1,726 research outputs found
Poisson multi-Bernoulli mixture trackers: continuity through random finite sets of trajectories
The Poisson multi-Bernoulli mixture (PMBM) is an unlabelled multi-target
distribution for which the prediction and update are closed. It has a Poisson
birth process, and new Bernoulli components are generated on each new
measurement as a part of the Bayesian measurement update. The PMBM filter is
similar to the multiple hypothesis tracker (MHT), but seemingly does not
provide explicit continuity between time steps. This paper considers a recently
developed formulation of the multi-target tracking problem as a random finite
set (RFS) of trajectories, and derives two trajectory RFS filters, called PMBM
trackers. The PMBM trackers efficiently estimate the set of trajectories, and
share hypothesis structure with the PMBM filter. By showing that the prediction
and update in the PMBM filter can be viewed as an efficient method for
calculating the time marginals of the RFS of trajectories, continuity in the
same sense as MHT is established for the PMBM filter
Bayesian Sequential Track Formation
This paper presents a theoretical framework for track building in multiple-target scenarios from the Bayesian point of view. It is assumed that the number of targets is fixed and known. We propose two optimal methods for building tracks sequentially. The first one uses the labelling of the current multitarget state estimate that minimizes the mean-square labeled optimal subpatternassignment error. This method requires knowledge of the posterior density of the vector-valued state. The second assigns the labeling that maximizes the probability that the current multi-targetstate estimate is optimally linked with the available tracks at the previous time step. In this case, we only require knowledge of the random finite-set posterior density without labels
Spooky effect in optimal OSPA estimation and how GOSPA solves it
In this paper, we show the spooky effect at a distance that arises in optimal estimation of multiple targets with the optimal sub-pattern assignment (OSPA) metric. This effect refers to the fact that if we have several independent potential targets at distant locations, a change in the probability of existence of one of them can completely change the optimal estimation of the rest of the potential targets. As opposed to OSPA, the generalised OSPA (GOSPA) metric (α=2) penalises localisation errors for properly detected targets, false targets and missed targets. As a consequence, optimal GOSPA estimation aims to lower the number of false and missed targets, as well as the localisation error for properly detected targets, and avoids the spooky effect
Non-Myopic Sensor Control for Target Search and Track Using a Sample-Based GOSPA Implementation
This paper is concerned with sensor management for target search and track
using the generalised optimal subpattern assignment (GOSPA) metric. Utilising
the GOSPA metric to predict future system performance is computationally
challenging, because of the need to account for uncertainties within the
scenario, notably the number of targets, the locations of targets, and the
measurements generated by the targets subsequent to performing sensing actions.
In this paper, efficient sample-based techniques are developed to calculate the
predicted mean square GOSPA metric. These techniques allow for missed
detections and false alarms, and thereby enable the metric to be exploited in
scenarios more complex than those previously considered. Furthermore, the GOSPA
methodology is extended to perform non-myopic (i.e. multi-step) sensor
management via the development of a Bellman-type recursion that optimises a
conditional GOSPA-based metric. Simulations for scenarios with missed
detections, false alarms, and planning horizons of up to three time steps
demonstrate the approach, in particular showing that optimal plans align with
an intuitive understanding of how taking into account the opportunity to make
future observations should influence the current action. It is concluded that
the GOSPA-based, non-myopic search and track algorithm offers a powerful
mechanism for sensor management.Comment: The paper has been submitted for publication in IEEE Transactions on
Aerospace and Electronic Systems and is currently in revie
Iterated Posterior Linearization Smoother
This note considers the problem of Bayesian smoothing in nonlinear state-space models with additive noise using Gaussian approximations. Sigma-point approximations to the general Gaussian Rauch-Tung-Striebel smoother are widely used methods to tackle this problem. These algorithms perform statistical linear regression (SLR) of the nonlinear functions considering only the previous measurements. We argue that SLR should be done taking all measurements into account. We propose the iterated posterior linearization smoother (IPLS), which is an iterated algorithm that performs SLR of the nonlinear functions with respect to the current posterior approximation. The algorithm is demonstrated to outperform conventional Gaussian nonlinear smoothers in two numerical examples
Both associative activation and thematic extraction count, but thematic false memories are more easily rejected
The main aim of this study was to analyse the roles played by associative activation and thematic
extraction in the explanation of false memories using the Deese, Roediger, McDermott (DRM)
paradigm. Associative lists with two different types of critical items (CIs) were used: one, the associative
CI, corresponded to the word most strongly primed by the associates in the list and another, the thematic
CI, was the word that best described the theme of the list. Following three different types of encoding
instructions (standard, warning or strategic), false recognition for these two types of CIs was analysed in
either self-paced or speeded response recognition tests. The results showed considerable levels of false
memories for both types of CIs. Even without the quality of being “good themes”, associative CIs
produced high levels of false recognition, which suggests that associative activation plays a prominent
role in false memory formation. More interestingly, thematic CIs were more prone to be edited out,
reinforcing the argument that thematic identifiability has a major role in the rejection of false memories
Bayesian Road Estimation Using Onboard Sensors
This paper describes an algorithm for estimating the road ahead of a host vehicle based on the measurements from several onboard sensors: a camera, a radar, wheel speed sensors,and an inertial measurement unit.We propose a novel road model that is able to describe the road ahead with higher accuracy than the usual polynomial model. We also develop a Bayesian fusionsystem that uses the following information from the surroundings: lane marking measurements obtained by the camera and leading vehicle and stationary object measurements obtained bya radar–camera fusion system. The performance of our fusion algorithm is evaluated in several drive tests. As expected, the more information we use, the better the performance is.Index Terms—Camera, information fusion, radar, road geometry,unscented Kalman filter (UKF)
Continuous-discrete multiple target tracking with out-of-sequence measurements
This paper derives the optimal Bayesian processing of an out-of-sequence (OOS) set of measurements in continuous-time for multiple target tracking. We consider a multi-target system modelled in continuous time that is discretised at the time steps when we receive the measurements, which are distributed according to the standard point target model. All information about this system at the sampled time steps is provided by the posterior density on the set of all trajectories. This density can be computed via the continuous-discrete trajectory Poisson multi-Bernoulli mixture (TPMBM) filter. When we receive an OOS measurement, the optimal Bayesian processing performs a retrodiction step that adds trajectory information at the OOS measurement time stamp followed by an update step. After the OOS measurement update, the posterior remains in TPMBM form. We also provide a computationally lighter alternative based on a trajectory Poisson multi-Bernoulli filter. The effectiveness of the two approaches to handle OOS measurements is evaluated via simulations
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