基于运动参数非搜索高速机动目标检测

章建成; 苏涛; 吕倩

doi:10.11999/JEIT151042

基于运动参数非搜索高速机动目标检测

doi: 10.11999/JEIT151042

基金项目:

国家自然科学基金(61001204)，中央高校基本科研业务费专向资金(JY0000902020)

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- 被引次数: 0
出版历程
- 收稿日期: 2015-09-14
- 修回日期: 2016-01-22
- 刊出日期: 2016-06-19

High-speed Maneuvering Target Detection Based on Non-searching Estimation of Motion Parameters

Funds:

The National Natural Science Foundation of China (61001204), Fundamental Research Funds for the Central Universities (JY0000902020)

摘要

摘要: 该文提出一种新的高速机动目标检测与参数估计算法。首先，通过二阶Keystone变换(KT)消除距离频率与慢时间的二次耦合，并计算对称瞬时自相关函数(SIAF)。其次，对SIAF不同维依次进行尺度逆傅里叶变换(SIFT)、尺度傅里叶变换(SFT)和快速傅里叶变换(FFT)实现能量积累，在新的参数空间进行峰值检测得到径向速度模糊数和径向加速度估计值。最后，根据估计的参数构造补偿函数对距离徙动和多普勒扩散进行补偿，并通过KT算法实现目标检测和距离、模糊径向速度的估计，结合补偿的径向速度模糊数计算出不模糊径向速度。由于不需要进行参数搜索，并且SIFT和SFT均能通过FFT快速实现，因此算法计算量得到大幅度减小。仿真实验验证了该算法的有效性。
- 目标检测 /
- 参数估计 /
- 尺度变换 /
- 对称瞬时自相关函数
Abstract: A novel algorithm for high-speed maneuvering target detection and parameter estimation is proposed. Firstly, the second-order Keystone Transform (KT) is utilized to remove the quadric coupling between the range frequency and the slow time, after that, the Symmetric Instantaneous Autocorrelation Function (SIAF) is calculated. Secondly, in order to achieve energy accumulation, Scaled Inverse Fourier Transform (SIFT), Scaled FT (SFT), and Fast FT (FFT) are successively performed on the different dimensions of the SIAF to obtain a new parameter space, then peak detection is carried out to achieve the estimation of radial velocity ambiguity integer and radial acceleration. Finally, a compensation function is constructed to compensate the range migration and the Doppler spread, then the KT algorithm is employed to realize target detection and the estimation of targets range and ambiguous radial velocity, with the radial velocity ambiguity integer and ambiguous radial velocity, the unambiguous radial velocity can be calculated. Since the brute-force searching procedure is eliminated, moreover, the SIFT and the SFT can be implemented with the FFT operation, the computational complexity of proposed algorithm is greatly reduced. The simulation results demonstrate the effectiveness of the proposed algorithm.
- Target detection /
- Parameter estimation /
- Scaled transform /
- Symmetric Instantaneous Autocorrelation Function (SIAF)

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参考文献(19)

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