Context Aided Tracking with Adaptive Hyperspectral Imagery

A methodology for the context-aided tracking of ground vehicles in remote airborne imagery is developed in which a background model is inferred from hyperspectral imagery. The materials comprising the background of a scene are remotely identified and lead to this model. Two model formation processes are developed: a manual method, and method that exploits an emerging adaptive, multiple-object-spectrometer instrument. A semi-automated background modeling approach is shown to arrive at a reasonable background model with minimal operator intervention. A novel, adaptive, and autonomous approach uses a new type of adaptive hyperspectral sensor, and converges to a 66% correct background model in 5% the time of the baseline {a 95% reduction in sensor acquisition time. A multiple-hypothesis-tracker is incorporated, which utilizes background statistics to form track costs and associated track maintenance thresholds. The context-aided system is demonstrated in a high- fidelity tracking testbed, and reduces track identity error by 30%

Contextual information aided target tracking and path planning for autonomous ground vehicles

Recently, autonomous vehicles have received worldwide attentions from academic research, automotive industry and the general public. In order to achieve a higher level of automation, one of the most fundamental requirements of autonomous vehicles is the capability to respond to internal and external changes in a safe, timely and appropriate manner. Situational awareness and decision making are two crucial enabling technologies for safe operation of autonomous vehicles. This thesis presents a solution for improving the automation level of autonomous vehicles in both situational awareness and decision making aspects by utilising additional domain knowledge such as constraints and influence on a moving object caused by environment and interaction between different moving objects. This includes two specific sub-systems, model based target tracking in environmental perception module and motion planning in path planning module. In the first part, a rigorous Bayesian framework is developed for pooling road constraint information and sensor measurement data of a ground vehicle to provide better situational awareness. Consequently, a new multiple targets tracking (MTT) strategy is proposed for solving target tracking problems with nonlinear dynamic systems and additional state constraints. Besides road constraint information, a vehicle movement is generally affected by its surrounding environment known as interaction information. A novel dynamic modelling approach is then proposed by considering the interaction information as virtual force which is constructed by involving the target state, desired dynamics and interaction information. The proposed modelling approach is then accommodated in the proposed MTT strategy for incorporating different types of domain knowledge in a comprehensive manner. In the second part, a new path planning strategy for autonomous vehicles operating in partially known dynamic environment is suggested. The proposed MTT technique is utilized to provide accurate on-board tracking information with associated level of uncertainty. Based on the tracking information, a path planning strategy is developed to generate collision free paths by not only predicting the future states of the moving objects but also taking into account the propagation of the associated estimation uncertainty within a given horizon. To cope with a dynamic and uncertain road environment, the strategy is implemented in a receding horizon fashion

Simple identification tools in FishBase

Simple identification tools for fish species were included in the FishBase information system from its inception. Early tools made use of the relational model and characters like fin ray meristics. Soon pictures and drawings were added as a further help, similar to a field guide. Later came the computerization of existing dichotomous keys, again in combination with pictures and other information, and the ability to restrict possible species by country, area, or taxonomic group. Today, www.FishBase.org offers four different ways to identify species. This paper describes these tools with their advantages and disadvantages, and suggests various options for further development. It explores the possibility of a holistic and integrated computeraided strategy

Hyperspectral-Augmented Target Tracking

With the global war on terrorism, the nature of military warfare has changed significantly. The United States Air Force is at the forefront of research and development in the field of intelligence, surveillance, and reconnaissance that provides American forces on the ground and in the air with the capability to seek, monitor, and destroy mobile terrorist targets in hostile territory. One such capability recognizes and persistently tracks multiple moving vehicles in complex, highly ambiguous urban environments. The thesis investigates the feasibility of augmenting a multiple-target tracking system with hyperspectral imagery. The research effort evaluates hyperspectral data classification using fuzzy c-means and the self-organizing map clustering algorithms for remote identification of moving vehicles. Results demonstrate a resounding 29.33% gain in performance from the baseline kinematic-only tracking to the hyperspectral-augmented tracking. Through a novel methodology, the hyperspectral observations are integrated in the MTT paradigm. Furthermore, several novel ideas are developed and implemented—spectral gating of hyperspectral observations, a cost function for hyperspectral observation-to-track association, and a self-organizing map filtering method. It appears that relatively little work in the target tracking and hyperspectral image classification literature exists that addresses these areas. Finally, two hyperspectral sensor modes are evaluated—Pushbroom and Region-of-Interest. Both modes are based on realistic technologies, and investigating their performance is the goal of performance-driven sensing. Performance comparison of the two modes can drive future design of hyperspectral sensors

Robotic Wireless Sensor Networks

In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future

A Survey of Beam Management for mmWave and THz Communications Towards 6G

Communication in millimeter wave (mmWave) and even terahertz (THz) frequency bands is ushering in a new era of wireless communications. Beam management, namely initial access and beam tracking, has been recognized as an essential technique to ensure robust mmWave/THz communications, especially for mobile scenarios. However, narrow beams at higher carrier frequency lead to huge beam measurement overhead, which has a negative impact on beam acquisition and tracking. In addition, the beam management process is further complicated by the fluctuation of mmWave/THz channels, the random movement patterns of users, and the dynamic changes in the environment. For mmWave and THz communications toward 6G, we have witnessed a substantial increase in research and industrial attention on artificial intelligence (AI), reconfigurable intelligent surface (RIS), and integrated sensing and communications (ISAC). The introduction of these enabling technologies presents both open opportunities and unique challenges for beam management. In this paper, we present a comprehensive survey on mmWave and THz beam management. Further, we give some insights on technical challenges and future research directions in this promising area.Comment: accepted by IEEE Communications Surveys & Tutorial

Track-oriented multiple hypothesis tracking based on Tabu search and Gibbs sampling

In order to circumvent the curse of dimensionality in multiple hypothesis tracking data association, this paper proposes two efficient implementation algorithms using Tabu search and Gibbs sampling. As the first step, we formulate the problem of generating the best global hypothesis in multiple hypothesis tracking as the problem of finding a maximum weighted independent set of a weighted undirected graph. Then, the metaheuristic Tabu search with two basic movements is designed to find the global optimal solution of the problem formulated. To improve the computational efficiency, this paper also develops a sampling based algorithm based on Gibbs sampling. The problem formulated for the Tabu search-based algorithm is reformulated as a maximum product problem to enable the implementation of Gibbs sampling. The detailed algorithm is then designed and the convergence is also theoretically analyzed. The performance of the two algorithms proposed are verified through numerical simulations and compared with that of a mainstream multiple dimensional assignment implementation algorithm. The simulation results confirm that the proposed algorithms significantly improve the computational efficiency while maintaining or even enhancing the tracking performance