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Dynamic based trajectory estimation and tracking in an uncertain environment

機(jī)譯：不確定環(huán)境中基于動(dòng)態(tài)的軌跡估計(jì)和跟蹤

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This paper develops smoothing data association based on integrated probabilistic data association (FLIPDA-S) tracker to identify a multicopter UAV (MUAV) and estimate its trajectory in an uncertain and cluttered environment. Recently, a number of unidentified MUAVs have been flown in prohibited airspace and a radar system has been utilized to identify the MUAV and track the trajectory. However, target tracking methods could have difficulties to track the target MUAV through randomly distributed and moving objects. The vehicle state estimation (VSE) algorithm, which utilize FLIPDA-S in this paper, adopts UAV kinematics and dynamics for tracking a UAV in clutter and false targets with significant UAV identification performance. The performance of the method has been validated with and without an initial position of the target MUAV. Both numerical simulation and experiments are demonstrated to verify the effectiveness and accuracy of FLIPDA-S with VSE algorithm to track UAVs in an uncertain and clutter environment.
機(jī)譯：本文開發(fā)基于綜合概率數(shù)據(jù)關(guān)聯(lián)（FLIPDA-S）跟蹤器來(lái)識(shí)別一個(gè)multicopter UAV（MUAV）并估計(jì)在一個(gè)不確定的和混亂的環(huán)境其軌跡平滑數(shù)據(jù)關(guān)聯(lián)。最近，一些身份不明的MUAVs已經(jīng)飛行在空中禁區(qū)和雷達(dá)系統(tǒng)已經(jīng)被用于識(shí)別MUAV和跟蹤軌跡。然而，目標(biāo)跟蹤方法可能有困難跟蹤通過(guò)隨機(jī)分布和移動(dòng)對(duì)象的目標(biāo)MUAV。車輛狀態(tài)估計(jì)（VSE）算法，其利用FLIPDA-S在本文中，采用UAV運(yùn)動(dòng)學(xué)和動(dòng)力學(xué)用于跟蹤在雜波和假目標(biāo)的UAV與顯著UAV識(shí)別性能。該方法的性能已經(jīng)被驗(yàn)證具有和不具有目標(biāo)MUAV的初始位置。兩個(gè)數(shù)值模擬和實(shí)驗(yàn)證明驗(yàn)證FLIPDA-S與VSE算法的有效性和準(zhǔn)確性來(lái)跟蹤在一個(gè)不確定的和雜波環(huán)境無(wú)人機(jī)。

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