Adaptive Detection Method Based On Array Processing At Subarray Level

With phased array antenna’s huge size, processing at subarray level is neededto reduce the cost and the computing burden.The task of the radar system is signaldetection and parameter estimation, constant false alarm detection algorithm candetect the target with the constant false alarm probability, various adaptivebeamforming algorithms should be oriented on these tasks. When the interferenceimpinges on the antenna sidelobe, sidelobe blanking technology can reduce the falsealarm probability effectively.Adaptive detection is an important issue in the radarsignal processing. Ensuring the constant false alarm performance, adaptive detectionhas good anti-jamming performance and target detection performance. Purpose ofthis paper is to apply the adaptive beamforming and guard channel shaping insidelobe blanking to the point target adaptive detection.Adaptive beamforming methods includes linear constrained minimum variancemethod, normalization method and optimal mismatch detection method.Linear constrained minimum variance method destroys the adaptive pattern,especially when there there is no interference but noise, the sidelobe level ofadaptive pattern rises obviously. For normalization and optimal mismatch detectionmethod there is no need to select between adaptive and non-adaptive mode, and theyhave a bility to keep the sidelobe level as low as possible. The new adaptivemethods are completely characterized with respect to both the pattern controlcapabilities and the detection capabilities and their performance compared eachother as well as the performance of the linear constrained minimum variance.Conventional guard channel based on the subarray level is constructed by theincoherent sum of the subarrys. Conventional adaptive guard channel based on thesubarray level is constructed by pre-whitening data of the subarrys. Improved guardchannel based on the subarray level is constructed by combining incoherent andcoherent summation. Improved adaptive guard channel based on the subarray levelis constructed by pre-whitening data of the subarrys. Compared with conventionalguard channel, the improve guard channel pattern is much smoother and has muchbetter performance of beam and sidelobe blanking.Applying the adaptive beamforming methods including linear constrainedminimum variance method, normalization method and optimal mismatch detectionmethod and guard channel shaping to generalized likelihood ratio of adaptivedetection algorithm, the generalized likelihood ratio test statistics with differentkinds of weights are constructed.Applying the adaptive beamforming methods including linear constrained minimum variance method, normalization method and optimal mismatch detectionmethod to adaptive matched filter detection of adaptive detection algorithm, theadaptive matched filter test statistics with different kinds of weights are constructed.The simulation results demonstrate the adaptive detection algorithms based ondirect subarray weighting effective. In the case of the same subarray weighted, whenthe signal-to-noise ratio is low, the performance of generalized likelihood ratio issuperior to the performance of adaptive matched filter detection; when thesignal-to-noise ratio is high, the performance of the adaptive matched filterdetection is superior to the performance of the generalized likelihood ratio testdetection. The performance of the generalized likelihood ratio test statistics withlinear constrained minimum variance is superior.