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AI-PHD filter under unknown signal-to-noise ratio

A technology with unknown conditions and signal-to-noise ratio, applied in instruments, radio wave measurement systems, radio wave reflection/re-radiation, etc. It can solve the problem of inability to target RCS estimation, PHD filtering that does not make full use of target measurement information, and is difficult to adapt to dense Clutter environment, etc.

Active Publication Date: 2017-05-31
NAVAL AVIATION UNIV
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Problems solved by technology

[0008] The purpose of the present invention is to propose an AI-PHD filter under the condition of unknown signal-to-noise ratio, so as to solve the problems that the general PHD filter does not make full use of the target measurement information, cannot estimate the target RCS, and is difficult to adapt to the dense clutter environment.

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Embodiment Construction

[0094] The AI-PHD filter under unknown conditions of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0095] Without loss of generality, assuming that at any moment, the target moves in the two-dimensional observation area of ​​S=[-200,200]×[-200,200], and the target can appear and disappear randomly in this area, the total simulation time is K= 50s, sampling interval T=1s; the initial appearance of the target obeys the Poisson model, and its density function γ k (x)=0.2N(x|x 0 ,Q b ), N(·|x 0 ,Q b ) means that the mean is x 0 , the covariance is Q b Gaussian distribution, where x 0 =[0 2 0 - 2] T and Q b =diag([10 5 10 5]), the minimum signal-to-noise ratio SNR possible for the target min =10dB, the possible maximum signal-to-noise ratio SNR max =40dB, its echo obeys the Swerling fluctuation model; the radar is located at point (0,-100), which can provide the distance R of the target k , Azimuth θ k and ampli...

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Abstract

The invention discloses an AI-PHD filter under unknown signal-to-noise ratio, which belongs to the field of radar data processing. A multi-target tracking method based on PHD filtering does not make full use of target measurement information, cannot estimate the target RCS, and is difficult to adapt to a dense clutter environment. According to the invention, the AI-PHD filter under unknown signal-to-noise ratio solves the problems; the target amplitude information is combined based on PHD filtering; a signal-to-noise ratio variable related to the target RSC is introduced into a target state vector by using the feature that particle filter is easy to expand; when the target number and the target state are estimated, the target RCS is estimated; the calculation amount increases linearly with the increase in the number of measurements; and the AI-PHD filter is especially suitable for multi-target tracking in the dense clutter environment, overcomes the limitation of a general PHD filter, and has a strong engineering application value and promote prospect.

Description

technical field [0001] The invention relates to a radar data processing method, in particular to a multi-target tracking filtering method, which is suitable for radar tracking of multiple targets under the echo fluctuation model. Background technique [0002] Multi-target tracking is one of the difficult problems in the field of radar target tracking. In the case of dense clutter, on the one hand, due to the randomness of the appearance and disappearance of targets, the number of targets is often time-varying and unknown; on the other hand, due to the interference of clutter and noise, data association and detection have Uncertainty, the number of measurements is also random. In this case, using radar to track multiple targets requires estimating the state of each target with an uncertain number of targets from the time-varying measurement. Therefore, it is particularly difficult to track multiple targets in a dense clutter environment. How to make full use of various meas...

Claims

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Application Information

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IPC IPC(8): G01S13/72G01S7/41
CPCG01S7/415G01S13/726
Inventor 谭顺成王国宏贾舒宜王娜
Owner NAVAL AVIATION UNIV
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