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This paper proposes a novel joint probabilistic data association (JPDA) filter for joint target tracking and track maintenance in the presence of unknown detection probability and clutter rate. The proposed algorithm consists of two main parts: (1) the standard JPDA filter with a Poisson point process birth model for multi-object state estimation; and (2) a multi-Bernoulli filter for detection probability...
Linear Discriminant Analysis (LDA) is widely-used for supervised dimension reduction and linear classification. Classical LDA, however, suffers from the ill-posed estimation problem on data with high dimension and low sample size (HDLSS). To cope with this problem, in this paper, we propose an Adaptive Wishart Discriminant Analysis (AWDA) for classification, that makes predictions in an ensemble way...
Reliable uncertainty estimation for time series prediction is critical in many fields, including physics, biology, and manufacturing. At Uber, probabilistic time series forecasting is used for robust prediction of number of trips during special events, driver incentive allocation, as well as real-time anomaly detection across millions of metrics. Classical time series models are often used in conjunction...
For manifolds with topologies that strongly differ from the standard topology of Rn, using common filters created for linear domains can yield misleading results. While there is a lot of ongoing research on estimation on the unit circle, higher-dimensional problems particularly pose a challenge. One important generalization of the unit circle is the unit hypersphere. In this paper, we propose a recursive...
In this paper, we represent a terrain inference method based on vibration features. Autonomous navigation in unstructured environments is a challenging problem. Especially, the detailed interpretation of terrain in unstructured environments is necessary to set an efficient navigation trajectory. As the vibration features are obtained from interactions between the robot and terrain, terrain inference...
Recently, due to wide observable range as well as low power consumption, the usage of high frequency radars has been expanded to ship detection for both harbor management and national security. However, range and angular resolutions are typically low in high frequency radars due to environmental and physical constraints. Thus, a target location detected on a high frequency radar system is far away...
The DOA estimation problem for wideband signals has attracted much attention in the past years, and how to utilize and derive the common DOA information among frequency bins is the essential question. We address the wideband DOA estimation problem in this paper, and to solve this problem we propose a joint sparse Bayesian learning algorithm based on the sparse signal representation (SSR) of the covariance...
This paper reviews the development history of simultaneous localization and mapping (SLAM) and concentrates on two mainstream methods: the filter-based method and the vision-based graph optimization method. FastSLAM and Real-Time Appearance-Based Mapping (RTAB-MAP) as two examples are adopted in the real experiments. The experiments are implemented on TurtleBot with Kinect in a small laboratory and...
Total number of failures of a software system can help practitioners to have a better understanding of the software quality. In this paper, we propose a model to predict the total number of software failures in a software system by analyzing the failure data from testing using models based on Zipf's law together with the information on code coverage. Failure data and code coverage are combined in...
The objective of this paper is to estimate the compressor discharge temperature measurements on an industrial gas turbine that is undergoing commissioning at site, using a data-driven model which is built using the test bed measurements of the engine. This paper proposes a Bayesian neuro-fuzzy modelling (BNFM) approach, which combines the adaptive neuro-fuzzy inference system (ANFIS) and variational...
Live-feeling communication is a seamless process of intelligent system estimating user intention solely on passive user-to-robot communication of user emotions and body movements. In this paper we study the live-feeling communication in an entertainment framework; a real-time streaming event (football) is split into set of important and relevant scenes, and for each scene the user intention is estimated...
Underwater acoustic (UWA) channel estimation based on fast Bayesian matching pursuit (FBMP) is investigated in this paper. In UWA multipath channel, the positions of UWA channel taps usually obey the Bernoulli distribution, while the coefficients of UWA channel taps obey the complex-valued Gaussian distribution. Such knowledge of distribution is considered as a prior for UWA channel estimation based...
In this paper, we present a pedestrian navigation method for indoor environments that uses a novel non-recursive Bayesian filter. Developed method takes building floor plans into account to compensate for error-prone heading measurements provided by a foot mounted micro electro-mechanical system (MEMS) inertial measurement unit (IMU). Zero velocity updates (ZUPT) are implemented intuitively to estimate...
Consistent reporting of Limit of Detection (LOD) and its inherent uncertainty is imperative as the number of sensors for challenging applications continues to rise. Here, we demonstrate a Bayesian approach to LOD estimation, which also provides estimates of LOD uncertainty. By using this method to compare seemingly similar sensors, we demonstrate that LOD uncertainty is at least as important as LOD...
A general approach is proposed to determine the occupancy in a room using sensor data and knowledge coming respectively from observation and questioning are determined. Means to estimate occupancy include motion detections, power consumptions and acoustic pressure rewarded by a microphone. The proposed approach is inspired from machine learning. It starts by determining the most useful measurements...
Lagrangian carotid strain imaging (LCSI) involves estimation of deformation in the carotid artery due to blood pressure variations under cardiac pulsation. Local strain over a cardiac cycle is tracked, which is computationally intensive. We incur long offline processing times for LCSI which becomes a limiting factor for clinical adoption. We report on the computational speedup obtained for a parallelized...
A Bayesian approach for system identification using kernel functions is a popular method. The kernel functions are considered as certain prior knowledge about a target system, so selecting proper kernels is required. Recent studies show that it is successful to use OBF-s(orthonormal basis function)-based kernels as the kernel functions, but estimating hyper-parameters of the kernel functions is a...
Microgrids are suitable electrical solutions for providing energy in rural zones. However, it is challenging to propose in advance a good design of the microgrid because the electrical load is difficult to estimate due to its highly dependence of the residential consumption. In this paper, a novel estimation methodology for the residential load profiles is proposed. Socio-demographic data and electrical...
Vertical height estimation is critical to indoor localization technique. However, the common story height covers from 2.8m to 6.0m in multistory buildings, which make it meaningless to estimate height alone. An efficient indoor location system should provide accurate floor estimation with fuzzy story height information. This paper proposes a Bayesian Network inference method to identify pedestrian's...
This article analyzes the performance of combining information from Scanning Electron Microscopy (SEM) micrographs with Static Light Scattering (SLS) measurements for retrieving the so-called Particle Size Distribution (PSD). The corresponding data fusion, which is formulated as a Bayesian inverse problem, is implemented using an emblematic Monte Carlo Markov Chain (MCMC) technique, the Metropolis-Hastings...
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