Jointly Optimizing Placement and Inference for Beacon-based Localization

The ability of robots to estimate their location is crucial for a wide variety of autonomous operations. In settings where GPS is unavailable, measurements of transmissions from fixed beacons provide an effective means of estimating a robot's location as it navigates. The accuracy of such a beacon-based localization system depends both on how beacons are distributed in the environment, and how the robot's location is inferred based on noisy and potentially ambiguous measurements. We propose an approach for making these design decisions automatically and without expert supervision, by explicitly searching for the placement and inference strategies that, together, are optimal for a given environment. Since this search is computationally expensive, our approach encodes beacon placement as a differential neural layer that interfaces with a neural network for inference. This formulation allows us to employ standard techniques for training neural networks to carry out the joint optimization. We evaluate this approach on a variety of environments and settings, and find that it is able to discover designs that enable high localization accuracy.Comment: Appeared at 2017 International Conference on Intelligent Robots and Systems (IROS

Real-time localization using received signal strength

Locating and tracking assets in an indoor environment is a fundamental requirement for several applications which include for instance network enabled manufacturing. However, translating time of flight-based GPS technique for indoor solutions has proven very costly and inaccurate primarily due to the need for high resolution clocks and the non-availability of reliable line of sight condition between the transmitter and receiver. In this dissertation, localization and tracking of wireless devices using radio signal strength (RSS) measurements in an indoor environment is undertaken. This dissertation is presented in the form of five papers. The first two papers deal with localization and placement of receivers using a range-based method where the Friis transmission equation is used to relate the variation of the power with radial distance separation between the transmitter and receiver. The third paper introduces the cross correlation based localization methodology. Additionally, this paper also presents localization of passive RFID tags operating at 13.56MHz frequency or less by measuring the cross-correlation in multipath noise from the backscattered signals. The fourth paper extends the cross-correlation based localization algorithm to wireless devices operating at 2.4GHz by exploiting shadow fading cross-correlation. The final paper explores the placement of receivers in the target environment to ensure certain level of localization accuracy under cross-correlation based method. The effectiveness of our localization methodology is demonstrated experimentally by using IEEE 802.15.4 radios operating in fading noise rich environment such as an indoor mall and in a laboratory facility of Missouri University of Science and Technology. Analytical performance guarantees are also included for these methods in the dissertation --Abstract, page iv

Target Tracking Using Wireless Sensor Networks

Tracking of targets in remote inaccessible areas is an important application of Wireless Sensor Networks (WSNs). The use of wired networks for detecting and tracking of intruders is not feasible in hard-to-reach areas. An alternate approach is the use of WSNs to detect and track targets. Furthermore, the requirements of the tracking problem may not necessarily be known at the time of deployment. However, issues such as low onboard power, lack of established network topology, and the inability to handle node failures have limited the use of WSNs in these applications. In this dissertation, the performance of WSNs in remote surveillance type of applications will be addressed through the development of distributed tracking algorithms. The algorithm will focus on identifying a minimal set of nodes to detect and track targets, estimating target location in the presence of measurement noise and uncertainty, and improving the performance of the WSN through distributed learning.The selection of a set of sensor nodes to detect and track a target is first studied. Inactive nodes are forced into `sleeping' mode to conserve power, and activated only when required to sense the target. The relative distance and angle of the target from sensor nodes are used to determine which of the sensors are needed to track the target.The effect of noisy measurements on the estimation of the position of the target is addressed through the implementation of a Kalman filter. Contrary to centralized Kalman filter implementations reported in the literature, implementation of the distributed Kalman filter is considered in the proposed solution.Distributed learning is implemented by passing on the knowledge of the target, i.e. the filter state and covariance matrix onto the subsequent node running the filter. The problem is mathematically formulated, and the stability and tracking error of the proposed strategy are rigorously examined. Numerical examples are then used to demonstrate the utility of the proposed technique.It will be shown by mathematical proofs and numerical simulation in this dissertation that distributed detection and tracking using a limited number of nodes can result in efficient tracking in the presence of measurement noise. Furthermore, minimizing the number of active sensors will reduce communication overhead and power consumption in networks, improve tracking efficiency, and increase the useful life span of WSNs