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: 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 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: 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: A computer implemented method selects K extreme elements of a list of N elements by partitioning each of the N elements into a plurality of sections. For each section the method selects a threshold selection determining at least K extreme entries from the list. This iteratively compares a corresponding section to a section threshold, counts a number of sections which are more extreme than the section threshold, increasing (or decreasing) the section threshold if the count is greater than K and decreasing (or increasing) the section threshold if the count is less than K. The method forms a combined threshold by concatenation of said section thresholds in order, compares each of the N elements to the combined threshold, and selects at least K elements from the set of N elements more extreme than the combined threshold.

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 computer implemented method selects K extreme elements of a list of N elements by partitioning each of the N elements into a plurality of sections. For each section from a most significant section to a least significant section the method selects a threshold selection determining at least K extreme entries from the list. This iteratively compares a corresponding section to a section threshold, counts a number of sections which are more extreme than the section threshold, increasing (or decreasing) the section threshold if the count is greater than K and decreasing the section threshold if the count is less than K. After finding the section threshold for the corresponding section the method forms a combined threshold by concatenation of said section thresholds in order from a most significant section to a least significant section, compares each of the N elements to the combined threshold, and selects at least K elements from the set of N elements more extreme than the combined threshold.

Abstract: A technique for reducing peak-to-average power ratio (PAPR) in digital signal communications is disclosed. In one particular exemplary embodiment, the technique may be realized by a method comprising the steps of modulating a frequency-domain input vector comprising a plurality of path vectors by applying a Gray code sequence of combinations of binary phase rotations to the plurality of path vectors, where, in each combination, the binary phase rotation of only one of the plurality of path vectors is changed from a previous combination in the Gray code sequence; and determining an optimal time-domain peak value based on the modulated frequency-domain input vector.