Complex Radar Cross Section Of The Frequency Characteristics Of Study

The research of Radar Cross Section (RCS) of targets is of great importance in radar detection and stealthy techniques. At present, two methods of research RCS, computation and measurenment, have some difficulties in extremely large electric size targets. A common useful method to acquire RCS of large metal targets is scaling model measurement, which depends on measure conditions. In this paper, the principle of scaling model RCS measurement is demonstrated by integral equations and a novel method of scaling model while keeping measure frequency is formed. That is by changing the permittivity of medium around radar targets, a similar RCS relationship between scaled-model and prototype can be obtained, while measure a smaller scaled model under the same frequency of prototype. It is hopeful to explore new RCS measurement technique and improve the measurement capacity to large complex targets.In order to find the law of RCS from known data of target, Autoregressive (AR) modeling is applied to analyse RCS data. For targets containing some ideal scattering centers, it is found that AR model can accomplish RCS data fitting well, can find out the intrinsic laws of scattering and extrapolate in definite scope. For complex target's RCS data, AR model is useful in RCS data fitting, but can not extrapolate and predict unknown data effectively. After analyzing the property of AR model and phenomenon of RCS, it is pointed out that AR model is not based on the intrinsic laws of scattering, and resulted in ineffectually prediction, and a model that can predict unkown data must be set up according intrinsic laws. In order to find a mathematic model to extrapolate and predict, a notion of "intrinsic parameter fitting" has come to being. Its importance to effectively extrapolating has been emphasized and its properties been concluded.After sum-up all the scattering law about Radar frequency and compareing with other models, a parameter model based on scattering laws is established in this paper. For such a model with indefinite variables and composing of nonlinear function series, there are some difficulties to solve out parameters efficiently and effectively in mathematics. After a wide search, a novel method of fetching scattering information from inner product of detection functions and a novel method of least squares fitting with selective policies have been created in this paper. Combining them with nonlinear optimization techniques can solve that nonlinear model well.Applying this algorithm to measured RCS data, the parameter model can be solved out well, and a list of estimation criterions have been built up to judge whether the parameter model is correct or whether the extrapolation is dependable while solving the model parameters. The results of analysis and extrapolation about one naval vessel and two planes' RCS data at X band agreed well with measured data. All of them have strongly proved the correctness and validity of parameter model, model solving and the estimation criterions in this paper. The demand for RCS data used in the parameter model and a standard for RCS data simplification is roughly concluded.The method of analyzing RCS data in this paper can be used in simplification measurement data, approximation parameter model, analyse RCS frequency signature. And the achievement in this paper can bring new ideas about the thory of nonideal scaling model RCS measurement, and in the following research, it is hopeful to be used in target classification, identification and research in stealthy and anti-stealthy techniques.