Multi-target tracking of birds in complex low-altitude airspace based on GM_PHD filter

The Journal of Engineering

Weishi Chen¹  Xiaolong Chen²  Tao Hong³  Wu Zhang³  Xinru Fu³ 

[1] Airport Research Institute, China Academy of Civil Aviation Science and Technology;Radar Detection Research Section, Naval Aviation University;School of Electronics and Information Engineering, Beihang University;

关键词: target tracking;    filtering theory;    probability;    gaussian processes;    feature extraction;    mixture models;    air safety;    nearest neighbour methods;    pattern clustering;    radar imaging;    radar tracking;    multitarget tracking;    gm_phd filter;    flying birds;    airport;    birth detection;    track extraction;    k-nearest neighbour algorithm;    birth intensity function;    clustering algorithm;    filter framework;    filtering results;    gaussian mixture implementation;    bt_gm_phd algorithm;    multiple flying bird targets;    complex low-altitude airspace;    gaussian mixture of probability hypothesis density;    death detection;    bird tracking gm_phd algorithm;   

DOI  :  10.1049/joe.2019.0746

来源: DOAJ

【 摘 要 】

GM_PHD (Gaussian mixture of probability hypothesis density) cannot completely track multiple targets, such as the flying birds in the complex low-altitude airspace near the airport, due to the lack of the steps of birth detection, track extraction and death detection. A new algorithm is proposed to solve this problem, which mainly contributes to the following three aspects. First, the k-nearest neighbour algorithm is used to detect the birth of bird targets from measurements which is necessary to construct the birth intensity function. Second, the clustering algorithm is introduced into the probability hypothesis density filter framework to extract the bird targets’ tracks from the filtering results. Third, an algorithm is added to detect the death of bird targets for better tracking. The Gaussian mixture implementation of the algorithm denoted as BT_GM_PHD (Bird Tracking GM_PHD) is presented. The test results on simulation and ground-truth data show that the proposed BT_GM_PHD algorithm can effectively track the multiple flying bird targets in the complex low-altitude airspace near the airport, outperforming the GM_PHD filter.

【 授权许可】

Unknown