Research On Target Recognition Methods Of Millimeter-Wave Detector Using Manifold Learning

Millimeter-wave detection system, widely used in military and civil fields, has many advantages in comparison with microwave detection system and infrared detection system. The better signal processing methods are desired along with the improvement of detection precision. Manifold learning proposed in 2000 is a new theory of machine learning, aiming to find the latent feature of high-dimensionality data, reconstruct the low-dimensionality manifold, and reduce the dimensionality. In this paper, a few manifold learning algorithms are improved and used in the target recognition of millimeter-wave (MMW) detector. The main contents of this paper are stated below.The signal real-time denoising is explored based on lifting 9-7 wavelet for reducing the influence of noises. All parameters of the denoising algorithm are approximated and the denominators are changed to integer power of 2. Then the algorithm only involves multiplication, addition and shift of integer. The demand of hardware is analyzed for realizing the real-time denoising. The signal processing system is constructed based on DSP, and the denoising of passive MMW detector signal is realized.The typical nonlinear manifold learning algorithms are applied to feature extraction of passive MMW detector signal and MMW high range resolution radar signal. And the experimental results show that the methods are adaptable to the signal from MMW detecting system. The improved algorithm, Neighborhood Preserving Discriminant Projections (NPDP), is proposed by generalizing the virtues of Neighborhood Preserving Projections (NPP) and Linear Discriminant Analysis (LDA) and introducing between-class scatter matrix. And the improved algorithm, Uncorrelated Neighborhood Preserving Projections (UNPP), is proposed by introducing an uncorrelated constraint which leads the feature vectors extracted to be uncorrelated and reduces the redundant information. Combining NPDP and UNPP, the improved algorithm, Uncorrelated Discriminant Neighborhood Preserving Projections (UDNPP), is proposed. For adaptation of nonlinear problem, UNPP and UDNPP are extended as Kernel Uncorrelated Neighborhood Preserving Projections (KUNPP) and Kernel Uncorrelated Discriminant Neighborhood Preserving Projections (KUDNPP) by kernel method. The proposed algorithms are used for target recognition of MMW detector and the experimental results indicate their good performance.A new one-class classification algorithm is proposed based on the idea of Locally Linear Embedding (LLE). This algorithm firstly computes the reconstruction weights of unknown samples. Then an error, on which the class of samples can be decided based, is defined as a criterion. The algorithm is applied to target recognition of passive MMW detector and the experimental results indicate its good performance in comparison with current popular one-class classification algorithms.A new multi-class classification algorithm is proposed based on the idea of Locally Linear Embedding (LLE). The algorithm concerns the reconstruction error of samples and the error from different class samples in neighborhood. Actually, the errors reflect the relation between samples and the low-dimensionality manifold. The algorithm is applied to target recognition of MMW high range resolution radar based range profile. The experimental results indicate that the algorithm can classify efficiently. Compared with current popular multi-class classification algorithms, it shows better performance. Moreover, estimation of the parameters is simple and the result is hardly affected by selection of parameters. The detection precision is improved efficiently.