Subspace signal detection method based on signal structural feature under small sample condition
A signal structure and signal detection technology, applied in the field of multi-channel active phased array radar, can solve the problems of detector performance loss and underutilized training sample requirements, and achieve the effect of simple structure and signal accumulation.
Pending Publication Date: 2019-03-19
AIR FORCE EARLY WARNING ACADEMY
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However, when the actual clutter covariance matrix does not satisfy the above special structure, it often leads to serious performance loss of the designed detector
It is worth pointing out that, unlike the unknown covariance matrix of clutter, the structural information of the signal is usually known, but it is underutilized in reducing the demand for training samples
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[0017] The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments. For a radar system with N channels, when the dimension of the signal subspace is p, the signal matrix can be represented by an N×p dimensional full-rank matrix A. Use N×1 dimensional column vector x to represent the data to be detected. When the data to be detected contains targets, x can be written as
[0018] x=Aθ+n (1)
[0019] Among them, the p×1-dimensional column vector θ represents the unknown amplitude of the signal, and the N×1-dimensional column vector n represents the sum of the noise and clutter components in the data to be detected, and n obeys the Gaussian distribution with a mean value of zero and a covariance matrix of R . In practical applications, R is unknown, and in order to estimate it, training sample data is needed. Assuming that there are L training samples that only contain clutter and noise components, the lth trainin...
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The invention discloses a subspace signal detection method based on a signal structural feature under a small sample condition. The method comprises the steps of firstly, utilizing a signal matrix forconducting signal energy accumulation on to-be-detected data and training sample data; then utilizing the to-be-detected data and training sample data after signal accumulation, and designing a self-adaptive detector according to a generalized likelihood ratio criterion; then according to statistical properties of the self-adaptive detector and a false alarm probability preset by a system, determining detection threshold and judging whether or not a target exists. Compared with a traditional detection method, the designed method fully utilizes structural information of signals, and can work on the small training sample condition that the training sample number is smaller than a system channel number. According to the subspace signal detection method, the designed detector has the featureof constant false alarm, and an independent CFAR processing process is not needed. The designed detector is simple in structure and can achieve signal accumulation, clutter suppression and signal detection without complicated steps.
Description
technical field [0001] The invention relates to a subspace signal detection method based on signal structure characteristics under small sample conditions, and is especially suitable for multi-channel active phased array radars. Background technique [0002] Phased array radar consists of multiple transmitting and receiving array elements, and each antenna unit is controlled by an independent phase shift switch. By controlling the phase of each antenna unit's transmission, radar beams with different directions can be synthesized. Phased array radar fundamentally solves the congenital deficiency of inertial scanning delay of traditional mechanical radar, and has outstanding capabilities such as fast response speed, high target update rate, strong multi-target tracking capability, and superior electronic countermeasure capability. Phased array radar uses multiple array elements to transmit and receive data independently, and can obtain information of multiple channels. The mul...
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IPC IPC(8): G01S13/04
CPCG01S13/04G01S2013/0245
Inventor 刘维建段克清王永良杜庆磊张昭建
Owner AIR FORCE EARLY WARNING ACADEMY