Lucila Patino-Studencki

A Low-Cost RSSI-Based Localization System for Wildlife Tracking

This paper presents a low-cost localization system for the high-resolution tracking of bats. The system bases on a ground network consisting of multiple low-cost receiver stations, and ultra-lightweight transmitters mounted on the bats. A main challenge of the received signal strength based localization is the limited dynamic range of the employed low-cost receivers. This challenge is solved using an efficient 2-stage differential correlation concept. It significantly improves the dynamic wrt. conventional power detection methods. In addition, this concept requires low processing power and is robust wrt. frequency offsets. Finally, this paper presents a performance evaluation employing reference measurements recorded in the rain forest of Panama.

Fundamental limits in RSSI-based direction-of-arrival estimation

The use of wireless sensor networks is rapidly increasing. Also the demand of ubiquitous location sensors is swiftly expanding. Hence, energy and location-awareness come into focus of research today. A prospective approach for low-power locating sensor networks is received signal strength indicator (RSSI)-based direction finding. The presented approach is based on RSSI difference measurements retrieved by a array of directed antennas. In this paper, fundamental limits of RSSI-based direction finding are evaluated, beyond the Cramer-Rao Lower Bound (CRLB). That is not applicable for the design of a localization system topology due to the nature of the gain difference function that leads to an unbounded variance of the unbiased estimator. Thus, a maximum likelihood (ML) approach to the RSSI-based direction finding is presented. The ML estimator yields a limited variance for all signal directions. However, that benefit comes at the expense of being biased. Beyond treating direction estimates, mean square position errors are compared for both, the unbiased and the ML estimator.

Pedestrian Tracking using a Generalized Potential Field Approach.

Mobile robots and autonomous driving cars operate in a shared environment with pedestrians. In order to avoid accidents, it is important to track and predict human trajectories in an optimal way. In this paper, a generalized potential field approach for characterizing pedestrian movements is proposed which goes beyond the well-known social force model. Its goal is to give a generalized architecture for improving the tracking accuracy of pedestrians in surveillance situations. In comparison to other fusion approaches, the number of proposed parameters is reduced and the parameters can be intuitively understood. For a simple scenario, in a forum the trajectories of pedestrians are predicted for a configured parameter set. For this purpose, the proposed model is used. The predicted trajectories are compared to the real trajectories of the pedestrians. First results regarding the accuracy of the approach are presented.

Probabilistic multipath mitigation in RSSI-based direction-of-arrival estimation

Nowadays, wireless sensor networks (WSNs) have become omnipresent. Also location-based services become more and more popular. In this context, energy- and location-awareness are essential properties of modern sensor networks. Addressing low-power positioning, received signal strength indicator (RSSI)-based direction finding is a prospective approach for WSNs providing location-based services. However, like with all radio-based localization techniques, RSSI-based direction finding is prone to multipath propagation. In this paper, a probabilistic approach to multipath mitigation in RSSI-based direction estimation is presented. The presented approach is verified by Monte Carlo simulations. Simulations reveal the capability of zero-mean estimates for arrival angle in presence of multipath. Furthermore, the impact on the position estimation with recursive Bayesian filters is assessed. Trajectory estimation in a multipath setting shows promising results. Errors arising from multipath propagation can be minimized to a large extent.

Deep Learning for Real-Time Capable Object Detection and Localization on Mobile Platforms

Industry 4.0 is one of the most formative terms in current times. Subject of research are particularly smart and autonomous mobile platforms, which enormously lighten the workload and optimize production processes. In order to interact with humans, the platforms need an in-depth knowledge of the environment. Hence, it is required to detect a variety of static and non-static objects. Goal of this paper is to propose an accurate and real-time capable object detection and localization approach for the use on mobile platforms. A method is introduced to use the powerful detection capabilities of a neural network for the localization of objects. Therefore, detection information of a neural network is combined with depth information from a RGB-D camera, which is mounted on a mobile platform. As detection network, YOLO Version 2 (YOLOv2) is used on a mobile robot. In order to find the detected object in the depth image, the bounding boxes, predicted by YOLOv2, are mapped to the corresponding regions in the depth image. This provides a powerful and extremely fast approach for establishing a real-time-capable Object Locator. In the evaluation part, the localization approach turns out to be very accurate. Nevertheless, it is dependent on the detected object itself and some additional parameters, which are analysed in this paper.

Multiple intention tracking by a generalizec potential field approach

Fully automated vehicles and mobile robots operate in a shared environment with pedestrians. To minimize the risk for pedestrians, it is very important to track them in a precise way. As cameras are often installed in surveillance situations, they are used for tracking pedestrians in a shared environment. To improve the accuracy of the tracking, it is necessary to include all available context information in the fusion process. One important information source is the intention of the pedestrian. A generalized potential field is used, which can be modeled using pedestrian movements. When the intention of the person is unknown, different hypotheses for the intention of the pedestrian are considered. A Multi-Hypotheses tracking filter fuses the intention information and the pedestrian position measurements of a camera, whereby the tracking accuracy is improved. The proposed approach is evaluated using real camera data from a simple scenario in Edinburgh Informatics Forum. All results are evaluated in dependence of the measurement quality and the frame rate of the camera. The Multi-Hypotheses based tracking outperforms the simple Kalman filter over the whole range of frame rates and standard deviations.

Characterization of an opportunistic wireless syntonization using a low-cost test-bed

The most promising way of bringing localization services to GPS denied areas is the use of wireless sensor networks. The attainable accuracy for many methods of localization is highly depending on the synchronization between the sensor nodes. In this paper we describe a concept of adjusting the frequency of the local clocks with the help of signals of opportunity. It is explained, how broadcast signals can be used as a wireless reference signal for this syntonization process. We characterize the syntonization accuracy with the help of time domain analysis techniques (e.g. Allan Deviation). The measurements are carried out with a new low-cost test-bed using a software-defined radio hardware platform and the open source software GNU Radio. Using this measurement setup we characterize the wireless syntonization performance between two sensor nodes.

Time-domain OFDM carrier phase estimation for wireless sensor network syntonization

One of the most promising ways of bringing localization services to GPS denied areas is the use of wireless sensor networks. For time difference of arrival (TDOA) systems, the achievable accuracy highly depends on the synchronization accuracy between the sensor nodes. In this paper we describe a new algorithm that achieves an ultra-precise wireless frequency synchronization using OFDM signals. Especially for indoor environments, which typically suffer from multi-path propagation, significant improvements compared to state-of-the-art algorithms can be reached. An additional benefit of our algorithm is the low complexity, which allows for low-cost implementations. After the explanation of our concept and the algorithms, its simulated performance is evaluated.