<p>While global navigation satellite system (GNSS)technologies have always been the go-to solution for localizationproblems, they may not be the best choice for some Internet-of-Things (IoT) applications due to the incurred power consumptionand cost. In this paper, we present an alternative satellite-basedlocalization method exploiting the signature of Doppler shifts andangle of arrival measurements as seen by a receiving satellite.We first derive the joint likelihood function of the measurements,which is represented as a combination of three Gaussian distri-butions. Then, we show that the maximum likelihood problemreduces to a more-efficient mean squared error minimizationin the Gaussian case as inferred from real measurements wecollected from low-earth-orbit satellite using a tracking groundstation. Thus, we propose utilizing a stochastic optimizer tosearch for the global minimum of the mean squared error whichrepresents the location of the ground IoT device as estimated bythe satellite platform. The emulated results show that the IoTdevice localization, under such realistic model, can be performedwith sufficient accuracy for IoT applications.</p>
<p>While global navigation satellite system (GNSS)technologies have always been the go-to solution for localizationproblems, they may not be the best choice for some Internet-of-Things (IoT) applications due to the incurred power consumptionand cost. In this paper, we present an alternative satellite-basedlocalization method exploiting the signature of Doppler shifts andangle of arrival measurements as seen by a receiving satellite.We first derive the joint likelihood function of the measurements,which is represented as a combination of three Gaussian distri-butions. Then, we show that the maximum likelihood problemreduces to a more-efficient mean squared error minimizationin the Gaussian case as inferred from real measurements wecollected from low-earth-orbit satellite using a tracking groundstation. Thus, we propose utilizing a stochastic optimizer tosearch for the global minimum of the mean squared error whichrepresents the location of the ground IoT device as estimated bythe satellite platform. The emulated results show that the IoTdevice localization, under such realistic model, can be performedwith sufficient accuracy for IoT applications.</p>
<p>Many Internet-of-Things (IoT)-over-satellite applications rely on affordable location-aware but energy-constrained IoT sensors. In this paper, we propose a novel method to estimate the satellite visibility window in IoT devices based on simple Doppler measurements. We present two scenarios where the orbital information of the serving satellite's is initially unknown to the IoT device: (i) we assume that the geographic coordinates are known to the IoT device, and (ii) we assume that the coordinates are completely unknown. Accordingly, we derive the Doppler measurement likelihood function, and simplify it to a root mean square error (RMSE) minimization problem. From a sequence of Doppler measurements, we estimate the orbital parameters of the serving satellite using a stochastic optimizer to minimize the RMSE. From the orbital estimation, we then predict the satellite visibility window (satellite pass). To gauge the accuracy of the window estimation, we apply the intersection-over-union metric to compute the overlapping visibility window between the ground truth and the estimation, and consequently present the results based on extensive Monte Carlo simulations.</p>
<p>Many Internet-of-Things (IoT)-over-satellite applications rely on affordable location-aware but energy-constrained IoT sensors. In this paper, we propose a novel method to estimate the satellite visibility window in IoT devices based on simple Doppler measurements. We present two scenarios where the orbital information of the serving satellite's is initially unknown to the IoT device: (i) we assume that the geographic coordinates are known to the IoT device, and (ii) we assume that the coordinates are completely unknown. Accordingly, we derive the Doppler measurement likelihood function, and simplify it to a root mean square error (RMSE) minimization problem. From a sequence of Doppler measurements, we estimate the orbital parameters of the serving satellite using a stochastic optimizer to minimize the RMSE. From the orbital estimation, we then predict the satellite visibility window (satellite pass). To gauge the accuracy of the window estimation, we apply the intersection-over-union metric to compute the overlapping visibility window between the ground truth and the estimation, and consequently present the results based on extensive Monte Carlo simulations.</p>
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