Orthogonal propagation operator direction finding method based on multi-peak quantum cuckoo search mechanism

Abstract

The invention provides the orthogonal propagation operator direction finding method based on a multi-peak quantum cuckoo search mechanism. The method comprises the following steps of through samplingdata, acquiring the estimation values of a covariance matrix and a propagation operator; initializing parameters and quantum bird eggs, and calculating the fitness of the mapped bird eggs; initializing a memory pool; using Levy flight to update the quantum bird eggs and calculating the fitness of the mapped bird eggs; using a memory operator to update the memory pool; using a selection operator toselect; renewing new nests to update the quantum bird eggs and calculating the fitness of mapped bird eggs; using the memory operator to update the memory pool; using the selection operator to select; determining whether to reach a current phase iteration number; if the current phase iteration number is reached, using a purification operator to delete a similar memory element and entering into anext phase; and determining whether to reach a maximum iteration number: if the maximum iteration number is not reached, continuously carrying out iteration; otherwise, outputting all the quantum birdeggs in the memory pool, after mapping, defining as a wave reaching direction. The method possesses advantages that an operation amount is low, a convergence speed is fast and direction finding precision is high.

Description

technical field [0001] The invention relates to an orthogonal propagation operator direction finding method based on a multi-peak quantum cuckoo search mechanism, and belongs to the technical field of array signal processing. Background technique [0002] In the field of array signal processing, source direction finding is an important research direction, and it has been widely used in radar, sonar, communication and many other fields. In order to determine the direction of arrival (DOA) of multiple sources, the classic super-resolution direction finding algorithm is a subspace decomposition algorithm, such as the multiple signal classification (MUSIC) algorithm and the use of rotation invariant technology The estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm, etc. Subspace decomposition direction-finding algorithms represented by the MUSIC algorithm can use the constructed multi-peak spatial spectral function to measure the direction of...

AI Technical Summary

Problems solved by technology

However, the solution of the traditional orthogonal propagation operator method either uses spectral peak search with quantization error to calculate the incoming wave direction, or uses a multi-dimensional joint optimization solution with a huge amount of calculation, which cannot meet the needs of high-precision real-time direction finding. The multimodal quantum cuckoo search (MQCS) mechanism performs multimodal optimization, which greatly reduces computational complexity, avoids quantization errors during spectral peak search, and is easy to expand to multi-dimensional situations

[0005] Since most of the research work on the original cuckoo search mechanism focuses on searching for the global optimal solution, the final result can only converge to a global optimal solution. From a practical point of view, we not only hope to obtain the global optimal solution, but also hope to obtain as many local optimal solutions as possible, that is, we need to optimize multiple extreme points at the same time. In this way, the limitations of the original cuckoo search mechanism are highlighted come out

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