Anti-noise Robustness Scalable Joint Estimation Of Frequency And Direction Of Arrival For Sparse Arrays

The frequency and direction of arrival of the incident signal are two physical parame-ters that are critical in the field of passive target sensing such as radar,wireless communi-cation systems,and electronic warfare.And the estimated difficulty of these two physical quantities will gradually increase as the incident frequency increases.The Nyquist sam-pling theorem in the spatial domain requires that the spacing of the antenna elements is not more than half of the wavelength of the incident signal.When the frequency of the inci-dent signal is high,the array must be densely arranged,which will cause a serious coupling effect between the elements.The time domain Nyquist sampling theorem requires that the sampling rate be no less than twice the highest frequency of the incident signal,which im-poses a significant burden on the analog-to-digital converter.Therefor,this paper proposes a joint frequency and direction of arrival estimation scheme for space-time undersampling conditions.It is of both high theoretical significance and great engineering value to improve the antinoise robustness of the joint estimator of frequency and direction of arrival,when the incident signal is undersampled in both the temporal domain and the spatial domain.To solve this problem,this paper starts from the breakthrough of two basic problems of this joint estimator.On one hand,in the aspect of sparse array arrangement and configuration,this paper constructs a relaxed coprime sparse array with only 3 sensors,whose element spacings are configured according to the requirements of towards robustness in residue number system.This saves hardware consumption and ensures the high sparsity of the array.Therefore,the coupling effect between array elements caused by the dense distribution of array elements is suppressed,so that it is not restricted by the Nyquist theorem of spatial domain.On the other hand,in the proposed design of the recovery algorithm of two parameters(frequency and direction of arrival),we utilize the towards robustness in residue number system algorithm,in which the mechanism of scalable adjustment of the anti-noise robust-ness is deeply analyzed.In the frequency reconstruction,the parallel undersampling method is adopted,which greatly improves the frequency range that can be estimated by the joint estimation scheme and is not limited by the time domain Nyquist theorem.In addition,when using towards robustness in residue number system algorithm for parameter recon-struction,the accurate acquisition of the remainder is very important.Therefore,this paper uses the spectrum correction technique to ensure the accuracy of the remainder acquisitionThe numerical results show that,the above two aspects of improvements essentially enhances the joint estimator' s anti-noise robustness.Particularly,compared to the oth-er joint estimators,the proposed estimator at least achieves 9dB improvement in the SNR thresholds of both frequency estimation and direction of arrival estimation,without increas-ing the hardware complexity and system cost.Therefore,the proposed antinoise robustness scalable joint estimator possesses a vast potentials in the radar,remote sensing and other passive sensing related applied fields.