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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Detecting weak target is an important and challenging problem in manyapplications such as radar, sonar etc. However, conventional detection methodsare often ineffective in this case because of low signal-to-noise ratio (SNR).This paper presents a track-before-detect (TBD) algorithm based on an improvedparticle filter, i.e. cost-reference particle filter bank (CRPFB), which turnsthe problem of target detection to the problem of two-layer hypothesis testing.The first layer is implemented by CRPFB for state estimation of possibletarget. CRPFB has entirely parallel structure, consisting amounts ofcost-reference particle filters with different hypothesized prior information.The second layer is to compare a test metric with a given threshold, which isconstructed from the output of the first layer and fits GEV distribution. Theperformance of our proposed TBD algorithm and the existed TBD algorithms arecompared according to the experiments on nonlinear frequency modulated (NLFM)signal detection and tracking. Simulation results show that the proposed TBDalgorithm has better performance than the state-of-the-arts in detection,tracking, and time efficiency.

Track-before-detect Algorithm based on Cost-reference Particle Filter Bank for Weak Target Detection