An Extended Target Detection Method Guided by Range Profile Prior

‍ ‍Extended target detection often employs a range-profile energy-accumulation detection method. However， incomplete range-profile information often reduces the detection performance as a result of a collapsing loss. To address this issue， this paper proposes an extended target detection method that is aided by a prior range profile， which effectively utilizes the prior envelope model of the range profile to accumulate the signal and improve the detection performance. The proposed method takes into account the coherent superposition of the complex range profile and complex Gaussian white noise， as well as the inaccurate phase prediction. It uses a detection model that takes the modulus of the observed data， and based on the likelihood ratio detection theory， derives a feature square matching detector under a low signal-to-noise ratio. This detector matches the envelope model of the target complex range profile with the envelope model of the observed data using square matching， and uses a threshold judgment to determine the presence of the target. The acquisition of the prior range profile is achieved by extracting the two-dimensional scattering center from the ISAR image and projecting it in the radar line-of-sight direction under the corresponding posture angle to obtain an approximate one-dimensional scattering center model of the target. The envelope model of the target distance image is then generated based on this model. Furthermore， this study analyzed the relationship between the energy detector and feature square matching detector from both theoretical and experimental perspectives. An experimental verification was conducted using data obtained through the reconstruction of the scattering center model and darkroom measurement methods. The experimental results demonstrated that the prior range profile-aided feature square matching detector could effectively improve the detection performance for targets under a low signal-to-noise ratio and had good adaptability to a prior model mismatch. This approach provides a new way to improve extended target detection performance under certain prior range profile conditions.