Ms. Jing Chen Profile

Jing Chen

at Institute of Optics and Electronics CAS

SPIE Involvement:

Author

Publications (2)

Proceedings Article | 27 September 2016 Paper

Online damage inspection of optics for ATP system

Jing Chen, Yu Jiang, Yao Mao, Xun Gan, Qiong Liu

Proceedings Volume 9684, 968432 (2016) https://doi.org/10.1117/12.2243287

KEYWORDS: Acquisition tracking and pointing, Optical inspection, Inspection, Optical components, Image segmentation, Image acquisition, Image processing, Imaging systems, Video, Air contamination

Read Abstract +

Proceedings Article | 27 September 2016 Paper

Research of maneuvering target prediction and tracking technology based on IMM algorithm

Zheng Cao, Yao Mao, Chao Deng, Qiong Liu, Jing Chen

Proceedings Volume 9684, 968430 (2016) https://doi.org/10.1117/12.2243242

KEYWORDS: Detection and tracking algorithms, Statistical analysis, Statistical modeling, Electro optical modeling, Motion models, Unmanned aerial vehicles, Error analysis, Target acquisition, Electronic filtering, Filtering (signal processing)

Read Abstract +

Maneuvering target prediction and tracking technology is widely used in both military and civilian applications, the study of those technologies is all along the hotspot and difficulty. In the Electro-Optical acquisition-tracking-pointing system (ATP), the primary traditional maneuvering targets are ballistic target, large aircraft and other big targets. Those targets have the features of fast velocity and a strong regular trajectory and Kalman Filtering and polynomial fitting have good effects when they are used to track those targets. In recent years, the small unmanned aerial vehicles developed rapidly for they are small, nimble and simple operation. The small unmanned aerial vehicles have strong maneuverability in the observation system of ATP although they are close-in, slow and small targets. Moreover, those vehicles are under the manual operation, therefore, the acceleration of them changes greatly and they move erratically. So the prediction and tracking precision is low when traditional algorithms are used to track the maneuvering fly of those targets, such as speeding up, turning, climbing and so on. The interacting multiple model algorithm (IMM) use multiple models to match target real movement trajectory, there are interactions between each model. The IMM algorithm can switch model based on a Markov chain to adapt to the change of target movement trajectory, so it is suitable to solve the prediction and tracking problems of the small unmanned aerial vehicles because of the better adaptability of irregular movement. This paper has set up model set of constant velocity model (CV), constant acceleration model (CA), constant turning model (CT) and current statistical model. And the results of simulating and analyzing the real movement trajectory data of the small unmanned aerial vehicles show that the prediction and tracking technology based on the interacting multiple model algorithm can get relatively lower tracking error and improve tracking precision comparing with traditional algorithms.

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years