45results about How to "Small root mean square error" patented technology

The present invention proposes a spectral reconstruction method based on micro-nano structure filter array modulation and sparse matrix transformation. The implementation steps are: constructing a micro-nano structure filter array; Measurement; sampling the transmission spectrum of each micro-nano structure filter at discrete points; measuring the transmitted light intensity through each micro-nano structure filter; constructing a matrix equation; performing discrete cosine transform on the matrix equation; discrete The cosine transformation matrix equation performs a sparse transformation; obtains the spectral reconstruction result. The present invention solves the problem that the noise error is not processed in the prior art, and the acquired reconstructed spectrum will still be affected by the noise error, and at the same time effectively reduces the root mean square error of the reconstructed spectrum. Simulation results show that the present invention effectively improves the accuracy of spectral reconstruction.

The invention discloses a wideband signal DOA estimation method based on a coprime array, comprising: S1, designing a coprime array structure by using an antenna; S2, sampling and discrete Fourier transforming the wideband signal received by the antenna in the coprime array Transform to obtain the frequency domain signal output model; S3, calculate the autocorrelation matrix of the frequency domain signal output model and vectorize it to obtain a new signal model; S4, process the new signal model to obtain the spatial smoothing coherence of the wideband signal Variance matrix; S5, dividing the airspace network, constructing a dictionary, and using the dictionary at multiple frequency points of the broadband signal to perform sparse representation of the spatial smooth covariance matrix, forming a multi-measurement vector sparse representation model of multiple dictionaries of the broadband signal ; S6, realize the DOA estimation of broadband signals in the form of solving the sparse inverse problem through the joint sparse constraints on the multi-dictionary sparse representation coefficients. The invention can improve the estimation accuracy of the direction angle of the broadband signal under the low signal-to-noise ratio, and reduce the direction-finding error.

The invention discloses a data-driven weighted orbit determination method for spacecraft, which includes: an orbit determination sample set Z={X,Y} composed of a measurement data set X and a corresponding target spacecraft orbit set Y, and the measurement Data weighting, constructing a weighted sample set for orbit determination; calculating the Gram matrix of the weighted sample set for orbit determination, using the elastic network as a loss function, and calculating the optimal estimated value of the spacecraft orbit determination result y(t) based on the Gram matrix. The present invention does not need to construct The complex dynamic model introduces the idea of ​​machine learning, and by learning a large number of labeled nominal orbits, the orbit of unknown spacecraft can be estimated; in addition, the training data and test data have the same noise characteristics , taking this sample data as training data can reduce the sensitivity of orbit determination results to measurement noise, and has broad application prospects.

The invention discloses a compressive sensing high-resolution array processing method based on subspaces, and a convex optimization calculation function is constructed by employing the sparsity of measured signals in a sparse matrix. According to the method, the signals can be recognized in a coherent condition of the signals and noise; and in different signal-to-noise ratio conditions, the root-mean-square error of the method is smaller than that of other contrast algorithms, and the calculating time of the method and the calculating time of the conventional methods are almost the same.

The invention discloses a nonlinear system state estimation method based on Kalman filtering positioning, and proposes a strongly adaptive Kalman filtering mechanism which combines a nonlinear filtering algorithm and Kalman filtering. The method comprises the steps: carrying out the simultaneous estimation of node positions and channel parameters through employing an RSSI state estimation algorithm based on square root volume Kalman filtering, and obtaining the estimation value of a state vector; employing the Kalman filtering for further processing according to the linear change of a state equation, obtaining optimal estimation, and building a strongly adaptive square root volume Kalman filtering algorithm; giving the design steps of the strongly adaptive square root volume Kalman filtering algorithm; and calculating a theoretical square root error lower bound under a state space model based on the RSSI state estimation. The method enables an estimation result to be improved, and improves the precision. The method does not need to excessively depend on improper initial conditions, can be well adapted to a highly nonlinear system, and is not liable to enable the algorithm to be divergent and ineffective.