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Abstract-This paper considers the problem of simultaneously detecting and tracking multiple targets. The problem can be formulated in a Bayesian framework and solved, in principle, by computation of the joint multitarget probability density (JMPD). In practice, exact computation of the JMPD is impossible, and the predominant challenge is to arrive at a computationally tractable approximation. A particle filtering scheme is developed for this purpose in which each particle is a hypothesis on the number of targets present and the states of those targets. The importance density for the particle filter is designed in such a way that the measurements can guide sampling of both the target number and the target states. Simulation results, with measurements generated from real target trajectories, demonstrate the ability of the proposed procedure to simultaneously detect and track ten targets with a reasonable sample size.

This paper introduces a new algorithm called Lane-finding in ANother domAin (LANA) for detecting lane markers in images acquired from a forward-looking vehicle-mounted camera. The method is based on a novel set of frequency domain features that capture relevant information concerning the strength and orientation of spatial edges. The frequency domain features are combined with a deformable template prior, in order to detect the lane markers of interest. Experimental results that illustrate the performance of this algorithm on images with varying lighting and environmental conditions, shadowing, lane occlusion(s), solid and dashed lines, etc. are presented. LANA seems to detect lane markers remarkably well under a very large and varied collection of roadway images. A comparison is drawn between this frequency feature-based LANA algorithm and the spatial feature-based LOIS lane detection algorithm. This comparison is made from experimental, computational, and methodological standpoints.

Adaptive sensing involves actively managing sensor resources to achieve a sensing task, such as object detection, classification, and tracking, and represents a promising direction for new applications of discrete event system methods. We describe an approach to adaptive sensing based on approximately solving a partially observable Markov decision process (POMDP) formulation of the problem. Such approximations are necessary because of the very large state space involved in practical adaptive sensing problems, precluding exact computation of optimal solutions. We review the theory of POMDPs and show how the theory applies to adaptive sensing problems. We then describe a variety of approximation methods, with examples to illustrate their application in adaptive sensing. The examples also demonstrate the gains that are possible from nonmyopic methods relative to myopic methods, and highlight some insights into the dependence of such gains on the sensing resources and environment.

Abstract-This paper addresses the problem of sensor management for a large network of agile sensors. Sensor management, as defined here, refers to the process of dynamically retasking agile sensors in response to an evolving environment. Sensors may be agile in a variety of ways, e.g., the ability to reposition, point an antenna, choose sensing mode, or waveform. The goal of sensor management in a large network is to choose actions for individual sensors dynamically so as to maximize overall network utility. An effective sensor management algorithm must combine prior knowledge, sensor models, environment models, and measurements to predict the best actions to take.Sensor management in the multiplatform setting is a challenging problem for several reasons. First, the state space required to characterize an environment is typically of very high dimension and poorly represented by a parametric form. Second, the network must simultaneously address a number of competing goals. Third, the number of potential taskings grows exponentially with the number of sensors. Finally, in low communication environments, decentralized methods are required.The approach we present in this paper addresses these challenges through a novel combination of particle filtering for nonparametric density estimation, information theory for comparing actions, and physicomimetics for computational tractability. The efficacy of the method is illustrated in a realistic surveillance application by simulation, where an unknown number of ground targets are to be detected and tracked by a network of mobile sensors.Index Terms-multiplatform sensor management, information theory, particle filtering, joint multitarget probability density, multitarget tracking.

This paper addresses the problem of tracking multiple moving targets by estimating their joint multitarget probability density (JMPD). The JMPD technique is a Bayesian method for tracking multiple targets that allows nonlinear, non-Gaussian target motions and measurement to state coupling. JMPD simultaneously estimates both the target states and the number of targets. In this paper, we give a new grid-free implementation of JMPD based on particle filtering techniques and explore several particle proposal strategies, resampling techniques, and particle diversification methods. We report the effect of these techniques on tracker performance in terms of tracks lost, mean squared error, and computational burden.

Abstract-Several authors have proposed sensor scheduling methods that are driven by information theoretic measures. In the information driven approach, the relative merit of different sensing actions is measured by the corresponding expected gain in information. Information driven approaches stand in stark contrast to task driven methods, i.e., methods that select some physical performance criteria and explicitly manage the sensor based on that criteria. This paper investigates the difference between a particular information driven approach, one that maximizes an alpha-Rényi measure of information gain, and task driven methods with a combination of theory and simulation. First, we give a mathematical relation that shows that when the decision error depends only weakly on the target state a certain type of marginalized information gain is a close approximation to the Bayes risk associated with performing a specific task. Second, we perform an empirical comparison between information driven and task driven approaches that maximize information gain or minimize risk, respectively. In particular, we give a task driven method that uses the sensor in a manner that is expected to maximize the probability the target is correctly located after the next measurement. We find as expected that the task driven method outperforms the information driven method when the performance is measured by risk, i.e., probability of localization error. However, the performance difference between the two methods is very small, suggesting that the information gain is a good surrogate for risk for this application.