In this thesis an effective algorithm is proposed to detect and estimate distributed target of unknown range profile, embedded in unknown Gaussian noise, unknown Doppler frequency.The noise variance matrix is uncorrelated in range but correlated in time, which allows the detection. The multidimensional signal model was the first time applied for detection in mono static radar, and generalized likelihood ratio test was resorted to eliminate the nuisance parameters. Simulation results revealed high detection performance and target estimation under relative low SNR.
In this thesis an effective algorithm is proposed to detect and estimate distributed target of unknown range profile, embedded in unknown Gaussian noise, unknown Doppler frequency.The noise variance matrix is uncorrelated in range but correlated in time, which allows the detection. The multidimensional signal model was the first time applied for detection in mono static radar, and generalized likelihood ratio test was resorted to eliminate the nuisance parameters. Simulation results revealed high detection performance and target estimation under relative low SNR.
