513 research outputs found
Optimal search: a practical interpretation of information-driven sensor management
We consider the problem of scheduling an agile sensor for performing optimal search for a target. A probability density function is created for representing our knowledge about where the target might be and it is utilized by the proposed sensor management criteria for finding optimal search strategies. The proposed criteria are: an information-driven criterion based on the Kullback-Leibler divergence and a criterion with practical meaning, i.e. performing the sensing action that will yield the maximum probability of detecting the target. It is shown that using the aforementioned criteria results in the same sensing actions when searching for a target and this result establishes a practical operational justification for using information-driven sensor management for performing search
SMC methods to avoid self-resolving for online Bayesian parameter estimation
Abstract—The particle filter is a powerful filtering technique that is able to handle a broad scope of nonlinear problems. However, it has also limitations: a standard particle filter is unable to handle, for instance, systems that include static variables (parameters) to be estimated together with the dynamic states. This limitation is due to the well-known “self-resolving” phenomenon, which is caused by the gradual loss of information that occurs during the resampling steps. In the context of online Bayesian parameter estimation, some approaches to handle this problem have proposed, such as adding artificial dynamics to the parameter model. However, these approaches typically both introduce new parameters (e.g. the intensity of artificial process noise) and inherent biases to the estimation problem. In this paper, we will give a give a look at two Sequential Monte Carlo techniques that do not rely on biasing the system model: the Autonomous Multiple Model particle filter and the Rao-Blackwellized Marginal particle filter. These approaches are not new, but have not been applied yet to the problem of online Bayesian parameter estimation for non-structured models. We will derive suitable adaptations of these methods for this problem and evaluate them using simulations. I
A Bayesian solution to multi-target tracking problems with mixed labelling
In Multi-Target Tracking (MTT), the problem of assigning labels to tracks (track labelling) is vastly covered in literature and has been previously formulated using Bayesian recursion. However, the existing literature lacks an appropriate measure of uncertainty related to the assigned labels which has sound mathematical basis and clear practical meaning (to the user). This is especially important in a situation where targets move in close proximity with each other and thereafter separate again. Because, in such a situation it is well-known that there will be confusion on target identities, also known as “mixed labelling‿. In this paper, we provide a mathematical characterization of the labelling uncertainties present in Bayesian multi-target tracking and labelling (MTTL) problems and define measures of labelling uncertainties with clear physical interpretation. The introduced uncertainty measures can be used to find the optimal track label assignment, and evaluate track labelling performance. We also analyze in details the mixed labelling phenomenon in the presence of two targets. In addition, we propose a new Sequential Monte Carlo (SMC) algorithm, the Labelling Uncertainty Aware Particle Filter (LUA-PF), for the multi target tracking and labelling problem that can provide good estimates of the uncertainty measures. We validate this using simulation and show that the proposed method performs much better when compared with the performance of the SIR multi-target SMC filter
Particle filter based MAP state estimation: A comparison
MAP estimation is a good alternative to MMSE for certain applications involving nonlinear non Gaussian systems. Recently a new particle filter based MAP estimator has been derived. This new method extracts the MAP directly from the output of a running particle filter. In the recent past, a Viterbi algorithm based MAP sequence estimator has been developed. In this paper, we compare these two methods for estimating the current state and the numerical results show that the former performs better
A Bayesian analysis of the mixed labelling phenomenon in two-target tracking
In mulit-target tracking and labelling (MTTL), mixed labelling corresponds to a situation where there is ambiguity in labelling, i.e. in the assignment of labels to locations (where a "location" here means simply an unlabelled single-target state. The phenomenon is well-known in literature, and known to occur in the situation where targets move in close proximity to each other and afterwards separate. The occurrence of mixed labelling has been empirically observed using particle filter implementations of the Bayesian MTTL recursion. In this memorandum, we will instead demonstrate the occurrence of mixed labelling (in the situation of closely spaced targets) using only the Bayesian recursion itself, for a scenario containing two targets and no target births or deaths. We will also show how mixed labelling generally persists after the targets become well-separated, and how mixed labelling might not happen when the unlabelled single-target state contains non-kinematic quantities
Labeling Uncertainty in Multitarget Tracking
In multitarget tracking, the problem of track labeling (assigning labels to tracks) is an ongoing research topic. The existing literature, however, lacks an appropriate measure of uncertainty related to the assigned labels that has a sound mathematical basis as well as clear practical meaning to the user. This is especially important in a situation where well separated targets move in close proximity with each other and thereafter separate again; in such a situation, it is well known that there will be confusion on target identities, also known as "mixed labeling." In this paper, we specify comprehensively the necessary assumptions for a Bayesian formulation of the multitarget tracking and labeling (MTTL) problem to be meaningful. We provide a mathematical characterization of the labeling uncertainties with clear physical interpretation. We also propose a novel labeling procedure that can be used in combination with any existing (unlabeled) MTT algorithm to obtain a Bayesian solution to the MTTL problem. One advantage of the resulting solution is that it readily provides the labeling uncertainty measures. Using the mixed labeling phenomenon in the presence of two targets as our test bed, we show with simulation results that an unlabeled multitarget sequential Monte Carlo (M-SMC) algorithm that employs sequential importance resampling (SIR) augmented with our labeling procedure performs much better than its "naive" extension, the labeled SIR M-SMC filter
mm-Wave Systems for High Data Rate Wireless Consumer Applications
ISM spectrum at 60GHz has attracted attention for possible high-speed applications in wireless communications for well over ten years. However, no high volume applications have emerged. Despite progress in mm-wave ICs, the power and cost of these efforts have not reached the level needed for mass deployment. This paper summarises the ARC funded GLIMMR project which aims to remedy this situation by designing systems on silicon that have both low cost and low power. In particular, the paper presents design work done to date that indicate that silicon (particularly SiGe) is on the cusp of being able to provide economical mm-wave systems
Localization from quantum interference in one-dimensional disordered potentials
We show that the tails of the asymptotic density distribution of a quantum
wave packet that localizes in the the presence of random or quasiperiodic
disorder can be described by the diagonal term of the projection over the
eingenstates of the disordered potential. This is equivalent of assuming a
phase randomization of the off-diagonal/interference terms. We demonstrate
these results through numerical calculations of the dynamics of ultracold atoms
in the one-dimensional speckle and quasiperiodic potentials used in the recent
experiments that lead to the observation of Anderson localization for matter
waves [Billy et al., Nature 453, 891 (2008); Roati et al., Nature 453, 895
(2008)]. For the quasiperiodic case, we also discuss the implications of using
continuos or discrete models.Comment: 5 pages, 3 figures; minor changes, references update
Aerosol-cloud relationships in continental shallow cumulus
Aerosol-cloud relationships are derived from 14 warm continental cumuli cases sampled during the 2006 Gulf of Mexico Atmospheric Composition and Climate Study (GoMACCS) by the Center for Interdisciplinary Remotely-Piloted Aircraft Studies (CIRPAS) Twin Otter aircraft. Cloud droplet number concentration is clearly proportional to the subcloud accumulation mode aerosol number concentration. An inverse correlation between cloud top effective radius and subcloud aerosol number concentration is observed when cloud depth variations are accounted for. There are no discernable aerosol effects on cloud droplet spectral dispersion; the averaged spectral relative dispersion is 0.30 ± 0.04. Aerosol-cloud relationships are also identified from comparison of two isolated cloud cases that occurred under different degrees of anthropogenic influence. Cloud liquid water content, cloud droplet number concentration, and cloud top effective radius exhibit subadiabaticity resulting from entrainment mixing processes. The degree of LWC subadiabaticity is found to increase with cloud depth. Impacts of subadiabaticity on cloud optical properties are assessed. It is estimated that owing to entrainment mixing, cloud LWP, effective radius, and cloud albedo are decreased by 50–85%, 5–35%, and 2–26%, respectively, relative to adiabatic values of a plane-parallel cloud. The impact of subadiabaticity on cloud albedo is largest for shallow clouds. Results suggest that the effect of entrainment mixing must be accounted for when evaluating the aerosol indirect effect
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