scholarly journals Aspects of the Subarrayed Array Processing for the Phased Array Radar

2015 ◽  
Vol 2015 ◽  
pp. 1-21 ◽  
Author(s):  
Hang Hu
Keyword(s):  
Phased Array ◽  
Array Processing ◽  
Direction Finding ◽  
Adaptive Beamforming ◽  
Fast Time ◽  
Configuration Optimization ◽  
Numerical Examples ◽  
Phased Array Radar ◽  
Time Space ◽  
Space Time Adaptive Processing

This paper gives an overview on the research status, developments, and achievements of subarrayed array processing for the multifunction phased array radar. We address some issues concerning subarrayed adaptive beamforming, subarrayed fast-time space-time adaptive processing, subarray-based sidelobe reduction of sum and difference beam, subarrayed adapted monopulse, subarrayed superresolution direction finding, subarray configuration optimization in ECCM (electronic counter-countermeasure), and subarrayed array processing for MIMO-PAR. In this review, several viewpoints relevant to subarrayed array processing are pointed out and the achieved results are demonstrated by numerical examples.

scholarly journals STAP Optimization of Airborne Phased Array Radar in Nonuniform Environment Based on EFA Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Bin Tang ◽  
Xiaoxia Zheng ◽  
Mingxin Liu ◽  
Mengxu Fang
Keyword(s):  
Dimensionality Reduction ◽  
Spatial Data ◽  
Phased Array ◽  
Small Sample ◽  
Sample Number ◽  
Phased Array Radar ◽  
Data Dimensionality Reduction ◽  
Airborne Phased Array Radar ◽  
Processing Optimization ◽  
Space Time Adaptive Processing

EFA (extended factored approach) algorithm is the main method of space-time adaptive processing technology (STAP) for airborne phased array radar, but it is faced with many problems, such as large number of samples and large amount of calculation. Therefore, this paper uses a method of spatial data dimensionality reduction processing based on cyclic iterative calculation to optimize its STAP. The final experimental results show that, after spatial data dimensionality reduction processing optimization, the STAP performance of EFA algorithm is further expanded in the range of sample number adaptation; especially in the case of small sample number, the optimized STAP performance has been basically close to the ideal compared with other optimization schemes; tap performance also proves that the optimization scheme in this paper has better convergence speed and STAP performance.

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