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Height Measurement for Meter-wave MIMO Radar Based on Block Orthogonal Matching Pursuit Preprocessing
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摘要: 米波雷达具有很好的反隐身性能。多输入多输出(MIMO)雷达的波形分集具有高自由度特点，使MIMO雷达在检测和参数估计等方面具有更多优势，故米波MIMO雷达受到广泛研究。而测高是米波MIMO雷达最重要的问题之一。针对米波MIMO雷达测高问题，最大似然和广义多重信号分类方法是米波MIMO阵列雷达测高方法行之有效的算法，但其计算量大，工程中难以接受。该文提出一种基于块正交匹配追踪(BOMP)预处理的方法来降低计算量。首先对MIMO阵列接收数据稀疏化处理，然后通过数学操作将其变形至适合于BOMP算法的信号模型，然后利用粗栅格得到角度粗估计。并以此为初始值中心，取MIMO雷达波束宽度作为搜索范围。仿真结果表明该算法能有效降低搜索类测高算法的计算量。
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关键词:
- 米波多输入多输出雷达 /
- 测高 /
- 块正交匹配追踪 /
- 最大似然 /
- 广义多重信号分类
Abstract: Meter-wave radar has good anti-stealth performance. The waveform diversity of Multiple-Input Multiple-Output (MIMO) radar can result in a higher degree of freedom, which makes MIMO radar more advantageous in detection and parameter estimation. Therefore, meter-wave MIMO radar has been widely studied. The radar height measurement is one of the most important research problems of the meter-wave MIMO radar. The maximum likelihood and generalized multiple signal classification algorithms are effective for measuring the radar height. However, they feature heavy computation complexity. In this paper, a preprocessing method based on Block Orthogonal Matching Pursuit (BOMP) is proposed to reduce the computation. First, the received data of MIMO array are sparse-processed, and then, using a mathematical operation, they are transformed into a signal model suitable for the BOMP algorithm; then coarse angle estimation is obtained using a large search grid. The coarse angle estimation is taken as the initial value, and the MIMO radar beam width as the search range. The simulation results show that the proposed algorithm can effectively reduce the computation of the search-type height measurement algorithm. -
图 1 米波MIMO雷达低仰角测高镜面反射模型示意图
Figure 1. Schematic diagram of height measurement of low elevation target with meter wave MIMO radar under specular reflection model
图 2 基于BOMP稀疏恢复的支撑位置估计结果
Figure 2. Support position estimation results based on BOMP sparse recovery algorithm
图 3 最大似然和广义MUSIC的全空域搜索估计结果
Figure 3. Estimation results of maximum likelihood and generalized MUSIC under whole airspace searching
图 4 10次独立实验的基于BOMP预处理后最大似然的搜索范围结果
Figure 4. 10 independent experiments results based on maximum likelihood after BOMP preprocessing
图 5 10次独立实验的基于BOMP预处理后广义MUSIC的搜索范围结果
Figure 5. 10 independent experiments results based on generalized MUSIC after BOMP preprocessing
图 6 最大似然和广义MUSIC算法的角度估计RMSE
Figure 6. RMSE of angle estimation with maximum likelihood and generalized MUSIC algorithms
图 7 最大似然和广义MUSIC算法的高度测量RMSE
Figure 7. RMSE of height measurement with maximum likelihood and generalized MUSIC algorithms
图 8 预处理和非预处理算法运算时间比较结果
Figure 8. Comparison results of run time of preprocessing and non-preprocessing algorithms
表 1 阵列雷达噪声子空间和导向矢量、导向矩阵的正交性
Table 1. Orthogonality of noise subspace of array radar and steering vector, manifold matrix
噪声子空间 导向矢量 导向矩阵(信号子空间) 常规阵列雷达 正交(MUSIC) 正交(广义MUSIC) MIMO阵列雷达 不正交(MUSIC) 正交(广义MUSIC) 
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表 2 BOMP算法的计算流程
Table 2. Calculation process of BOMP algorithm
输入：匹配滤波后的矢量化数据、角度网格数、目标数。 初始化：用接收数据初始化残差、用角度网格数初始化字典、初
始化支撑集。迭代：(1) 利用残差和字典计算投影； (2) 根据投影寻找块最大的坐标值，并将此值坐标放入块
支撑集；(3) 利用块支撑集更新残差； (4) 迭代(1)至(3)，迭代次数达到目标数停止。 输出：利用块支撑集计算块支撑向量。
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