TDM-MIMO radar system based on optimized sparse array and signal processing method thereof

Abstract

The invention discloses TDM-MIMO radar system based on an optimized sparse array and a signal processing method thereof. The system is based on a sparse array, and optimizes array distribution by using a genetic algorithm; and optimized array distribution lowers sidelobe level and improves energy use ratio. The system transmits an LFMCW signal by using a TDM technology without sending an orthogonal signal and matching filtering at a receiving end, thereby making radar system structure less complex. When the number of array elements is specific, DOA performance of the system is superior to thatof a traditional uniform linear array MIMO radar system; under the condition of identical array aperture, the system has the same DOA performance as the uniform linear array MIMO radar system, and has higher angular resolution and more concise structure. Further, the signal calibration processing method provided in the invention can improve accuracy and resolution of DOA estimation of a mobile target by the system effectively under a condition of low signal to noise ratio.

Description

technical field [0001] The invention belongs to the field of radar systems and signal processing, in particular to a TDM-MIMO radar system based on an optimized sparse array and a signal processing method thereof. Background technique [0002] MIMO is a multiple-input multiple-output technology, which is widely used in radar systems. Because the uniform linear array is easy to implement, most MIMO radars currently use a uniform linear array structure. When the array structure is fixed, the array aperture of the uniform linear array structure cannot be changed, which greatly limits the practical application of MIMO radar. In practical applications, in order to obtain a larger array aperture with a limited number of antenna elements, sparse Arrays are introduced into MIMO radars. Sparse array means that the array is sparsely arranged. Compared with the traditional uniform linear array, the number of array elements in the sparse array is less, but the sparse array can obtain ...

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Problems solved by technology

There are many optimization methods. For example, the minimum redundancy optimization is to exhaustively exhaust the number of array elements in a sparse array to obtain the least redundant array element number and array element arrangement position. However, when the number of array elements is large, the exhaustive method will make This method has a very large amount of calculation; particle swarm optimization starts from the random arrangement of sparse arrays, selects a certain variable such as the side lobe of the array pattern as the fitness function, and then searches for the optimal solution through iterative search, but it is easy Trapped in a loc

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