2,383 research outputs found
A Collaborative Sensor Fusion Algorithm for Multi-Object Tracking Using a Gaussian Mixture Probability Hypothesis Density Filter
This paper presents a method for collaborative tracking of multiple vehicles that extends a Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter with a collaborative fusion algorithm. Measurements are preprocessed in a detect-before-track fashion, and cars are tracked using a rectangular shape model. The proposed method successfully mitigates clutter and occlusion problems. In order to extend the field of view of individual vehicles and increase the estimation confidence in the areas where a target is observable by multiple vehicles, PHD intensities are exchanged between vehicles and fused in the Collaborative GM-PHD filter using a novel algorithm based on the Generalized Covariance Intersection. The method is extensively evaluated using a calibrated, high-fidelity simulator in scenarios where vehicles exhibit both straight and curved motion at different speeds
Multi-Target Tracking in Distributed Sensor Networks using Particle PHD Filters
Multi-target tracking is an important problem in civilian and military
applications. This paper investigates multi-target tracking in distributed
sensor networks. Data association, which arises particularly in multi-object
scenarios, can be tackled by various solutions. We consider sequential Monte
Carlo implementations of the Probability Hypothesis Density (PHD) filter based
on random finite sets. This approach circumvents the data association issue by
jointly estimating all targets in the region of interest. To this end, we
develop the Diffusion Particle PHD Filter (D-PPHDF) as well as a centralized
version, called the Multi-Sensor Particle PHD Filter (MS-PPHDF). Their
performance is evaluated in terms of the Optimal Subpattern Assignment (OSPA)
metric, benchmarked against a distributed extension of the Posterior
Cram\'er-Rao Lower Bound (PCRLB), and compared to the performance of an
existing distributed PHD Particle Filter. Furthermore, the robustness of the
proposed tracking algorithms against outliers and their performance with
respect to different amounts of clutter is investigated.Comment: 27 pages, 6 figure
World Modeling for Intelligent Autonomous Systems
The functioning of intelligent autonomous systems requires constant situation awareness and cognition analysis. Thus, it needs a memory structure that contains a description of the surrounding environment (world model) and serves as a central information hub. This book presents a row of theoretical and experimental results in the field of world modeling. This includes areas of dynamic and prior knowledge modeling, information fusion, management and qualitative/quantitative information analysis
Improved Gaussian mixture probability hypothesis density for tracking closely spaced targets
Probability hypothesis density (PHD) filter is a suboptimal Bayesian multi-target filter based on random finite set. The Gaussian mixture PHD filter is an analytic solution to the PHD filter for linear Gaussian multi-target models. However, when targets move near each other, the GM-PHD filter cannot correctly estimate the number of targets and their states. To solve the problem, a novel reweighting scheme for closely spaced targets is proposed under the framework of the GM-PHD filter, which can be able to correctly redistribute the weights of closely spaced targets, and effectively improve the multiple target state estimation precision. Simulation results demonstrate that the proposed algorithm can accurately estimate the number of targets and their states, and effectively improve the performance of multi-target tracking algorithm
Sensor Fusion and Resource Management in MIMO-OFDM Joint Sensing and Communication
This study explores the promising potential of integrating sensing
capabilities into multiple-input multiple-output (MIMO)-orthogonal frequency
division multiplexing (OFDM)-based networks through innovative multi-sensor
fusion techniques, tracking algorithms, and resource management. A novel data
fusion technique is proposed within the MIMO-OFDM system, which promotes
cooperative sensing among monostatic joint sensing and communication (JSC) base
stations by sharing range-angle maps with a central fusion center. To manage
data sharing and control network overhead introduced by cooperation, an
excision filter is introduced at each base station. After data fusion, the
framework employs a three-step clustering procedure combined with a tracking
algorithm to effectively handle point-like and extended targets. Delving into
the sensing/communication trade-off, resources such as transmit power,
frequency, and time are varied, providing valuable insights into their impact
on the overall system performance. Additionally, a sophisticated channel model
is proposed, accounting for complex urban propagation scenarios and addressing
multipath effects and multiple reflection points for extended targets like
vehicles. Evaluation metrics, including optimal sub-pattern assignment (OSPA),
downlink sum rate, and bit rate, offer a comprehensive assessment of the
system's localization and communication capabilities, as well as network
overhead
Online Audio-Visual Multi-Source Tracking and Separation: A Labeled Random Finite Set Approach
The dissertation proposes an online solution for separating an unknown and time-varying number of moving sources using audio and visual data. The random finite set framework is used for the modeling and fusion of audio and visual data. This enables an online tracking algorithm to estimate the source positions and identities for each time point. With this information, a set of beamformers can be designed to separate each desired source and suppress the interfering sources
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