Detection and Parameter Estimation of Air Maneuvering Targets via Reconstructing Time Samples
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摘要: 空时自适应处理(Space-Time Adaptive Processing, STAP)是一种有效的机载预警雷达动目标检测方法。但当来袭目标具有很强的机动性时，其多普勒频率随时间变化，使得传统STAP方法的相参积累性能大大下降。针对这种情况，该文提出了一种将STAP与分数阶Fourier变换(FRactional Fourier Transform, FRFT)相结合的机载雷达空中机动目标检测和参数估计方法。该方法利用空间采样来重构时间采样，等效于增加了单个阵元的脉冲点数，解决了由于机载预警雷达在一个相干处理时间内发射脉冲点数有限而导致直接应用FRFT估计精度较差的问题。仿真结果证明了该方法的有效性。
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关键词:
- 机载预警雷达 /
- 机动目标 /
- 空时自适应处理(STAP) /
- 分数阶Fourier变换(FRFT)
Abstract: Space-Time Adaptive Processing (STAP) is an effective method for moving target detection in airborne radar. However, the Doppler frequency will change with time when the target has strong maneuvering, which would degrade the coherent integration performance of STAP significantly. For the detection and the parameter estimation of air maneuvering targets, a new algorithm is proposed in this paper, which combines STAP with FRactional Fourier Transform (FRFT). Space samples are used to reconstruct the time samples, which is equivalent to increasing the number of time samples within a Coherent Processing Interval (CPI). Consequently, the problem of the poor estimation performance by using the FRFT, which is caused by the limited number of time samples of airborne radar within a CPI, can be resolved. Numerical examples are provided to demonstrate the performance of the proposed algorithm. -
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