Abstract:

Using the data compression technology like principal component analysis (PCA) for high resolution range profile (HRRP) feature extraction will lead to the decrease of recognition rate, for it only reflects the linear structure of HRRP in fixed azimuth frames which cannot accurately describe the target. So the mixtures of probabilistic PCA method is proposed for solving this problem, and the expectation maximization algorithm is adopted to estimate the statistic parameters for this method. Because the real data distribution can be obtained from the algorithm, this method would offer the potential to model the similar density of HRRP adequately for clustering to separate azimuth frame and reduce the local dimensionality for storing the template. Finally, the adaptive Gaussian classifier (AGC) and Kullback-Leibler (KL) distance classifier are both utilized to test the performance of obtained statistical features. Simulation experimental results show that this method not only reduce the dimensionality of HRRP, but also extract the statistical feature to eliminate the azimuth sensitivity.