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Single-base MIMO radar target detection method based on Riemannian manifold

A radar target and detection method technology, applied in the direction of measuring device, radio wave measurement system, radio wave reflection/re-radiation, etc., can solve distance deviation, sample covariance matrix cannot replace statistical covariance matrix, and affect MIMO radar detection Performance and other issues

Active Publication Date: 2019-12-20
JILIN UNIV
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Problems solved by technology

[0004] In the process of MIMO radar target detection, when the number of snapshots is small, the sample covariance matrix cannot replace the statistical covariance matrix; moreover, to calculate the distance between points in the traditional Euclidean space, directly use the Euclidean distance Modulo calculation of vectors will lead to deviations in calculating the distance between two points on the manifold, which will affect the performance of MIMO radar target detection

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  • Single-base MIMO radar target detection method based on Riemannian manifold
  • Single-base MIMO radar target detection method based on Riemannian manifold
  • Single-base MIMO radar target detection method based on Riemannian manifold

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Embodiment Construction

[0085] figure 1 is the system model of single base MIMO radar target detection, M transmitting array elements transmit signals simultaneously, and use N receiving array elements to process the echo signals at the receiving end, where the direction of arrival is θ, and the distance between array elements is d= d t = d r =λ / 2. figure 2 It is a flow chart of the monostatic MIMO radar target detection method based on the Riemannian manifold, and its specific implementation steps are as follows:

[0086] Step 1: The signal received by the receiver of the monostatic MIMO radar is:

[0087]

[0088] Among them, a r (θ)=[1,e -j2πsin(θ)d / λ ,...,e -j2πsin(θ)(N-1)d / λ ] T is the receiving steering vector, a t (θ)=[1,e -j2πsin(θ)d / λ ,...,e -j2πsin(θ)(M-1)d / λ ] T is the transmitting steering vector, θ is the direction of arrival angle, λ is the signal wavelength, d is the distance between array elements, (·) T Indicates the transposition of the matrix; β is the target scatte...

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Abstract

The invention relates to a single-base MIMO radar target detection method based on Riemannian manifold, which belongs to the field of multiple-input multiple-output (MIMO) radar target detection. Through studying the MIMO radar target detection method based on Riemannian manifold, statistical covariance matrixes of received signals and noise are acquired by utilizing a regular burg recurrence method under single snapshot, a Riemannian distance between Riemannian mean values of the received signal statistical covariance matrix and the noise statistical covariance matrix is regarded as a detection statistic, whether a signal exists or not is judged by utilizing a judgment rule, the single-base MIMO radar target detection method belongs to a Riemannian geometric method, and the MIMO radar target detection performance at a low signal-to-noise ratio during single snapshot is improved.

Description

technical field [0001] The invention belongs to the field of multiple-input multiple-output (MIMO) radar target detection, relates to a single-base MIMO radar target detection method based on Riemannian manifolds, and is suitable for single-base MIMO radar target detection under low signal-to-noise ratio and single snapshot conditions. Background technique [0002] Multiple-input multiple-output (MIMO) radar is a new system radar that uses multiple antennas to transmit and receive, that is, multiple transmitting antennas simultaneously transmit mutually orthogonal signals, and then use multiple receiving antennas to receive signals, and use waveform diversity, etc. The technology processes and analyzes the received signals to improve the performance and estimation accuracy of radar target detection. [0003] Riemannian geometry is a geometric theory proposed by German mathematician Riemann in the middle of the 19th century. Riemannian manifold is a differential manifold. In ...

Claims

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Application Information

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IPC IPC(8): G01S13/04G01S7/292
CPCG01S13/04G01S7/292
Inventor 姜宏周美含孙帅郭帛洋赵安琪张铭航曲良东莫秀玲
Owner JILIN UNIV
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