Abstract: A system and method using a hybrid-weighted, multi-gridded robust approach to localize a sound source are more accurate and robust that conventional techniques. In one embodiment, the system may determine a distance to the acoustic source using a first grid and weighting combination, may determine an angle to the acoustic source using a second grid and weighting combination and may combine the distance and angle estimates to generate a more robust and accurate acoustic source location determination.

Abstract: Systems and methods for unsupervised indoor localization are provided. Sensor data obtained from a device carried by a user can be used to simultaneously estimate the indoor location of a user and identify landmarks within the indoor environment based on their signatures. The landmarks can be used to reset the location estimate of the user, and the location estimate of the user can be used to improve the learned location of the landmarks. This recursive process leads to excellent accuracy in indoor localization. Systems and methods for estimating the heading direction of a user are also provided. Sensor data obtained from the user can be used to analyze the forces acting on the user in order to give an accurate heading direction estimate.

Abstract: Systems and methods for unsupervised indoor localization are provided. Sensor data obtained from a device carried by a user can be used to simultaneously estimate the indoor location of a user and identify landmarks within the indoor environment based on their signatures. The landmarks can be used to reset the location estimate of the user, and the location estimate of the user can be used to improve the learned location of the landmarks. This recursive process leads to excellent accuracy in indoor localization. Systems and methods for estimating the heading direction of a user are also provided. Sensor data obtained from the user can be used to analyze the forces acting on the user in order to give an accurate heading direction estimate.

Abstract: In a wireless communication network, the location of an addressable receiver relative to the locations of a plurality of addressable sources of electromagnetic radiation is found using probabilistic models of the signal strength measured at the addressable receiver. The inventive method provides location determination on a finer spatial scale than was heretofore available. A region of interest is calibrated via a discrete-space radio map storing probability distributions of received signal strength at the measurement locations. The stored probability distributions are compensated for temporal variability and biases, such as through temporal correlations of the sampled received signal strength.

Abstract: A method of managing a network of sensors in an energy aware manner includes the steps of clustering the sensors to minimize energy consumption, routing the network (step 44), modeling the energy available at each sensor (step 45), and rerouting the network (step 47) when the sensor battery level drops to a predetermined value, or when the energy model is adjusted because it deviates from a sensor's actual energy state.

Abstract: In a wireless communication network, the location of an addressable receiver relative to the locations of a plurality of addressable sources of electromagnetic radiation is found using probabilistic models of the signal strength measured at the addressable receiver. The inventive method provides location determination on a finer spatial scale than was heretofore available. A region of interest is calibrated via a discrete-space radio map storing probability distributions of received signal strength at the measurement locations. The stored probability distributions are compensated for temporal variability and biases, such as through temporal correlations of the sampled received signal strength.

Abstract: A method of managing a network of sensors in an energy aware manner includes the steps of clustering the sensors to minimize energy consumption, routing the network (step 44), modeling the energy available at each sensor (step 45), and rerouting the network (step 47) when the sensor battery level drops to a predetermined value, or when the energy model is adjusted because it deviates from a sensor's actual energy state.