Abstract:
Methods, devices and instruction-carrying storage operate to track a target object over time and space. The tracking techniques involve obtaining a point cloud of reflection points at time n, a target from time n−1, state information including previous location information for the target and previous group distribution for previous reflection points associated with the target at time n−1; predicting a location of the target at time n based on the state information; determining a gate around the target and which of the multiple reflection points are within the gate; determining, for each of the multiple reflection points determined to be within the gate, a likelihood that the corresponding reflection point is associated with the target; determining current group distribution for the reflection points determined to likely be associated with the target; and outputting the determined current group distribution and current location information of the target.
Abstract:
Techniques for target tracking that include obtaining state information for a first target object, the state information including previous location information for the first target object and a previous group distribution for points associated with the first target object at a previous point in time, predicting a location for the first target object based on the obtained state information, receiving a first set of points, identifying a first distribution of points, from the first set of points based on the predicted location to associate one or more first points of the first distribution of points with the target object, determining a current group distribution for the points associated with the first target object, and outputting a current location information and a current group distribution point.
Abstract:
A wireless device includes a preamble detector configured to identify a plurality of preambles transmitted via a random access channel of a wireless network. The preamble detector includes a noise floor estimator. The noise floor estimator is configured to: estimate, for a given preamble root sequence identified by the preamble detector, a noise floor value as mean energy of received signal samples, excluding detected preamble samples on the give preamble root sequence, below a noise floor threshold assigned to the given preamble root sequence. The noise floor estimator is configured to compute the noise floor threshold as a product of: average energy of the received signal samples less total signal energy contained in each cyclic prefix window in which a preamble is detected using the given preamble root sequence; and a predetermined normalized relative noise floor threshold based on a target false preamble detection rate.
Abstract:
A wireless device includes a preamble detector configured to identify a plurality of preambles transmitted via a random access channel of a wireless network. The preamble detector includes a noise floor estimator. The noise floor estimator is configured to: estimate, for a given preamble root sequence identified by the preamble detector, a noise floor value as mean energy of received signal samples, excluding detected preamble samples on the give preamble root sequence, below a noise floor threshold assigned to the given preamble root sequence. The noise floor estimator is configured to compute the noise floor threshold as a product of: average energy of the received signal samples less total signal energy contained in each cyclic prefix window in which a preamble is detected using the given preamble root sequence; and a predetermined normalized relative noise floor threshold based on a target false preamble detection rate.
Abstract:
A wireless device includes a preamble detector configured to identify preambles transmitted via a random access channel of a wireless network. The preamble detector includes preamble false alarm logic. The preamble false alarm logic is configured to set a preamble false alarm detection window, and compare, to one another, preambles identified in the false alarm detection window. The preamble false alarm logic is configured to identify, based on the comparison, a largest of the preambles in the false alarm detection window, and reject all but the identified largest of the preambles as false alarm detections.
Abstract:
Methods, devices and instruction-carrying storage operate to track a target object over time and space. The tracking techniques involve obtaining a point cloud of reflection points at time n, a target from time n−1, state information including previous location information for the target and previous group distribution for previous reflection points associated with the target at time n−1; predicting a location of the target at time n based on the state information; determining a gate around the target and which of the multiple reflection points are within the gate; determining, for each of the multiple reflection points determined to be within the gate, a likelihood that the corresponding reflection point is associated with the target; determining current group distribution for the reflection points determined to likely be associated with the target; and outputting the determined current group distribution and current location information of the target.
Abstract:
A wireless device includes a preamble detector configured to identify preambles transmitted via a random access channel of a wireless network. The preamble detector includes preamble false alarm logic. The preamble false alarm logic is configured to set a preamble false alarm detection window, and compare, to one another, preambles identified in the false alarm detection window. The preamble false alarm logic is configured to identify, based on the comparison, a largest of the preambles in the false alarm detection window, and reject all but the identified largest of the preambles as false alarm detections.