Robust and scalable sparse array frequency and doa estimation method and device

Abstract

The invention discloses a robust and scalable sparse array frequency and DOA estimation method and device. The method includes: obtaining a corrected frequency, amplitude, and phase parameter group according to a relaxed mutual prime sparse array and a Tsui spectrum corrector; Construct the first remainder array according to the corrected frequency, and combine the frequency reconstruction for the robust remainder system, and obtain the frequency estimation value and wavelength through the average operation of the frequency; construct according to the corrected phase information and amplitude information Mean vector, according to the relationship between the phase and the mean vector, the phase estimation of the signal on the array element is obtained, and two phase difference values ​​are obtained by making a difference; the second remainder array is constructed according to the two phase difference values, and combined with a robust remainder system Perform DOA reconstruction to obtain intermediate parameters, and combine with the estimation formula to calculate the estimated value of DOA. The device includes: a relaxed mutual prime sparse array, an ADC sampler, a DSP, and a display device. The present invention utilizes the relaxed co-prime sparse array to complete frequency and DOA estimation.

Description

technical field [0001] The invention relates to the technical field of array signal analysis and processing, in particular to a robust and scalable sparse array frequency and DOA estimation method and device, which are applied to anti-noise robustness. Background technique [0002] The joint estimation of the frequency of the incident signal and the direction of arrival (DOA) is the radar [1] , wireless communication system [2] , electronic warfare and other critical issues in the field of passive target perception, however, the difficulty of the estimation gradually increases with the increase of the frequency of the incident signal. For example, the current radar operating frequency band is gradually transitioning from S-band, C-band to X-band and K-band, and its signal wavelength is gradually shortened from meter-level, decimeter-level, and centimeter-level accordingly. This leads to two outstanding problems: First, the Nyquist sampling theorem in the space domain requi...

AI Technical Summary

Problems solved by technology

This leads to two outstanding problems: First, the Nyquist sampling theorem in the space domain requires that the distance between antenna elements should not be less than half the signal wavelength, so for short wavelengths, the array elements have to be densely arranged, which will inevitably lead to Very serious inter-element coupling effect; second, the time-domain Nyquist sampling theorem requires that the sampling rate is not lower than twice the highest frequency of the signal, so as the working frequency band increases, the existing analog-to-digital converter ( analog to digital converter, ADC) cannot meet the requirements in terms of sampling rate and power consumption

The scheme mainly considers two basic issues: one is the arrangement and configuration of sparse array elements, and the other is the design of frequency and DOA parameter reconstruction algorithms

In terms of improving the first basic problem (that is, the arrangement and configuration of sparse array elements), it can be achieved by increasing the number of array elements and the number of snapshots collected by each array element. However, this means increasing the burden on the system cost, and often Engineering applications are not allowed

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