Abstract: A method for determining a 3-dimensional (3D) attitude of a platform includes receiving satellite relayed information regarding an ambiguous phase single-difference measurement (?); resolving a phase ambiguity of the ambiguous phase single-difference measurement (?) to determine an unambiguous phase single-difference estimate (?); calculating coarse direction vectors xcor and ycor based on the unambiguous phase single-difference estimate (?); estimating improved direction vectors x and y based on the coarse direction vectors xcor and ycor and by imposing constraints on the improved direction vectors x and y and an angle between the improved direction vectors x and y; and calculating the 3D attitude of the platform from the improved direction vectors x and y.

Abstract: Beamformers and beamforming methods are disclosed which involve diagonal loading (regularization). Features may include the automatic determination of the regularization parameter using a linearly constrained minimum power (LCMP) bounded perturbation regularization (BPR) approach.

Abstract: Beamformers and beamforming methods involve diagonal loading (regularization). Significant features include the automatic determination of the regularization parameter using a linearly constrained minimum power (LCMP) bounded perturbation regularization (BPR) approach. More specifically, the method has the advantage of efficient beamforming when the desired signal is from a moving source. After determining an initial regularization parameter, each data window (i.e., snapshot) uses the regularization parameter of the prior window to recursively determine the next regularization parameter.

Abstract: A method for determining a 3-dimensional (3D) attitude of a platform includes receiving satellite relayed information regarding an ambiguous phase single-difference measurement (?); resolving a phase ambiguity of the ambiguous phase single-difference measurement (?) to determine an unambiguous phase single-difference estimate (?); calculating coarse direction vectors xcor and ycor based on the unambiguous phase single-difference estimate (?); estimating improved direction vectors x and y based on the coarse direction vectors xcor and ycor and by imposing constraints on the improved direction vectors x and y and an angle between the improved direction vectors x and y; and calculating the 3D attitude of the platform from the improved direction vectors x and y.

Abstract: Various examples are provided for node estimation and topology discovery for networks. In one example, a method includes receiving a packet having an identifier from a first node; adding the identifier to another transmission packet based on a comparison between the first identifier and existing identifiers associated with the other packet; adjusting a transmit probability based on the comparison; and transmitting the other packet based on a comparison between the transmit probability and a probability distribution. In another example, a system includes a network device that can adds an identifier received in a packet to a list including existing identifiers and adjust a transmit probability based on a comparison between the identifiers; and transmit another packet based on a comparison between the transmit probability and a probability distribution. In another example, a method includes determining a quantity of sensor devices based on a plurality of identifiers received in a packet.

Abstract: Various examples are provided for node estimation and topology discovery for networks. In one example, a method includes receiving a packet having an identifier from a first node; adding the identifier to another transmission packet based on a comparison between the first identifier and existing identifiers associated with the other packet; adjusting a transmit probability based on the comparison; and transmitting the other packet based on a comparison between the transmit probability and a probability distribution. In another example, a system includes a network device that can adds an identifier received in a packet to a list including existing identifiers and adjust a transmit probability based on a comparison between the identifiers; and transmit another packet based on a comparison between the transmit probability and a probability distribution. In another example, a method includes determining a quantity of sensor devices based on a plurality of identifiers received in a packet.

Abstract: The peak detection method using blind source separation extracts true peaks from noisy peaks in a more robust way that does not require any a priori information. Information regarding true peak location is obtained by thresholding the output of a wavelet transform. The value of the threshold is dependent on noise variance. While noise variance is normally unknown, the present method implements a blind source separation technique to calculate the noise variance. The blind source separation technique does not require information of the incoming signal or the channel noise, and hence is suitable for CR (cognitive radio) peak detection.

Abstract: The peak detection method using blind source separation extracts true peaks from noisy peaks in a more robust way that does not require any a priori information. Information regarding true peak location is obtained by thresholding the output of a wavelet transform. The value of the threshold is dependent on noise variance. While noise variance is normally unknown, the present method implements a blind source separation technique to calculate the noise variance. The blind source separation technique does not require information of the incoming signal or the channel noise, and hence is suitable for CR (cognitive radio) peak detection.

Abstract: The method of performing structure-based Bayesian sparse signal reconstruction is a Bayesian approach to sparse signal recovery that has relatively low complexity and makes a collective use of: a priori statistical properties of the signal and noise; sparsity information; and the rich structure of the sensing matrix ?. The method is used with both Gaussian and non-Gaussian (or unknown) priors, and performance measures of the ultimate estimate outputs are easily calculated.

Abstract: The OFDM peak-to-average power ratio reduction apparatus and method provides a compressive sensing algorithm that estimates the sparsity pattern of a sparse vector by a limited number of measurements. When the positions of the clipped peaks are known beforehand by, e.g., a genie-augmented receiver, then the algorithm optimally performs amplitude estimation utilizing a least squares estimation technique. When the cardinality of the peak-reducing signal is known at the receiver at initialization, the receiver optimizes the estimated peak-reducing signal by using least squares.

Abstract: The present invention relates to impulse noise estimation and removal in orthogonal frequency division multiplexing (OFDM) transmissions, and particularly to transmissions in power line communications and digital subscriber line (DSL) transmissions. The method includes the steps of: modulating data to be transmitted; performing an inverse fast Fourier transform; inserting a cyclic prefix into the data; transmitting the data as a set of OFDM symbols via a channel; receiving the set of OFDM symbols; removing the cyclic prefix; performing a fast Fourier transform; estimating impulse noise; canceling the impulse noise based upon the estimated impulse noise to produce a set of impulse noise-free data; estimating the channel; and demodulating and detecting the data transmitted based upon the estimated channel and the set of impulse noise-free data.

Abstract: The cyclic prefix-based enhanced data recovery method retains the cyclic prefix (CP) upon reception and routes the CP to a data detection module to enhance the operation of the orthogonal frequency division multiplexed (OFDM) receiver whether operating in the blind, semi-blind, training, or perfectly known channel modes. Processing of the OFDM symbol and the CP is performed in the data detector and obtains data recovery by computing a maximum likelihood estimation based on the CP and the OFDM symbols.

Abstract: The OFDM peak-to-average power ratio reduction apparatus and method provides a compressive sensing algorithm that estimates the sparsity pattern of a sparse vector by a limited number of measurements. When the positions of the clipped peaks are known beforehand by, e.g., a genie-augmented receiver, then the algorithm optimally performs amplitude estimation utilizing a least squares estimation technique. When the cardinality of the peak-reducing signal is known at the receiver at initialization, the receiver optimizes the estimated peak-reducing signal by using least squares.

Abstract: The present invention relates to impulse noise estimation and removal in orthogonal frequency division multiplexing (OFDM) transmissions, and particularly to transmissions in power line communications and digital subscriber line (DSL) transmissions. The method includes the steps of: modulating data to be transmitted; performing an inverse fast Fourier transform; inserting a cyclic prefix into the data; transmitting the data as a set of OFDM symbols via a channel; receiving the set of OFDM symbols; removing the cyclic prefix; performing a fast Fourier transform; estimating impulse noise; canceling the impulse noise based upon the estimated impulse noise to produce a set of impulse noise-free data; estimating the channel; and demodulating and detecting the data transmitted based upon the estimated channel and the set of impulse noise-free data.