzed and the bias caused by the time-discretization is characterized as well. Extensive simulation results and comparisons are provided showing the effectiveness of the proposed technique in coping with both stationary and drifting targets.

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Single Range Localization in 3-D: Observability and Robustness Issues

Cited by 35 publications

References 32 publications

Output outlier robust state estimation

Output outlier robust state estimation

Deadbeat source localization from range-only measurements: A robust kernel-based approach

Deadbeat Source Localization From Range-Only Measurements: A Robust Kernel-Based Approach

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