Abstract: Systems and methods for classifying a modulation scheme of a wireless signal are described. In some embodiments, a system receives a wireless signal modulated based on a modulation scheme having a constant modulus. The system can generate a resampled signal from the wireless signal based on features extracted from the wireless signal and perform blind equalization on the resampled signal based on a constant modulus criterion to generate an equalized signal. Then, the system can cause a modulation classifier to classify the received wireless signal to a modulation scheme from a plurality of predetermined modulation schemes based on the equalized signal. By preconditioning the wireless signal to reduce feature variability imparted by a propagation channel onto the wireless signal, the system can increase the classification accuracy of the modulation classifier.

Abstract: Systems and methods for classifying a modulation scheme of a wireless signal are described. In some embodiments, a system receives a wireless signal modulated based on a modulation scheme having a constant modulus. The system can generate a resampled signal from the wireless signal based on features extracted from the wireless signal and perform blind equalization on the resampled signal based on a constant modulus criterion to generate an equalized signal. Then, the system can cause a modulation classifier to classify the received wireless signal to a modulation scheme from a plurality of predetermined modulation schemes based on the equalized signal. By preconditioning the wireless signal to reduce feature variability imparted by a propagation channel onto the wireless signal, the system can increase the classification accuracy of the modulation classifier.

Abstract: Systems and methods for classifying a modulation scheme of a wireless signal are described. In some embodiments, a system receives a wireless signal modulated based on a modulation scheme having a constant modulus. The system can generate a resampled signal from the wireless signal based on features extracted from the wireless signal and perform blind equalization on the resampled signal based on a constant modulus criterion to generate an equalized signal. Then, the system can cause a modulation classifier to classify the received wireless signal to a modulation scheme from a plurality of predetermined modulation schemes based on the equalized signal. By preconditioning the wireless signal to reduce feature variability imparted by a propagation channel onto the wireless signal, the system can increase the classification accuracy of the modulation classifier.

Abstract: Systems and methods for classifying a modulation scheme of a wireless signal are described. In some embodiments, a system receives a wireless signal modulated based on a modulation scheme having a constant modulus. The system can generate a resampled signal from the wireless signal based on features extracted from the wireless signal and perform blind equalization on the resampled signal based on a constant modulus criterion to generate an equalized signal. Then, the system can cause a modulation classifier to classify the received wireless signal to a modulation scheme from a plurality of predetermined modulation schemes based on the equalized signal. By preconditioning the wireless signal to reduce feature variability imparted by a propagation channel onto the wireless signal, the system can increase the classification accuracy of the modulation classifier.

Abstract: A method and system of reliably detecting a reactive jamming attack and estimating the jammer's listening interval for exploitation by a communication system comprises channelizing one or more signals of interest (SOI), channelizing one or more signals of unknown origin (SUO), identifying frequency support patterns for the SOI and SUO using Bayes thresholds, comparing SOI and SUO detection map histories, and determining a percent match, where a match percentage above a specified minimum indicates a reactive attack. Edge detection can be used to enhance jammer support. Embodiments further detect reactive jammer adaptation to changes in the SOI's frequency support. Embodiments include detectors that are insensitive to jammer modulation and/or signal type. A jammer reaction delay and/or size and periodicity of receive window can be detected. Embodiments determine if a jammer is copying and retransmitting the SOI's waveform(s), and/or if the jammer is anticipatory.

Abstract: Jammer behavior modeling utilizes two-layer hidden Markov models (HMMs) for identifying an interferer's plurality of modes and accumulating statistics on transitions between the interferer's plurality of modes for use in improved jammer characterization. The two-layer hidden Markov model characterizes jammer behavior by estimating time-varying but repetitive (mode-cycling) jammer behavior, providing estimates of future states for use by a strategy optimizer. Steps include receiving input data from an interferer; determining if models exist for describing the interferer's behavior; determining if a new model is needed; building a first layer HMM for each state of the interferer; building a second layer HMM using an output from the first layer HMM; and outputting the results from the first and second layer HMMs to a strategy optimizer to identify an interferer's plurality of modes and accumulate statistics on transitions between the interferer's plurality of modes for use in jammer mode prediction.

Abstract: A method and system of reliably detecting a reactive jamming attack and estimating the jammer's listening interval for exploitation by a communication system comprises channelizing one or more signals of interest (SOI), channelizing one or more signals of unknown origin (SUO), identifying frequency support patterns for the SOI and SUO using Bayes thresholds, comparing SOI and SUO detection map histories, and determining a percent match, where a match percentage above a specified minimum indicates a reactive attack. Edge detection can be used to enhance jammer support. Embodiments further detect reactive jammer adaptation to changes in the SOI's frequency support. Embodiments include detectors that are insensitive to jammer modulation and/or signal type. A jammer reaction delay and/or size and periodicity of receive window can be detected. Embodiments determine if a jammer is copying and retransmitting the SOI's waveform(s), and/or if the jammer is anticipatory.

Abstract: Techniques for multiple access differential M-ary coding applications that can be optionally aided by multiuser detection (MUD) are disclosed. An initial hopped feature decoding is performed to provide data estimates for each user represented in a received co-channel signal. Interference cancellation can then be performed using MUD, thereby providing an interference-cancelled signal. Data estimates remaining are then re-decoded. Iteration on the interference cancellation and re-decoding can be carried out to satisfy a particular rule of iteration, although iteration is not always necessary. The final decoded signal can then be provided to its destination.

Abstract: A multiple access processing system for a plurality of users employing a multiuser detector processing partial blocks of data within a window. The multiuser detector processes the data for all users within a processing window and is interrupted at successive frame boundaries. Once an entire block is processed by the MUD, the blocks for that user are decoded and the processing continues.

Abstract: Techniques for multiple access differential frequency-hopped spread spectrum (MA-DFHSS) aided by multiuser detection (MUD) are disclosed. An initial DFH decoding is performed to provide data estimates for each user represented in a received co-channel signal. Interference cancellation is then performed using MUD, thereby providing an interference-cancelled signal. Data estimates remaining are then re-decoded. Iteration on the interference cancellation and re-decoding can be carried out to satisfy a particular rule of iteration, although iteration is not always necessary. The final decoded signal can then be provided to its destination.

Abstract: A Turbo multi-user detector (MUD) processing system in multiple access communications channels that decreases the likelihood of improper decoding of the final values of interest, thereby allowing for a reduction in the number of iterations performed and lowers complexity without negatively impacting performance. The present invention comprises a multi-user detector serially concatenated to two decoder sections in such a manner that data flows iteratively from the MUD and to each detector stage and back to the MUD to correct for errors.

Abstract: Techniques for multiple access differential M-ary coding applications that can be optionally aided by multiuser detection (MUD) are disclosed. An initial hopped feature decoding is performed to provide data estimates for each user represented in a received co-channel signal. Interference cancellation can then be performed using MUD, thereby providing an interference-cancelled signal. Data estimates remaining are then re-decoded. Iteration on the interference cancellation and re-decoding can be carried out to satisfy a particular rule of iteration, although iteration is not always necessary. The final decoded signal can then be provided to its destination.