Adaptive Detection For Radar Target Based On Multistatic Radar

With the gradually increasing complexity of the electromagnetic environment,radar is facing more and more severe tests in the military and civilian fields.Compared with monostatic radar,multistatic radar can further explore the detection potential of radar from the perspective of waveform diversity,spatial diversity,and clutter diversity.For high-resolution maritime radar,modeling of sea clutter with space-time-varying characteristics and target detection in this background have always been a research topic that attracts much attention.Therefore,this article will study the adaptive target detection of multistatic radar and its related issues.The motivation for the design of adaptive target detection algorithms under the Bayesian framework is several fold.In particular,the traditional adaptive target detection and its parameter estimation method are often constraints.The main work and research results of this article are as follows:1.The amplitude statistical characteristics of sea clutter and the basic concepts of multistatic radar system are introduced.First,according to the evolution course of radar from low-resolution to high-resolution,the amplitude distributions of clutter model from Gaussian model to non-Gaussian model are reviewed.Moreover,the fitting differences of the measured data among all the amplitude distributions are briefly analyzed and compared.Then,the composition and common classification of multistatic radar system are introduced from a broad level.Then,we briefly introduce the Net RAD radar to be referred to in this article.2.Based on Bayesian framework,we mainly study the multistatic radar adaptive target detection algorithm under the background of compound Gaussian clutter.For high-resolution inhomogeneity clutter,adaptive detection algorithms based on the LRT criterion are designed.The proposed algorithms aim at making up for the shortcomings of the traditional adaptive detection algorithm,by fully considering the prior information of the clutter distribution parameters.Considering the two posterior distributions of the texture component and the speckle covariance matrix structure of the compound Gaussian model,three adaptive detectors,named M-BGLRT,MGLRT-MAP-? and MGLRT-MAP-? individually,are derived.With the aid of the posterior distribution of the distribution parameters,the experimental results show that the Bayesian framework effectively mitigates the performance loss of traditional adaptive detectors under high-resolution inhomogeneity clutter.At the same time,for the case of parameter mismatch,the three detectors also show considerable robustness and selectivity.3.The problem of homo-frequency interference in complex scenes is considered.The asynchronous homo-frequency interference cancellation methods are proposed by drawing on the characteristics of zeroing the power level inside the connected area but retaining the boundary information in the differential operation.The proposed method is decoupled from the existing scene segmentation algorithms.It also satisfies the accuracy requirements of practical applications and does not involve complex matrix operations.It is easy to transplant to existing systems without additional computational burden.In the end,a radar data visualization system based on asynchronous homo-frequency interference cancellation algorithm is formed.