Near-field and far-field source mixed direction finding method based on quantum mouse group

Abstract

The invention discloses a near-field and far-field source mixed direction finding method based on a quantum mouse group, and the method comprises the steps: constructing a separation operator on the basis of obtaining a far-field source angle, obtaining a far-field source fourth-order cumulant matrix through the operator, obtaining a pure near-field source fourth-order cumulant matrix through the difference of the fourth-order cumulant matrix, and carrying out a related process of parameter search through a quantum mouse group mechanism, so that the technical problems of angle ambiguity and low efficiency of a far and near field signal source separation method in the existing mixed source direction finding method are solved. According to the method, a relatively accurate mixed source direction finding result can be rapidly obtained, quantization errors do not exist, the array aperture can be expanded through the fourth-order cumulant matrix, the direction finding precision is improved, compared with a traditional near-field and far-field source mixed direction finding method, the speed is higher, the precision is higher, and the application limitation of an existing method is broken through.

Description

technical field [0001] The invention relates to a mixed direction finding method of near-field and far-field sources in a Gaussian noise environment, in particular to a mixed direction-finding method of near-field and far-field sources based on quantum mouse groups, belonging to the field of array signal processing. Background technique [0002] Passive source location uses the steering vector matrix of the sensor array and the observed source vector, and uses array signal processing related algorithms to estimate the target parameters of the source, such as: arrival azimuth, elevation angle, distance and other parameters. Passive source location belongs to the field of array signal processing, and has a wide range of applications in many military or life fields in radar, information warfare, seismology, and microphone arrays, so it has attracted widespread attention. [0003] The classic direction finding method is aimed at near-field source or far-field source signal. Howe...

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Problems solved by technology

[0003] The classic direction finding method is aimed at near-field source or far-field source signal. However, in practical engineering problems, far-field source signal source and near-field source signal source may exist at the same time. This situation is called mixed source, mixed source In direction finding, it is necessary to estimate the angle and distance of the near-field source. For the far-field source, it is simplified to angle parameter estimation. Therefore, the traditional far-field source or near-field source positioning method cannot be directly used in mixed source direction finding. Therefore, the method of direction finding for mixed sources needs to be further studied

[0004] When the traditional noise subspace algorithm is used for mixed source location, the process of spectral peak search is used. If a larger spectral peak search interval is selected, there will be quantization errors. On the contrary, if a smaller spectral peak search interval is selected, the search results will It takes a long time, and there is a contradiction between the estimation accuracy and the search time. Using an intelligent optimization algorithm to replace the peak search process is a potential solution, but the existing intelligent optimization algorithm is applied to the mixing of near-field and far-field sources. The complex engineering problem of direction finding has many defects, such as slow convergence speed, easy to fall into local convergence, etc. Therefore, it is necessary to design a new and efficient solution method for specific engineering problems

[0005] Through searching the existing technical documents, it is found that "Passive Localization of Mixed Near-Field and Far-Field Sources Using Two-stage MUSIC Algorithm [ J]" constructs two fourth-order cumulant matrices, the first fourth-order cumulant matrix only includes the angle information of the mixing source, and uses the MUSIC algorithm to obtain the angle parameters, the second fourth-order cumulant matrix includes the angle and For distance information, substituting the estimated angle information of all far-field signal sources and near-field signal sources into the two-dimensional MUSIC peak search function can reduce the search function to one-dimensional and estimate the distance parameters of near-field sources. When the angles of the field source and the near-field source are similar, the performance drops seriously or even fails, and the angle is ambiguous, and the calculation amount of the algorithm is large due to the construction of two fourth-order cumulant matrices, and there are quantization errors in the direction finding results; He J et al. In "Signal Processing" (2012,60(4):2066-2070.), "Efficient application of MUSICalgorithm under the coexistence of far-field and near-field sources[J]" published on "Signal Processing" (2012,60(4):2066-2070.), proposed the use of oblique projection technology Realize the separation of far and near field signal sources, and avoid the estimation error problem caused by angle ambiguity while reducing the amount of calculation, but this method only uses the anti-diagonal line of the covariance matrix of the array received data when estimating the parameters of the near field source element, which leads to suboptimal azimuth and distance estimation performance for near-field sources

[0006] The search results of the existing literature show that the existing hybrid direction finding method of near-field and far-field sources has the disadvantages of high computational complexity and the inability to achieve efficient separation of near-field and far-field sources. Therefore, a new hybrid direction finding method is designed. The source separation method further proposes a new hybrid direction finding method for near-field and far-field sources. Specifically, a separation operator is constructed on the basis of obtaining the angle of the far-field source. Through this operator, the fourth-order accumulation of the far-field source can be obtained. The pure fourth-order cumulant matrix of the near-field source is obtained through the difference of the fourth-order cumulant matrix, and the relevant process of parameter search is carried out through the quantum mouse swarm mechanism, which solves the angle ambiguity and the far-near field in the existing hybrid source direction finding method. The technical problem of inefficiency of signal source separation method

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