To address the limitations of traditional radar in time-varying and complex environments, a novel closed-loop structure for cognitive radar is introduced in this paper. The proposed system begins by estimating the power spectrum density of clutter using a clutter inversion algorithm. Next it predicts the upcoming clutter information using an autoregression model and a clutter prior information matrix as input. Finally, it employs a water-filling method and a time-synthesis algorithm to design an optimal spectrum and a constant modulus transmit sequence utilizing the prediction clutter priori information. Moreover, experimental results using real-measured data under varying parameters demonstrate that the proposed strategy outperforms traditional radar process.
For bistatic inverse synthetic aperture radar (Bi-ISAR) cross-range scaling (CRS), it needs to estimate the effective rotational velocity (ERV) and correct linear-geometry distortion at the same time. In this paper, the effective rotational velocity (ERV), rotational center (RC) and ratio of linear-geometry distortion (RLGD) are jointly estimated by optimizing the image quality, which is measured by the image entropy. After parameter estimation and phase compensation, the image without linear-geometry distortion is generated by the matched Fourier transform (MFT). Numerical results validate that the proposed method works robust under different signal to noise ratio (SNR) conditions.
In order to improve the inverse synthetic aperture radar (ISAR) imaging quality of precession space target, an algorithm based on phase matching processing (PMP) of complex range profile envelope (CRPE) is proposed in this paper. By phase matching processing, only the echo components located at the scattering points corresponding to the main body of the warhead can be coherently accumulated. The echoes of other scattering centers without any coherence will cancel each other when they are transformed. Hence, the focusing of scatters centers of the warhead main body is improved and the interference caused by non-spin symmetric components is well suppressed. Simulation results confirmed the effectiveness of the method.
For the high-speed moving target, its high-resolution range profile (HRRP) obtained by wideband radar is stretched by the high order phase error. To obtain well-focused HRRP, the phase error induced by target velocity should be compensated, utilizing either measured or estimated target velocity. When the radar echo is under sampled, however, the HRRP will suffer from strong side and grid lobes, which deteriorates the performance of velocity estimation. A novel velocity estimation and compensation of high-speed target for under sampled data is proposed. The variational Bayesian inference based on the Laplacian scale mixture (LSM) prior is utilized to reconstruct HRRP with high resolution from the under sampled data. During the reconstruction of HRRP, the minimum entropy-based Newton method is used to estimate the velocity to compensate the high order phase error. Experimental results validate the effectiveness of the proposed velocity estimation and compensation algorithm.
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.