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[an error occurred while processing this directive]收稿日期: 2012-06-28
网络出版日期: 2025-05-29
基金资助
国防预研基金
Millimeter Wave Detector Ground Target Recognition Algorithm Based on ODNHP
Received date: 2012-06-28
Online published: 2025-05-29
针对毫米波探测器识别地面目标时的强地杂波问题,在正交判别邻域保持投影(ODNPP)的基础上,提出正交判别邻域Hessian投影(ODNHP)算法。ODNHP在正交化约束和判别信息的基础上,利用样本的切空间坐标构造Hessian二次型矩阵,实现目标函数的优化。将ODNHP分别与NPP、ONPP和ODNPP进行了毫米波地面目标识别仿真实验,结果表明,ODNHP降低了杂波的影响,在较低的维数下实现更高的识别率。
关键词: 流形学习; 毫米波探测器; 地面目标识别; Hessian 算子
张蓉蓉 , 李跃华 , 王剑桥 , 孟飞 . 基于ODNHP 的毫米波探测器地面目标识别算法[J]. 弹箭与制导学报, 2012 , 32(6) : 183 -186 . DOI: 10.15892/j.cnki.djzdxb.2012.06.014
In view of strong ground clutter when millimeter wave detector recognizing the ground object, based on the orthogonal discriminant neighborhood Hessian projection (ODNHP) algorithm, the orthogonal discriminant neighborhood keep projection (ODNPP) was presented. Introducing Hessian operator in ODNHP can ensure the optimization of object function basis on keeping information of ODNPP Boolean manipulation in restraint and discrimination. The experiment of millimeter wave detector ground target recognition simulation is respectively used the algorithms of NPP、ONPP、ODNPP and ODNHP. The results show that ODNHP can reduce the influence of clutters and get higher recognition rate at the lower dimension.
| [1] | Xuelian Yu, Xuegang Wang, Benyong Liu. A direct kernel uncorrelated discriminant analysis algorithtn[J]. IEEE Signal Processing Letters, 2007, 14 (10): 742- 745. |
| [2] | Xuelian Yu, Xuegang Wang, Benyong Liu. Supervised kernel neighborhood preserving projections for radar target recognition[J]. Signal Processing, 2008, 88 (9): 2335- 2339. |
| [3] | Xuelian Yu, Xuegang Wang. Kernel uncorrelated neighborhood discriminative embedding for radar target recognition[J]. Electronics Letters, 2008, 44 (2): 154- 155. |
| [4] | Tenenbaum JB, Silva V, Langford JC. A global feometric framework for nonlinear dimensionality reduction[J]. Science, 2000, 290 5500: 2319- 2323. |
| [5] | Roweis ST, Saul LK. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000, 290 5500: 2323- 2326. |
| [6] | M Belkin, P Niyogi. Laplacian eigenmaps for dimensionality reduction and data representation[J]. Neural Computation, 2003, 15 (6): 1373- 1396. |
| [7] | DL Donoho, C Grimes. Hessian eigenmaps: New locally linear embedding techniques for high-dimensional data[J]. Proceedings of the National Academy of Sciences, 2005, 102 (21): 7426- 7431. |
| [8] | Y W Pang, L Zheng, ZK Liu, et al. Neighborhood preserving projections (NPP): a novel linear dimension reduction method[C] // ICIC 2005, Part I, Lecture Notes in Computer Science, Springer, 2005: 117- 125. |
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