Separating and tracking multiple beacon sources for deep space optical communications
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Abstract:
We propose a solution for pointing and tracking an optical terminal using one or more beacons and a slowly varying background image. The primary application is a deep space optical communication terminal, where multiple source tracking provides robustness against beacon outage. Our solution uses optical orthogonal codes modulated on each beacon to separate the signal from each source for centroiding. This technique allows calculation of the transmit pointing vector from each beacon location as well as from the background image. The latter can be used to track during beacon outages. We present a simple algorithm for performing this separation, and apply it to experimental data from a photon-counting detector illuminated by two beacons and one constant source. Our results show that the photon flux from each source can be accurately estimated even in the low signal, high background regime. We estimate the variance of the signal estimator due to Poisson fluctuations and infer the effect on a centroid estimator for tracking.Keywords:
Beacon
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Centroid
Source tracking
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Localization is a critical issue in wireless sensor networks. In most localization schemes, there are beacons being placed as references to determine the positions of objects or events appearing in the sensing field. The underlying assumption is that beacons are always static. In this work, we define a new Beacon Movement Detection (BMD) problem. Assuming that there are unnoticed changes of locations of some beacons in the system, this problem is concerned about how to automatically monitor such situations and identify these beacons. Removal of such beacons in the positioning engine may improve the localization accuracy. Two schemes are proposed to solve the BMD problem. Finally, we evaluate how these solutions can improve the accuracy of localization schemes in case that there are unnoticed movement of some beacons. Simulation results show that our solutions alleviate 53% the decrease of positioning accuracy caused by the exceptional beacon movement.
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Electric beacon
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The principle of a gate centroid tracking algorithm is introduced.Its tracking error model was established.The error of the gate centroid tracking algorithm in infrared imaging was discussed and analyzed.The centroid tracking error decreases when there is less misjudgment probability,more target pixels and fewer background pixels in the gate.The correct match coefficient increases when there is a greater SNR,fewer correlated searches and more pixels in correlated calculation.Test results show that the algorithm can reduce tracking error,improve tracking accuracy and stability.
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Compared to the instantaneous mobile source tracking, the mobile diffusion source tracking is more difficult. In this paper, we give a study on the mobile diffusion source tracking in sensor networks. The CPA realtime localization method, the centroid realtime localization algorithm, the analytic realtime localization algorithm and the tracking method based on PF(Particle Filter) are presented to solve the mobile diffusion source tracking problem. The preconditions, advantages and deficiencies of the methods are given. The performances of different tracking methods are compared in simulations when node densities and sampling intervals are different. The results show that all the proposed methods are valid, while the tracking method based on PF is the most robust method compared to others.
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Plume tracking has to deal with tracking strategy and geometrical design of sensors array. This research paper proposes a Priority base (PB) algorithm, modeling the insect behavior to localize the odor source and acquire better response from sensor array. The sensor array designed includes five gas sensors (three TGS 813 and two MQ2) and three temperature sensor used to track wind. The array in mounted on a movable platform Mokhtar [7] for tracking the plume and localizing the source.
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In local positioning systems (LPS) that use active beacons placed in the environment, the knowledge of the precise position of these beacons is supposed by the positioning algorithms. Previously the positions of the beacons have been directly measured or indirectly deduced and entered to the positioning system. In this work, a self-calibration method for LPS is presented that allows the deduction of the position of a number of N beacons from measurements of distances between these beacons and different test points in the area of measurements. Only the positions of three test points (in 3D positioning) have to be previously known, the rest (the number depending on the number of beacons) could be at any unknown position. The method has been developed for both spherical and hyperbolic positioning systems. In both cases simulated and real results have been presented.
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Localization is a fundamental research issue in wireless sensor networks (WSNs). In most existing localization schemes, several beacons are used to determine the locations of sensor nodes. These localization mechanisms are frequently based on an assumption that the locations of beacons are known. Nevertheless, for many WSN systems deployed in unstable environments, beacons may be moved unexpectedly; that is, beacons are drifting, and their location information will no longer be reliable. As a result, the accuracy of localization will be greatly affected. In this paper, we propose a distributed beacon drifting detection algorithm to locate those accidentally moved beacons. In the proposed algorithm, we designed both beacon self-scoring and beacon-to-beacon negotiation mechanisms to improve detection accuracy while keeping the algorithm lightweight. Experimental results show that the algorithm achieves its designed goals.
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In this study, the repeated covering and uncovering by the hands of a beacon before one's eyes is performed, and the identification of the corresponding beacon is obtained based on the changes in the received signal strength indicator at the control terminal. Recently, beacons that employ Bluetooth Low Energy (BLE) have spread, and are used for a variety of purposes. However, when one wishes to control several beacons, there are no clear means for readily identifying each beacon. In the proposed method, the changes in the received signal strength indicator of BLE are used to identify the BLE beacon. The output radio signals of BLE are weak, and almost all BLE beacons are small enough to fit in one's hand. If the beacon is covered with both hands, the signals of BLE are attenuated. In this study, the algorithms for beacon identification were examined, and control applications were prepared. In addition, it was possible to identify within 13 s the target beacon among several beacons.
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The optimum uncertain field tracking algorithm (OUFTA) incorporates knowledge of source motion as well as uncertainty in the propagation environment [S. L. Tantum and L. W. Nolte, ‘‘Tracking and localizing a moving source in an uncertain shallow-water environment,’’ J. Acoust. Soc. Am. 103, 362–373 (1998)]. It is shown how detection, tracking, and adaptivity to an uncertain environment can be formulated within a unifying and optimal framework. Performance results and trade-offs are presented for simulated data. Results are also presented using this approach with low signal-to-noise ratios and real data (SWELLEX). [Research supported by ONR.]
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This paper presents a localization system that uses infrared beacons and a camera equipped with an optical band-pass filter. Our system can reliably detect and identify individual beacons at 100m distance regardless of lighting conditions. We describe the camera and beacon design as well as the image processing pipeline in detail. In our experiments, we investigate and demonstrate the ability of the system to recognize our beacons in both daytime and nighttime conditions. High precision localization is a key enabler for automated vehicles but remains unsolved, despite strong recent improvements. Our low-cost, infrastructure-based approach helps solve the localization problem. All datasets are made available.
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In this paper,a double tracking algorithm target tracking system based on DM6437-355 development board combining with OpenCV and VC technology is designed. Both the centroid tracking algorithm and the center of gravity tracking algorithm are set into the system,the centroid tracking algorithm is an improvement based on threshold image segmentation pretreatment,the center of gravity tracking algorithm is proposed for setting different weights I(x,y) for each sample point,it is an improvement solving the coordinates of the center of gravity. The phenomenon has been significantly improved,that the tracked target is lost by the target rolling and deforming and partially being shaded. A different tracking algorithm depending on different situations is chosen,the target tracking results in the midst of a real environment is strengthened.
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