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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper addresses the problem of real-time detection and tracking of anon-cooperative target in the challenging scenario with almost no a-prioriinformation about target birth, death, dynamics and detection probability.Furthermore, there are false and missing data at an unknown yet low rate in themeasurements. The only information given in advance is about thetarget-measurement model and the constraint that there is no more than onetarget in the scenario. To solve these challenges, we model the movement of thetarget by using a polynomial trajectory function of time (T-FoT), which aims toestimate the continuous-time trajectory of the target rather than a series ofdiscrete-time point estimates as is done in most existing filters/trackers.Data-driven T-FoT initiation and termination strategies are proposed foridentifying the (re-)appearance and disappearance of the target. During theexistence of the target, real target measurements are distinguished fromclutter if the target indeed exists and is detected, in order to update theT-FoT at each scan for which we design a least-squares estimator. Overall, ourapproach is Markov-free, data-driven yet analytical. Simulations using eitherlinear or nonlinear systems are conducted to demonstrate the effectiveness ofour approach in comparison with the Bayes optimal Bernoulli filters. Theresults show that our approach is comparable to the perfectly modeled filters,even outperforms them in some cases while requiring much less a-prioriinformation and computing much faster.

From Target Tracking to Targeting Track: A Data-Driven Yet Analytical Approach to Joint Target Detection and Tracking