In this paper, we present the performance of the Gaussian mixture probability hypothesis density (GM-PHD) filter in tracking multiple ground targets using a passive acoustic-sensor network. For this purpose, an experimental setup consisting of a network of microphones and a loudspeaker was prepared. Non-cooperative transmissions from a loudspeaker (i.e. illuminator of opportunity) are exploited by non-directional separately located microphones (i.e. Doppler measuring sensors). Experimental proof-of-concept study results show that it is possible to track multiple ground targets using only Doppler shift measurements in a passive multi-static scenario. © 2012 ISIF (Intl Society of Information Fusi)
The paper applies a recently developed Consensus Gaussian Mixture - Cardinalized Probability Hypothe...
Bearings-only tracking is a challenging estimation problem due to the variable observability of the ...
Multi-target tracking (MTT) is one of the most important functions of radar systems. Traditional mul...
In this paper, we analyze the performance of the Gaussian mixture probability hypothesis density (GM...
In this paper, we analyze the performance of the Gaussian mixture probability hypothesis density (GM...
In this paper, we address the problem of multi-target detection and tracking over a network of separ...
In this paper, an improved nonlinear Gaussian mixture probability hypothesis density (GM-PHD) filter...
In this work, we address the problem of tracking an acoustic source based on measured time differenc...
In this correspondence, a new multi-target tracking (MTT) algorithm based on the probability hypothe...
This paper presents the application of the Gaussian Mixture Probability Hypothesis Density (GM-PHD) ...
Due to the Doppler Blind Zone (DBZ), the target tracking of Doppler radar becomes more and more comp...
This paper presents a method to achieve multi target tracking using acoustic power measurements obta...
In extended target tracking, targets potentially produce more than one measurement per time step. Mu...
In extended target tracking, targets potentially produce more than one measurement per time step. Mu...
In this paper, we explore the potential of networked microphone arrays for multiple target tracking....
The paper applies a recently developed Consensus Gaussian Mixture - Cardinalized Probability Hypothe...
Bearings-only tracking is a challenging estimation problem due to the variable observability of the ...
Multi-target tracking (MTT) is one of the most important functions of radar systems. Traditional mul...
In this paper, we analyze the performance of the Gaussian mixture probability hypothesis density (GM...
In this paper, we analyze the performance of the Gaussian mixture probability hypothesis density (GM...
In this paper, we address the problem of multi-target detection and tracking over a network of separ...
In this paper, an improved nonlinear Gaussian mixture probability hypothesis density (GM-PHD) filter...
In this work, we address the problem of tracking an acoustic source based on measured time differenc...
In this correspondence, a new multi-target tracking (MTT) algorithm based on the probability hypothe...
This paper presents the application of the Gaussian Mixture Probability Hypothesis Density (GM-PHD) ...
Due to the Doppler Blind Zone (DBZ), the target tracking of Doppler radar becomes more and more comp...
This paper presents a method to achieve multi target tracking using acoustic power measurements obta...
In extended target tracking, targets potentially produce more than one measurement per time step. Mu...
In extended target tracking, targets potentially produce more than one measurement per time step. Mu...
In this paper, we explore the potential of networked microphone arrays for multiple target tracking....
The paper applies a recently developed Consensus Gaussian Mixture - Cardinalized Probability Hypothe...
Bearings-only tracking is a challenging estimation problem due to the variable observability of the ...
Multi-target tracking (MTT) is one of the most important functions of radar systems. Traditional mul...