﻿ 基于自适应融合的弹道目标空间位置重构<sup>*</sup>
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Space position reconstruction of ballistic target based on adaptive fusion
XU Dan, TIAN Bo, FENG Cunqian, HE Sisan, ZHAO Shuang
Air and Missile Defense College, Air Force Engineering University, Xi'an 710051, China
Received: 2016-06-01; Accepted: 2016-10-01; Published online: 2016-11-24 09:59
Foundation item: National Natural Science Foundation of China (61372166, 61501495)
Corresponding author. FENG Cunqian, E-mail:fengcunqian@sina.com
Abstract: Aimed at the problem that signal radar extracts parameters difficultly and has higher error in estimating parameters due to coupling of precession cone target parameters, a novel algorithm based on uniting the multi-aspect micro-motion information is proposed for parameter extraction and fusion. First, cone-shaped precession model are established and scattering points profile sequence are analyzed. After that, different-aspect conic node scattering point is matched and identified using Hough transform. Then, the micro-motion information of two radars is united as one solving unit to solve the coupling parameters, and other parameters also can be solved. Moreover, precession angle is taken as an example to analyze the variance of error, and each unit is fused by the weighed fusion array based on minimum variance. And other parameters are optimized in the same way. Finally, in one precession period, cone target space position can be reconstructed based on solving the coordinates of conic node and coning vector. Simulation results prove that the proposed method can realize the extraction of high accuracy parameters and cone's reconstruction availably.
Key words: precession     Hough transform     fusion     space position reconstruction     performance analysis

1 进动锥体目标距离像分析 1.1 锥体进动建模

 图 1 进动锥体目标模型示意图 Fig. 1 Sketch map of precession cone target model

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1.2 散射点关联

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2 参数求解 2.1 进动角θ、结构参数h1和雷达视角αi的求解

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2.2 结构参数h2r的求解

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3 进动角自适应融合 3.1 进动角加权系数矩阵求解

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3.2 进动角误差方差分析

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3.3 融合求解

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4 锥体目标三维空间位置重构 4.1 组网雷达模型

 图 2 宽带组网雷达示意图 Fig. 2 Sketch map of wideband netted radar
4.2 三维矢量求解

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5 仿真分析 5.1 仿真结果

 图 3 求解单元1中2部雷达测得的时间-距离像 Fig. 3 Time-range profiles measured by two radars of solving unit 1
 图 4 提取雷达2散射点A中值和幅值过程 Fig. 4 Extraction process of scattering point A's mean value and amplitude value from radar 2

 参数 理论值 单元1 单元2 单元3 3个单元参数融合 估计值 误差/% 估计值 误差/% 估计值 误差/% 参数融合 误差/% θ 10° 10.332° 3.32 10.317° 3.17 9.774° 2.26 10.184° 1.84 xA -0.275 -0.282 2.54 -0.268 2.54 -0.271 1.45 0.272 1.10 yA 1.286 1.318 2.49 1.307 1.63 1.305 1.48 1.310 1.87 zA 1.262 1.292 2.38 1.288 2.06 1.284 1.74 1.286 1.90 ex 0 0.000 1 0.01 0.000 1 0.01 0.000 1 0.01 0.000 1 0.01 ey 0.707 0.724 2.40 0.720 2.16 0.721 1.98 0.722 2.18 ez 0.707 0.723 2.26 0.720 1.84 0.719 1.70 0.721 1.98

 参数 理论值 雷达1 雷达2 雷达3 3部雷达参数融合 估计值 误差/% 估计值 误差/% 估计值 误差/% 参数融合 误差/% h1 2 m 2.053 m 2.65 1.968 m 1.60 1.972 m 1.40 1.977 m 1.25 h2 0.5 m 0.514 m 2.80 0.512 m 2.40 0.507 m 1.40 0.511 m 1.80 r 0.5 m 0.492 m 1.60 0.507 m 1.40 0.507 m 1.40 0.506 m 1.20 α1 145° 146.58° 1.09 α2 167° 165.74° 0.75 α2 177° 176.15° 0.48

 图 5 锥体顶点的三维重构 Fig. 5 Three-dimensional reconstruction of conic node
5.2 算法性能分析

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 图 6 求解单元误差性能分析 Fig. 6 Error performance analysis of solving unit
6 结论

1) 进动角和结构参数误差的大小与雷达性能是相关的。可通过组网雷达的不同性能进行融合处理，降低参数误差。

2) 锥顶坐标和锥旋矢量可以实现锥体目标空间位置的重构，利用融合的参数重构出的目标位置与理论位置接近。

3) 所有单元求出参数进行融合估计与直接将所有参数进行平均估计相比，参数整体误差要小。

4) 进动目标空间位置重构可以为目标识别和目标二维成像提供一定参考。

5) 本文方法不仅可以运用在微动层面，在利用组网进行参数优化求解上均有一定的参考意义。

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#### 文章信息

XU Dan, TIAN Bo, FENG Cunqian, HE Sisan, ZHAO Shuang

Space position reconstruction of ballistic target based on adaptive fusion

Journal of Beijing University of Aeronautics and Astronsutics, 2017, 43(6): 1216-1223
http://dx.doi.org/10.13700/j.bh.1001-5965.2016.0463