English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

To enhance maneuvering target tracking in modern battlefield, cognitive radar could adjust its waveforms and information processing manner. In this paper, a novel adaptive waveform design method based on multiple model interaction and measurement information fusion is developed. First, some latest measurements and virtual ones are collected to exploit more robust information. Second, the unknown target state is formulated via the multi-model idea, and the tracking framework is highlighted by the matrix-weighted multi-model fusion (MMF) in lieu of the probability-weighted way. Finally, the MMF output covariance matrix is selected as the ellipse metric, and ellipse parameters can be obtained by using the eigenvalue decomposition. Given these parameters, fractional Fourier transform is used to rotate the measurement error-ellipse to make them orthogonal, and further obtain the desirable rotating orientations for the cognitive transmitting waveform. Simulations show that compared with several algorithms, e.g., MIMM and IMM, our algorithm could further improve tracking performance and robustness.

Cognitive Tracking Waveform Design Based on Multiple Model Interaction and Measurement Information Fusion