arXiv subjects

Donald J. Bucci

Publications and source records attributed to Donald J. Bucci.

4 recordsLinked to original sources

arXiv2019

Obstructed Target Tracking in Urban Environments

Accurate tracking in urban environments necessitates target birth, survival, and detection models that quantify the impact of terrain and building geometry on the sequential estimation procedure. Current efforts assume that target trajectories are limited to fixed paths, such as road networks. In these settings a single airborne platform with a downward-facing camera is capable of fully observing a target, outside of a few obstructed regions that can be determined a priori (e.g. tunnels). However, many practical target types are not necessarily restricted to road networks and thus require knowledge of azimuthal shadowed regions to the sensor. In this paper, we propose the integration of geospatial data for an urban environment into a particle filter realization of a random finite set target tracking algorithm. Specifically, we use 3D building polygons to compute the azimuthal shadowed regions with respect to deployed sensor location. The particle filter predict and update steps are modified such that (1) target births are assumed to occur in line-of-sight (LOS) regions, (2) targets do not move into obstructions, (3) true target detections only occur in LOS regions. The localization error performance improvement for a single target Bernoulli filter under these modifications is presented using freely available building vector data of New York City.

eess.SY↗

arXiv2019

Passive Multi-Target Tracking Using the Adaptive Birth Intensity PHD Filter

Passive multi-target tracking applications require the integration of multiple spatially distributed sensor measurements to distinguish true tracks from ghost tracks. A popular multi-target tracking approach for these applications is the particle filter implementation of Mahler's probability hypothesis density (PHD) filter, which jointly updates the union of all target state space estimates without requiring computationally complex measurement-to-track data association. Although this technique is attractive for implementation in computationally limited platforms, the performance benefits can be significantly overshadowed by inefficient sampling of the target birth particles over the region of interest. We propose a multi-sensor extension of the adaptive birth intensity PHD filter described in (Ristic, 2012) to achieve efficient birth particle sampling driven by online sensor measurements from multiple sensors. The proposed approach is demonstrated using distributed time-difference-of-arrival (TDOA) and frequency-difference-of-arrival (FDOA) measurements, in which we describe exact techniques for sampling from the target state space conditioned on the observations. Numerical results are presented that demonstrate the increased particle density efficiency of the proposed approach over a uniform birth particle sampler.

eess.SY↗

arXiv2018

K-medoids Clustering of Data Sequences with Composite Distributions

This paper studies clustering of data sequences using the k-medoids algorithm. All the data sequences are assumed to be generated from \emph{unknown} continuous distributions, which form clusters with each cluster containing a composite set of closely located distributions (based on a certain distance metric between distributions). The maximum intra-cluster distance is assumed to be smaller than the minimum inter-cluster distance, and both values are assumed to be known. The goal is to group the data sequences together if their underlying generative distributions (which are unknown) belong to one cluster. Distribution distance metrics based k-medoids algorithms are proposed for known and unknown number of distribution clusters. Upper bounds on the error probability and convergence results in the large sample regime are also provided. It is shown that the error probability decays exponentially fast as the number of samples in each data sequence goes to infinity. The error exponent has a simple form regardless of the distance metric applied when certain conditions are satisfied. In particular, the error exponent is characterized when either the Kolmogrov-Smirnov distance or the maximum mean discrepancy are used as the distance metric. Simulation results are provided to validate the analysis.

cs.LG↗

arXiv2018

Nonparametric Composite Hypothesis Testing in an Asymptotic Regime

We investigate the nonparametric, composite hypothesis testing problem for arbitrary unknown distributions in the asymptotic regime where both the sample size and the number of hypotheses grow exponentially large. Such asymptotic analysis is important in many practical problems, where the number of variations that can exist within a family of distributions can be countably infinite. We introduce the notion of \emph{discrimination capacity}, which captures the largest exponential growth rate of the number of hypotheses relative to the sample size so that there exists a test with asymptotically vanishing probability of error. Our approach is based on various distributional distance metrics in order to incorporate the generative model of the data. We provide analyses of the error exponent using the maximum mean discrepancy (MMD) and Kolmogorov-Smirnov (KS) distance and characterize the corresponding discrimination rates, i.e., lower bounds on the discrimination capacity, for these tests. Finally, an upper bound on the discrimination capacity based on Fano's inequality is developed. Numerical results are presented to validate the theoretical results.

cs.IT↗