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The ability to recognize objects and to localize them precisely is essential in all service robotic applications. One of the main challenges for service robots during operation lies in the handling of unavoidable uncertainties which originate from model and sensor inaccuracies and are characteristic for realistic application scenarios. Robustness under real world conditions can only be achieved when...
A new sequential Monte Carlo procedure for approximating the probability hypothesis density is proposed. The algorithm, based on the replacement of numerical approximation with exact computation, is applicable to the class of conditionally linear/Gaussian models. The proposed algorithm is applied with an efficient, measurement-directed importance density to multiple target tracking using range-bearings...
In this paper, we present a novel multiple-model probability hypothesis density (MMPHD) filter for multiple maneuvering targets tracking. In the proposed MMPHD filter, the multiple models are composed of two models, namely a constant velocity (CV) model and a ??current?? statistical (CS) model, and the PHD is approximated by a set of weighted random samples propagated over time using sequential Monte...
This paper presents an UAV fault and state prediction approach which is based on particle filter. In the UAV system, on account of its dynamic environment, mechanical complexity and other factors, it is difficult to avoid all potential faults. So, in order to early detect the potential fault, fault forecast is necessary so as to avoid enormous losses. As the input and output response model of UAV...
In this paper, we introduce a pilot-aided multipath channel estimator for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. Typical estimation algorithms assume the number of multipath components and delays to be known and constant, while their amplitudes may vary in time. In this work, we focus on the more realistic assumption that also the number of...
Reverberation and noise cause significant deterioration of audio quality and intelligibility to signals recorded in acoustic environments. Noise is usually modeled as a common signal observed in the room and independent of room acoustics. However, this simplistic model cannot necessarily capture the effects of separate noise sources at different locations in the room. This paper proposes a noise model...
The Stiefel manifold comprises sets of orthonormal vectors in Euclidean space, and as such arises in a variety of contemporary statistical signal processing contexts. Here we consider the problem of estimating the state of a hidden Markov process evolving on this manifold, given noisy observations in the embedding Euclidean space. We describe an approach using sequential Monte Carlo methods, and provide...
Continuous-time marked point processes appear in many areas of science and engineering including queuing theory, seismology, neuroscience and finance. In numerous applications, these point processes are unobserved but actually drive an observation process. Here, we are interested in optimal sequential Bayesian estimation of such partially observed point processes. This class of filtering problems...
An adaptive algorithm is presented for the estimation of reflection coefficients of the lattice processor. Unlike currently available block processing algorithms, this does not require a priori storage of data, nor does it involve ad hoc choices of iteration step lengths which concern hill-climbing techniques.
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