Signal model order estimation method based on signal correlation matrix

Abstract

The invention provides a signal model order estimation method based on a signal correlation matrix, which can realize correct and effective estimation of a signal model order. The method comprises thefollowing steps that 1, a discrete time signal sequence composed of N signals is collected, the discrete time signal sequence is expressed as u(1), u(2),..., u(N) according to the time sequence, in the formula, u(i) represents the signal collected at the i*ts moment, and ts is the sampling period of the signal; 2, a matrix A is constructed based on the N signals obtained in the step 1, the matrixA is an M*(N+M-1) dimensional matrix, and M is smaller than N; 3, the signal correlation matrix R and the characteristic value of the R are acquired based on the matrix A; 4, a parameter r(p) of thesignal correlation matrix R is acquired based on the characteristic value of the signal correlation matrix R; and 5, the minimum value of the parameter r(p) obtained in the step 4 is taken, wherein the label corresponding to the minimum value is the order of the signal model.

Description

technical field [0001] The invention relates to the technical field of signal spectrum analysis, in particular to a method for estimating the order of a signal model based on a signal correlation matrix. Background technique [0002] When performing signal spectrum analysis, the model order of the signal is often involved. For example, the order of the ARMA model in the rational spectrum estimation, the number of sources in the DOA estimation, and the filter order in the system identification are all signal problems. model order problem. The model order of the signal is an important parameter of the signal spectrum analysis technology, and sometimes it can even directly affect the success or failure of the algorithm, because the signal model order is related to the correctness of the signal modeling. If it is not estimated properly, it will lead to over-modeling or under-modeling of the signal. A modeling situation arises that introduces unnecessary signal noise, which in t...

AI Technical Summary

Problems solved by technology

The model order of the signal is an important parameter of the signal spectrum analysis technology, and sometimes it can even directly affect the success or failure of the algorithm, because the signal model order is related to the correctness of the signal modeling. If it is not estimated properly, it will lead to over-modeling or under-modeling of the signal. A modeling situation arises that introduces unnecessary signal noise, which is likely to lead to unpredictable and significant biases in the estimated results

[0003] At present, the commonly used signal model order estimation methods mainly include information theory criterion (ALC) and minimum description length criterion (MDL). These methods are generally derived from information theory and are widely used in engineering. The problem: First, these methods are based on the premise that the probability density function of the signal is known, but the actual situation is that we often cannot correctly predict the probability distribution of the signal, which inevitably leads to estimation errors; secondly, and most importantly, , the scope of application of these methods is generally the case where the data vectors are independent and identically distributed, but there are also a large number of cases where there is a strong correlation between the data vectors in engineering. In this case, additive colored noise is inevitable. Therefore, There is a certain risk of failure in signal order estimation methods with independent and identical distribution requirements

Images