Chaotic Inverse Filter and Fractal Parameter with Applications on Target Detection
混沌逆滤波与分形参数在目标检测中的应用

This paper provides methods to recover the dynamics and target characters from the measured data. An example of radar data proves that the dynamics hidden in observed data can be found by inverse filter of chaos. Using the Fractal Brownian Motion (FBM) model, we get fractal dimensions which show that fractal dimension of data from tracking vehicle by firing liquid is less than that by firing solid. It is also pointed out that chaotic inverse filter can be applied to increase the signal-to-noise ratio, and fractal dimension may be a new character index to identify targets in data processing on radar.