| With the complexity of the underwater environment and the quietness of underwater submarines,the existing underwater target detection methods are forced to develop in low frequency bands and anti-interference.The vector sensor is a compound sensor that can simultaneously pick up the sound pressure and vibration velocity in the sound field simultaneously and has low-frequency directivity.Its appearance provides a more powerful tool for underwater target detection.Existing subspace highr-resolution algorithms have achieved many excellent results after decades of development,but such algorithms have disadvantages of poor robustness in engineering implementation,especially for low signal-to-noise ratio(SNR)and small snapshots.Subspace algorithms need to use the number of information sources as a priori knowledge to divide the subspace.With the quiet development of existing underwater operations,it will undoubtedly increase the difficulty of obtaining the number of targets.When the number of information sources is obtained incorrectly,it will leads to subspace division errors,which in turn leads to a sharp deterioration in the robustness of subspace algorithms.In order to solve this drawback,this paper proposes a class of robust highr-resolution algorithms based on vector sensors that do not require the number of sources.Starting from the theory of eigenvalue arrangement of a specific matrix,this thesis constructs a specific data model that includes scanning sources,and analyzes the influence of scanning sources on the eigenvalue arrangement rules when different parameters are used to obtain the exact arrangement rules of specific matrices under different parameters.Furthermore,the highrresolution DOA estimation with unknown source number is realized.However,such highrresolution algorithms with unknown number of sources need to perform eigenvalue decomposition at each scanning angle,and the fast-growing algorithm calculations directly affect the real-time performance of the detection system.For this reason,the paper analyzes the covariance matrix and proposes a kind of low-complexity DOA estimation algorithm based on the principle of real-virtual separation,which effectively reduces the spectrum search range and reduces the calculation amount of the algorithm.The specific research is divided into the following parts:(1)DOA estimation based on noise power invariant(NPI)Based on the research of signal power and signal eigenvalues,an efficient highr-resolution DOA estimation algorithm without the number of sources is constructed through proper selection of parameters.This algorithm uses the scan source and the reference matrix to construct the scan matrix,and takes the smallest eigenvalue and the smallest eigenvalue of the reference matrix to construct the spatial pseudo-spectrum,so as to achieve the goal of not requiring the number of information sources.Follow-up experiments show that the algorithm is affected by the adjustment parameters and the power exponent.When the appropriate adjustment parameters and the power exponent are selected,the noise power estimation performance is better than other high-resolution algorithms.(2)Capon-like(Exponent Capon-like,E-Capon-like)algorithm based on power exponentHowever,the selection range of the adjustment parameters of the NPI algorithm is too small.In order to solve this problem,the paper proposes an E-Capon-like algorithm.First,the power of the sampling covariance matrix is used to weaken the influence of the signal components in the matrix on the eigenvalues,making the matrix as a whole approach the noise subspace,thereby obtaining high-resolution properties;secondly,combining this matrix with the scanning source to analyze the differences.The arrangement of eigenvalues under the scanning angle constructs a simple spatial pseudo-spectrum and achieves a high-resolution effect.Subsequent simulation experiments show that this algorithm can not only obtain target orientation information when the number of sources is unknown,avoiding the acquisition of the target number,but also compared with other high-resolution algorithms,the proposed algorithm has better estimation performance and is suitable for more severe underwater environment.(3)DOA estimation algorithm based on generalized Capon(G-Capon)The above-mentioned NPI algorithm and E-Capon-like algorithm are both proposed for white noise,ignoring the impact on color noise.For this reason,the paper proposes a highresolution G-Capon algorithm with unknown source number.Firstly,the research is carried out based on the generalized Capon model under white noise and color noise,and the eigenvalue arrangement rule under the specific matrix is analyzed,so as to realize the robust highresolution estimation of the target.In order to optimize the performance of the algorithm,the article analyzes the reasons for the formation of false peaks,the detection threshold,and the influencing factors among the adjustment parameters.The theory shows that when the adjustment parameters are selected too large,the algorithm detection threshold will be reduced,but at the same time it will also lead to false peaks.Peak formation;On the contrary,when the adjustment parameter is selected too small,although it will not lead to the formation of false peaks,the detection threshold of the algorithm will increase at this time.To this end,the article presents a method for selecting adjustment parameters.This method not only suppresses the formation of false peaks,but also reduces the minimum detection threshold of the algorithm.Through subsequent comparison with other high-resolution algorithms,the method of selecting parameters is explained.The correctness of and the superiority of the proposed algorithm.(4)Low-complexity DOA estimation algorithmAlthough the above algorithm realizes the target DOA estimation without the number of sources,it still needs to search the entire spatial spectrum,which undoubtedly brings heavy search calculations.For this reason,the article uses the idea of symmetric virtual source to propose a kind of half-spectrum search low-complexity DOA estimation method.Through the internal analysis of the sampling covariance matrix,it is shown that when the steering vector satisfies the odd function property,the real part of the sampling covariance matrix contains both the target angle and the target symmetric angle.Using this idea,the article proposes SR-Caponlike and SR-NPI algorithms,which can obtain a complete target angle through half-spectrum search and judgment,which greatly reduces the computational complexity of the algorithm.(5)Field experiment verificationThe sea trial data collected by the acoustic vector sensors is used to verify the proposed algorithm.The verification experiments are carried out from the number of array elements and signal frequency.The target DOA estimation,eigenvalue arrangement and target tracking experiments are carried out to show the proposed algorithm’s performance.The correctness of the theory and the applicability of field experiments.Experimental results in the field show that robust high-resolution algorithms can not only accurately obtain the azimuth of underwater targets when the number of targets is unknown,but also that the eigenvalue arrangement of such algorithms basically meets the theoretical derivation,indicating that this type of algorithm can be applied to the field Engineering;the low-complexity DOA estimation algorithm can not only form a spectrum peak at the target position,but also form a spectrum peak at its symmetrical position.This achieves the purpose of half-spectrum search,effectively reducing the calculation of the algorithm’s spectrum search,and it is the data Real-time processing offers possibilities. |
