10 results about "Cyclic correlation" patented technology

Embodiments include methods for a radar receiver, including receiving a composite signal corresponding to a transmitted radar signal. The composite signal comprises a plurality (M) of signal streams corresponding to a respective plurality (M) of signal pulses in the transmitted radar signal. Each signal pulse includes a main pulse and a cyclic extension (CE). Each signal stream includes delayed samples corresponding to a plurality of propagation delays of the corresponding signal pulse. Such methods include performing a cyclic correlation of each signal stream with a replica of the main pulse of the corresponding signal pulse, thereby generating a delay-time plane comprising a plurality (M) of correlation streams including samples corresponding to the plurality of propagation delays. Such methods include performing a plurality of DFTs on the correlation streams to generate a delay-Doppler plane. Such methods include searching the delay-Doppler plane for magnitude peaks corresponding to detected objects.

This invention discloses a low-power DTMB transmitter carrier recovery and constellation mapping enhancement method and platform, belonging to the field of terrestrial digital television broadcasting technology. The transmitter samples feedback signals from the intermediate frequency (IF), RF preamplifier, or power amplifier output. Based on the PN420, PN595, or PN945 frame header characteristics, it performs cyclic correlation, sliding correlation, or segmented correlation to obtain frequency offset, phase error, and correlation confidence, and drives a digital phase-locked loop to complete transmitter carrier self-calibration. The system generates non-uniform pre-scaling and pre-rotation parameters based on the feedback constellation error, causing the antenna port constellation to converge to a standard DTMB constellation; enhanced constellation mapping is enabled when supported by a dedicated receiver. The low-power control module reduces power consumption and improves transmission quality based on correlation confidence, EVM scheduling correlator, phase calculation, constellation pre-compensation, digital predistortion, and Doherty power amplifier bias.

The embodiment of the invention provides a sampling control period calculation method of a self-balancing robot and related equipment. The method comprises the following steps: acquiring a kinetic model and motor control input information of the self-balancing robot; based on the kinetic model and the motor control input information, calculating sampling control description information of the self-balancing robot for any interval moment included in the preset sampling interval; acquiring an instantaneous energy functional, a cyclic correlation energy functional and an interval accumulated energy functional corresponding to any interval moment of the self-balancing robot, and constructing cyclic energy constraint information based on the instantaneous energy functional, the cyclic correlation energy functional and the interval accumulated energy functional; and performing negative qualitative analysis on the cyclic energy constraint information in combination with the sampling control description information, and calculating a sampling control period corresponding to the self-balancing robot. Therefore, the maximum allowable sampling control period can be obtained through calculation, and communication and calculation resources are saved while stable operation of the system is ensured.

The application discloses a kind of integrated signal generation and multi-target clustering identification method and device of feeling.This application uses unified AFDM waveform as bottom transmission mechanism, realizes full diversity gain in time-frequency plane by adjusting affine parameter, effectively suppresses the inter-subcarrier interference under high dynamic scene.In the receiving end, the distance-Doppler heat map is reconstructed by fast cyclic correlation radar algorithm, and the original detection point cloud is extracted combined with 2D-CFAR detection.Introduce the DBSCAN clustering algorithm based on dimensionless weight, and the scattered point is condensed into an independent entity target in RDM physical space, and the radial velocity characteristics of clustering centroid are used to construct automatic decision engine, to realize the real-time classification and identification of static obstacles and dynamic moving targets.The application solves the technical pain points of traditional ISAC system in high frequency, high mobility scene, such as sensing point fragmentation and confusion of static and dynamic targets.

A method of detecting multiple radar targets based on joint radar and communication using OTFS signals is provided. The method includes: generating a range-Doppler map (RDM) based on a first delay-Doppler (DD) domain signal and a second DD domain signal, whereby a first time domain signal is generated from an OTFS modulation based on the first DD domain signal and transmitted to multiple radar targets, and the second DD domain signal is generated from an OTFS demodulation based on a second time domain signal received and reflected from the multiple radar targets from the first time domain signal transmitted; and detecting a dominant radar target amongst the multiple radar targets based on the RDM. In particular, the method further includes, for each iteration of a plurality of iterations: modifying a correlation intermediate term associated with the RDM associated with the iteration based on the detected dominant radar target to obtain a modified correlation intermediate term, the correlation intermediate term relating to a cyclic correlation between the first and second DD domain signals; modifying the RDM associated with the iteration based on the modified correlation intermediate term; and detecting a dominant radar target (i.e., a next dominant radar target) amongst the multiple radar targets based on the RDM modified based on the modified correlation intermediate term. There is also provided a corresponding system for detecting multiple radar targets and a corresponding joint radar and communication system for detecting multiple targets.

The application relates to the technical field of MIMO radars, and discloses a radar waveform combining TDM and DDM, a signal processing method and device and a medium, which comprise a modulated signal transmitting module, a return signal receiving module and a return signal processing module, the application combines the advantages of TDM and DDM, solves the problem of speed ambiguity caused by TDM through slow-time random sampling, the waveform can be well applied to large-scale arrays, avoids speed ambiguity, improves the utilization rate of a Doppler band, a relatively wide Doppler sub-band improves the accuracy of target detection, meanwhile, the application provides a corresponding signal processing procedure for the waveform, and provides a method for separating channels through cyclic correlation using a template, the corresponding target transmitting channels can be quickly and accurately separated, the demand for angle measurement can be met, and the method provided by the application can be compatible with a traditional FMCW radar, and has strong application flexibility.

This application discloses an enhanced high-precision search and capture method for Rowland signals based on convolutional neural networks, comprising: acquiring a target signal sequence within the GRI range of the group repetition period; simulating and generating training data and training a convolutional neural network to obtain a signal search and capture model; using the model to determine whether the signal exists, and if it exists, generating a local pulse group sequence; calculating the maximum value and sampling position of the correlation value sequence through cyclic correlation to determine the signal starting point and complete the search and capture. This method achieves high-precision signal capture under low signal-to-noise ratio by combining neural networks and cyclic correlation.

The invention relates to a Doppler and channel joint estimation method of an OFDM (Orthogonal Frequency Division Multiplexing) system under an impulse noise background, which comprises the following steps of: constructing an OFDM communication system model in which a channel has Doppler frequency shift and a sending signal is an OFDM modulation signal, and then respectively calculating a cyclic autocorrelation function of the sending signal and a cyclic autocorrelation function of a receiving signal in the OFDM communication system model; the method comprises the following steps of: constructing a cyclic correlation matrix equation by using the difference of an OFDM signal, AWGN noise and impulse noise in cyclic correlation function energy distribution, and finally obtaining a Doppler frequency shift estimation value and a channel parameter by solving the matrix equation, thereby realizing Doppler frequency shift and channel estimation of an OFDM system under the impulse noise background. And better robust performance is shown in an impulse noise environment.

This invention belongs to the field of large-scale model training and push cluster communication optimization technology, specifically disclosing a network traffic timing orchestration method and system for large-scale model parallel training tasks. The system comprises a topology and traffic acquisition module, a square wave construction module, a conflict calculation module, a scheduling decision module, and an execution module. The method uses sampled cluster communication link occupancy and a link task square wave constructed using the periodicity and temporal dependence characteristics of large-scale model training as input. It utilizes the cyclic correlation between cluster communication link occupancy and the link task square wave to characterize the link conflict state under different timing offsets. By introducing a signal change rate index, it selects the optimal timing offset to orchestrate the training task, thereby improving the utilization rate of link resources. This system can be integrated as a pluggable module into the cluster physical resource scheduler to further reduce cross-node communication conflicts.

The application discloses a method, system, device and medium for identifying MIMO-OFDM space-frequency coding mode under alpha stable distribution noise, which comprises the following steps: firstly, performing space-time-frequency coding inter-class identification based on fractional low-order correlation on a received signal; then, extracting a cyclic correlation entropy spectrum feature map of the received signal; and finally, inputting the extracted feature map into a trained deep forest network to identify the space-frequency coding mode; the system and device are based on space-time-frequency coding inter-class identification based on fractional low-order time lag correlation, inter-class identification of unknown signals is realized, and whether space-frequency coding is used is confirmed; the application extracts a cyclic correlation entropy spectrum feature map of a received signal, combines a deep forest network, and converts a coding identification problem into an image identification problem; the application can effectively identify MIMO-OFDM space-frequency coding mode under alpha stable distribution noise, solves the problem that a traditional method is limited in function in non-cooperative communication, and is also applicable to a Gaussian noise environment.