Target separation detection and motion parameter estimation method based on time-varying autoregressive model

Abstract

The authors report a target separation detection and motion parameter estimation method based on the time-varying autoregressive (TVAR) model. The TVAR model is used to extract the instantaneous Doppler frequencies of multiple targets or scattering points in a same radar range cell, and the motion parameters are estimated based on the Doppler frequencies at every pulse time in a radar frame. In particular, for coherent multipulse echo signal, the TVAR model is first utilised to extract multiple instantaneous Doppler frequencies at each pulse time, thus forming a Doppler frequency matrix. Second, the Doppler tracking and polynomial fitting methods are utilised to estimate the radial velocity and acceleration based on the Doppler frequency matrix. Finally, the target separation detection is achieved by acquiring and validating multiple Doppler frequency components in the same range cell. Simulations and verifications are carried out, and the results show that the proposed method is effective for target separation detection and precision motion parameter estimation, which could be of great value radar target detection, tracking, and automatic target recognition.

Keywords

• radar detection
• target tracking
• parameter estimation
• radar resolution
• radar target recognition
• radar tracking
• doppler radar
• object detection
• autoregressive processes
• radar signal processing
• radar imaging
• filtering theory
• target separation detection
• motion parameter estimation method
• time-varying autoregressive model
• tvar model
• multiple targets
• radar range cell
• motion parameters
• pulse time
• multiple instantaneous doppler frequencies
• doppler frequency matrix
• doppler tracking
• acquiring validating multiple doppler frequency components
• precision motion parameter estimation
• great value radar target detection
• automatic target recognition