This paper focuses on the issue of reduced estimation accuracy in target tracking systems due to the presence of varying levels of glint noise in radar measurements. Specifically, the performance degradation of the widely used the Huber Converted Measurement Kalman Filter (HCMKF) algorithm is addressed as the pollution rate of glint noise in measurements increases. In this paper, the Robust Converted Measurement Kalman Filter (RCMKF) algorithm based on approximate least absolute deviation is proposed to address this issue. Simulation results indicate that under measurements with lower pollution rate of glint noise, this method performs slightly better than HCMKF, and under measurements with higher pollution rate of glint noise, it outperforms HCMKF significantly. Moreover, the proposed algorithm has a lower computational cost compared to HCMKF.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.