CN-121984643-A - Ultra-wideband pulse waveform space-time synchronous compression method, system, medium and product

CN121984643ACN 121984643 ACN121984643 ACN 121984643ACN-121984643-A

Abstract

The invention relates to the technical field of signal compression, in particular to an ultra-wideband pulse waveform space-time synchronous compression method, an ultra-wideband pulse waveform space-time synchronous compression system, a medium and a product. The method comprises the steps of executing multidimensional orthogonal projection on an original ultra-wideband pulse signal by using a space-time joint perception dictionary to extract space-time characteristic coefficients, adjusting an observation matrix according to channel state information, performing compression sampling on the space-time characteristic coefficients by using the adjusted observation matrix to generate compressed observation vectors, executing manifold learning dimension-reduction mapping on the compressed observation vectors to construct a space-time manifold topological structure, acquiring manifold characteristic coordinates based on the space-time manifold topological structure, introducing weighted least square constraint to perform nonlinear reconstruction on the manifold characteristic coordinates to generate a sparse pulse sequence, executing variable decibel leaf-s iterative reasoning on the sparse pulse sequence to acquire optimal quantization parameters, and performing entropy coding on the sparse pulse sequence by using the optimal quantization parameters to generate a transmission code stream. The invention can ensure the integrity and no distortion of the details of the pulse signals.

Inventors

FENG KUISHENG
LI NA
LI AIMIN
HUANG QIAOYUAN

Assignees

阳光学院

Dates

Publication Date: 20260505
Application Date: 20260403

Claims (10)

1. The ultra-wideband pulse waveform space-time synchronous compression method is characterized by comprising the following steps of: constructing a space-time joint perception dictionary, and executing multidimensional orthogonal projection on an original ultra-wideband pulse signal by using the space-time joint perception dictionary so as to extract space-time characteristic coefficients; According to the channel state information, adjusting the row vector distribution of the observation matrix, and performing compressed sampling on the space-time characteristic coefficients by using the adjusted observation matrix to generate a compressed observation vector; performing manifold learning dimension reduction mapping on the compressed observation vector to construct a space-time manifold topology structure, and acquiring manifold feature coordinates based on the space-time manifold topology structure; introducing weighted least square constraint to carry out nonlinear reconstruction on the manifold feature coordinates to generate a sparse pulse sequence; And performing variable decibel leaf iterative reasoning on the sparse pulse sequence to obtain an optimal quantization parameter, and performing entropy coding on the sparse pulse sequence by utilizing the optimal quantization parameter to generate a transmission code stream.
2. The method of claim 1, wherein constructing a spatio-temporal joint perceptual dictionary, performing multi-dimensional orthogonal projection on an original ultra-wideband pulse signal using the spatio-temporal joint perceptual dictionary to extract spatio-temporal feature coefficients, comprises: collecting original ultra-wideband pulse signals and extracting covariance matrixes of time dimension and space dimension of the original ultra-wideband pulse signals; constructing a K-L transformation base according to the diagonalized eigenvector of the covariance matrix; Carrying out tensor product operation on the Gabor atom library and the K-L transformation base to generate the space-time joint perception dictionary; sparse decomposition is carried out on the original ultra-wideband pulse signals by utilizing the space-time joint perception dictionary so as to calculate the matching coefficient of each atom in a Gabor atom library; And selecting atomic combinations with matching coefficients larger than a preset threshold value to generate the space-time characteristic coefficients.
3. The method of claim 1, wherein adjusting the row vector distribution of the observation matrix according to the channel state information, and performing compressive sampling on the space-time characteristic coefficients by using the adjusted observation matrix, to generate a compressive observation vector, comprises: receiving feedback information of a channel, wherein the feedback information comprises multipath delay spread and Doppler frequency shift parameters; calculating the time domain sparsity of the channel based on the multipath time delay spread, and calculating the frequency domain sparsity of the channel based on the Doppler frequency shift parameter; constructing a cost function taking the channel state information as a parameter, and iteratively updating the row vector weight of the observation matrix by a gradient descent method; And carrying out weighted projection on the space-time characteristic coefficient by using the updated observation matrix to generate the compressed observation vector.
4. The method of ultra-wideband pulse waveform space-time synchronous compression of claim 1, wherein said performing manifold learning dimension-reduction mapping on said compressed observation vector to construct a space-time manifold topology and obtaining manifold feature coordinates based on said space-time manifold topology comprises: Constructing a neighborhood graph of the compressed observation vector, and calculating geodesic distances among data points in the neighborhood graph; Performing feature decomposition on the neighborhood graph by using a Laplace feature mapping algorithm to obtain a low-dimensional embedded vector; Mapping the low-dimensional embedded vector to a Riemann manifold space, and calculating a main curvature direction of the Riemann manifold space; rearranging data points in the neighborhood graph along the principal curvature direction to generate the space-time manifold topological structure; and extracting coordinate values of stable nodes in the space-time manifold topological structure to serve as the manifold characteristic coordinates.
5. The method of claim 1, wherein said introducing a weighted least squares constraint non-linearly reconstructs said manifold feature coordinates to generate a sparse pulse sequence, comprising: establishing a target optimization function comprising a signal sparsity constraint and a data fidelity constraint; introducing a weighted least square term into the target optimization function, and adjusting a weight coefficient of the weighted least square term according to the energy density of the local area of the original ultra-wideband pulse signal; Carrying out iterative solution on the target optimization function by adopting a conjugate gradient method to obtain an optimal sparse solution; And restoring to obtain a pulse amplitude parameter and a time delay parameter corresponding to the original ultra-wideband pulse signal according to the optimal sparse solution, and generating the sparse pulse sequence.
6. The method of time-space synchronous compression of ultra-wideband pulse waveforms according to claim 1, wherein said performing a variational bayesian iterative reasoning on said sparse pulse sequence to obtain an optimal quantization parameter, and entropy encoding said sparse pulse sequence with said optimal quantization parameter, generating a transmission code stream, comprises: initializing a priori distribution model of quantization parameters, wherein the priori distribution model comprises quantization step length and probability density functions; Taking the sparse pulse sequence as observation data, and calculating posterior probability distribution of quantization parameters by using a variable decibel leaf method; Iteratively updating the quantization parameter until convergence according to the expected value maximization principle of the posterior probability distribution so as to obtain the optimal quantization parameter; And performing scalar quantization and arithmetic coding on the sparse pulse sequence by utilizing the optimal quantization parameter to generate the transmission code stream.
7. The ultra-wideband pulse waveform spatiotemporal synchronous compression method of claim 1, wherein said performing manifold learning dimension-reduction mapping on said compressed observation vector further comprises the steps of: calculating k nearest neighbors of each sample point, and constructing a local reconstruction weight matrix; Solving the local reconstruction weight matrix by minimizing reconstruction errors; Embedding the high-dimensional data into the low-dimensional space by utilizing the local reconstruction weight matrix, and introducing regularization items in the embedding process to control the sparse distribution density of the sample points so as to generate the space-time manifold topological structure.
8. An ultra-wideband pulse waveform spatiotemporal synchronous compression system for implementing the method of ultra-wideband pulse waveform spatiotemporal synchronous compression of any of claims 1-7, comprising: the space-time feature extraction module is used for constructing a space-time joint perception dictionary, and performing multidimensional orthogonal projection on the original ultra-wideband pulse signal by using the space-time joint perception dictionary so as to extract space-time feature coefficients; the self-adaptive compressed sampling module is used for adjusting the row vector distribution of the observation matrix according to the channel state information, and performing compressed sampling on the space-time characteristic coefficients by using the adjusted observation matrix to generate a compressed observation vector; The manifold topology processing module is used for executing manifold learning dimension reduction mapping on the compressed observation vector to construct a space-time manifold topology structure and acquiring manifold feature coordinates based on the space-time manifold topology structure; The nonlinear reconstruction module is used for introducing weighted least square constraint to carry out nonlinear reconstruction on the manifold feature coordinates so as to generate a sparse pulse sequence; and the Bayesian inference coding module is used for executing the variable decibel leaf iterative inference on the sparse pulse sequence to acquire the optimal quantization parameter, and performing entropy coding on the sparse pulse sequence by utilizing the optimal quantization parameter to generate a transmission code stream.
9. A computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the ultra wideband pulse waveform spatiotemporal synchronous compression method of any of claims 1 to 7.
10. An electronic product comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the ultra wideband pulse waveform spatiotemporal synchronous compression method of any one of claims 1 to 7 when executing the computer program.

Description

Ultra-wideband pulse waveform space-time synchronous compression method, system, medium and product Technical Field The invention relates to the technical field of signal compression, in particular to an ultra-wideband pulse waveform space-time synchronous compression method, an ultra-wideband pulse waveform space-time synchronous compression system, a medium and a product. Background With the rapid evolution of the fifth generation mobile communication technology and the next generation wireless communication system, the ultra-wideband pulse radio technology has wide prospect in the fields of high-speed sensor networks, accurate ground penetrating imaging, panoramic industrial monitoring and the like by virtue of extremely high transmission rate, centimeter-level positioning precision and excellent anti-interference performance. However, ultra-wideband pulse signals have extremely wide spectrum bandwidths and time-domain narrow pulse characteristics in nanosecond or even picosecond levels, and direct digital acquisition of the ultra-wideband pulse signals requires a sampling rate in the order of gigahertz according to the nyquist sampling theorem, which puts extremely severe demands on the hardware bandwidth, sampling accuracy of an analog-to-digital converter and throughput capability of a subsequent data processing unit. In order to break through the sampling bottleneck, a compressed sensing theory is widely adopted in the prior art, and data acquisition and compression are realized at a rate far lower than the Nyquist frequency by utilizing the sparse characteristic of signals. Conventional compressed sensing methods typically employ a pre-set fixed observation matrix (e.g., a random gaussian matrix or bernoulli matrix) to process the projected low-dimensional observation vector and focus on algorithmically recovering the original ultra-wideband pulse signal during the reconstruction phase. However, in a practical complex electromagnetic transmission environment, the ultra-wideband pulse signal often presents a complex statistical characteristic of non-stationary, non-gaussian and time-varying space-frequency correlation, and the inherent structural information is not only contained in a sparse domain, but also hidden in the geometric topology of a high-dimensional data manifold. In the prior art, the observed data is generally regarded as a discrete point set in Euclidean space, and linear dimension reduction and processing modes are adopted, so that nonlinear manifold structure distribution characteristics of signals under channel fading such as multipath effect, doppler frequency shift and the like are ignored. When the channel state of the processing mode is dynamically changed, the optimal matching of the observation matrix and the sparse characteristic of the channel is difficult to realize, so that the observation data contains a large amount of redundant environmental noise and lacks enough signal essential characteristics, and the detail distortion of weak pulse waveforms is easily caused by noise amplification in the linear reconstruction process. Accordingly, there is a need to improve one or more problems in the related art as described above. It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the application and thus may include information that does not form the prior art that is already known to those of ordinary skill in the art. Disclosure of Invention The present disclosure provides a method and related apparatus for compressing ultra-wideband pulse waveforms in a time-space synchronous manner, which aims to overcome at least one of the drawbacks of the prior art. In order to achieve the above purpose, the technical scheme disclosed by the invention is as follows: According to a first aspect of an embodiment of the present disclosure, there is provided an ultra-wideband pulse waveform space-time synchronous compression method, including the steps of: constructing a space-time joint perception dictionary, and executing multidimensional orthogonal projection on an original ultra-wideband pulse signal by using the space-time joint perception dictionary so as to extract space-time characteristic coefficients; According to the channel state information, adjusting the row vector distribution of the observation matrix, and performing compressed sampling on the space-time characteristic coefficients by using the adjusted observation matrix to generate a compressed observation vector; performing manifold learning dimension reduction mapping on the compressed observation vector to construct a space-time manifold topology structure, and acquiring manifold feature coordinates based on the space-time manifold topology structure; introducing weighted least square constraint to carry out nonlinear reconstruction on the manifold feature coordinates to generate a sparse pulse sequence; And performing variable decibe