An Approach to the Modeling of the Electromagnetic Scattering From Distributed Targets with Applications to Surface Ships
Don York Northam
Doctoral Dissertation, Year: 1983, Advisor: John S. Baras
There are three basic classes of radar-target models: probabilistic, deterministic, and stochastic. The primary application of the probabilistic models is the study of target detection, of the deterministic model is the obtaining of precise scattering solutions for complex targets, and of the stochastic models is the study of the stochastic properties of time-varying radar signals.
The purpose of this dissertation is to develop a stochastic model of distributed radar targets, especially ships, that directly incorporates target structure and motion. The model was required to be useful as a tool in the stochastic simulation and analysis of tracking-radar signals over time intervals that are short relative to the time constants of the target motion. The model is based upon the observation that distributed targets often appear to radars as being composed of several dominant scatterers. A concept (unit-scatterer) is introduced that quantifies this observation and that leads to a useful model of distributed targets. Based on this concept and assuming the presence of over-water multipath, analytical representations of radar cross sectoin and glint are developed and implications of the small time-interval requirement are investigated. Using these representations, a simulation is developed and used to investigate the stochastic properties of both radar cross section and glint for an example ship target. Simulation outputs are presented and analyzed to illustrate the implications of the model given variations in the significant parameters.
The model was developed to incorporate the major strengths of the existing deterministic and stochastic models: ability to account directly for target stricture and motion, and ease of obtaining target-signature time series, respectively. The deterministic models, though precise, are extremely inefficient in generating these time series and require a great deal of information about target structure and motion. The stochastic models do not directly account for target structure and motion and rely heavily upon target measurements. The model presented here efficiently generates target-signature time series given information about target structure and motion. The existing deterministic and stochastic models can be viewed as limiting cases of this new model.