An Exact Trajectory Solution from Doppler Shift Measurements

Webster, Roger

doi:10.1109/taes.1982.309235

Cited by 41 publications

(18 citation statements)

References 1 publication

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“…The propagation delay ∆(t; θ, s i ) is implicitly given by (1). Even though the motion model and source location are given on closed form, all attempts to obtain a closed form expression for ∆(t; θ, s i ) have failed.…”

Section: The Circular Motion Modelmentioning

confidence: 99%

“…A number of authors have considered the case with a single sensor by which the source passage distance and assumed constant speed are estimated [1][2][3][4]. It appears, however, that the estimation problem is inherently nonlinear in the parameters, and a rather wide range of different numerical approaches are proposed to pursue the, in various senses, optimal estimate.…”

Section: Introductionmentioning

confidence: 99%

“…By also using the frequency time derivative as a measurement, the grid search can be conducted in only one dimension, see [7]. Actually, by disregarding the retardation, an exact solution based on a deterministic model and three frequency measurements can be found by calculating the roots of a third order polynomial, see [1].…”

Section: Introductionmentioning

confidence: 99%

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Distributed localization using acoustic Doppler

2015

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It is well-known that the motion of an acoustic source can be estimated from Doppler shift observations. It is however not obvious how to design a sensor network to efficiently deliver the localization service. In this work a rather simplistic motion model is proposed that is aimed at sensor networks with realistic numbers of sensor nodes. It is also described how to efficiently solve the associated least squares optimization problem by Gauss-Newton variable projection techniques, and how to initiate the numerical search from simple features extracted from the observed frequency series. The methods are evaluated by Monte Carlo simulations and demonstrated on real data by localizing an all-terrain vehicle. It is concluded that the processing components included are fairly mature for practical implementations in sensor networks.

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Section: The Circular Motion Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Distributed localization using acoustic Doppler

2015

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“…On the other hand, in GM implementation no such problem exist and implementation is easier. Lastly, handling these two problems get worse if the prior newborn intensity covers whole surveillance volume, which is the case in this work [20], [19], [21], [22].…”

Section: Gm-phd Filter Parameters Are Chosen As Follows Maximum Numbmentioning

confidence: 99%

“…However, tracking using Doppler-only measurements is not an easy problem due to several reasons: 1-)since Doppleronly measurements are uninformative, target state remains unobservable before collecting at least three Doppler measurements from sensors with different locations, 2-) since Doppler measurements are typically accurate and initially target state is unobservable, initial measurement updates are weighted significantly and, thus, low-complexity nonlinear filters diverge and also special care should be given to avoid sample impoverishment when using particle implementations, 3-)mentioned two problems get worse if the prior distribution covers the whole surveillance volume (i.e. too big covariance values for location and velocity), which is the case in most of the practical applications, in the state space [20], [21], [22].…”

Section: Introductionmentioning

confidence: 99%

Multi-target tracking with PHD filter using Doppler-only measurements

Güldoǧan

Lindgren

Gustafsson

et al. 2014

Digital Signal Processing

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In this paper, we address the problem of multi-target detection and tracking over a network of separately located Doppler-shift measuring sensors. For this challenging problem, we propose to use the probability hypothesis density (PHD) filter and present two implementations of the PHD filter, namely the sequential Monte Carlo PHD (SMC-PHD) and the Gaussian mixture PHD (GM-PHD) filters. Performances of both filters are carefully studied and compared for the considered challenging tracking problem. Simulation results show that both PHD filter implementations, successfully tracks multiple targets using only Doppler shift measurements. Moreover, as a proof-of-concept, an experimental setup consisting of a network of microphones and a loudspeaker was prepared. Experimental study results reveal that it is possible to track multiple ground targets using acoustic Doppler shift measurements in a passive multi-static scenario. We observed that the GM-PHD is more effective, efficient and easy to implement than the SMC-PHD filter.

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Sensor Control for Random Set BasedParticle Filters

Ristić¹

2013

Particle Filters for Random Set Models

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An Exact Trajectory Solution from Doppler Shift Measurements

Cited by 41 publications

References 1 publication

Distributed localization using acoustic Doppler

Distributed localization using acoustic Doppler

Multi-target tracking with PHD filter using Doppler-only measurements

Sensor Control for Random Set BasedParticle Filters

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