應用影像偵測架構於目標追蹤系統之研究

The Research of Applying Image Detection Algorithm to Target Tracking System

呂宗鏈(Chung-Lain Lu)

指導教授 ： 林志民

元智大學/電機通訊學院/電機工程學系/博士(2011年)

https://doi.org/10.6838/YZU.2011.00128

摘要

現今的國防科技日益進步，雷達多目標追蹤的條件皆較以往複雜，目標估測架構於雷達追蹤系統擔任重要角色，相關結合技術則為關鍵技術。本研究提出結合影像資料及數據資料之相關結合技術，其優點當感測器偵測到訊號之後，經過影像處理程序與資料結合相關技術，提供目標運動的資訊供追蹤系統參考判斷用有較多資訊，可得到較佳的追蹤結果。應用資料相關結合技術運算法於追蹤系統，擷取其特殊的運算架構，將之應用於本研究，並結合目標追蹤架構，以達到最佳的追蹤效果。本論文將提出三種不同追蹤技術作為比較，根據模擬結果，證明本研究所提之適應性偵測架構結合影像資料及數據資料，進而判別出正確的雷達感測資料與目標軌跡關係，將可得到較佳的追蹤結果。

關鍵字

數據資料 ； 目標影像資料 ； 資料相關結合技術 ； 影像處理 ； 適應性偵測器

並列摘要

A new technology of defense system is progressed day by day. The multiple-target tracking (MTT) conditions are more complicated for radar surveillance systems. The estimation algorithm plays an important role in a radar tracking system. Data association is the key technique in a radar multiple-target tracking system. An improved estimation approach using both quantity data and target feature is investigated in this research. The advantage of this approach is that the system will use image processing algorithm to obtain more target information. A data association is applied to match between radar measurements and existing target tracks. Moreover, an adaptive estimator is applied to combine the quantity data and target feature for estimation problems. In this dissertation, three tracking algorithms are applied for comparison. Based on the simulation results, the adaptive tracking procedure which applied to combine the quantity data and target feature for estimation problems has better tracking performance for multiple-target tracking systems.

並列關鍵字

Quantity data ； Target feature ； Data association ； Image processing ； Adaptive estimator

參考文獻

[2] C. Lin, Y. Liu, and C. Lee, “An Efficient Neural Fuzzy Network Based on Immune Particle Swarm Optimization for Prediction and Control Applications,” International Journal of Innovative Computing, Information, and Control, Vol. 4, No. 7, pp. 1171-1172, 2008.

[3] S. Puranik and J.K. Tugnait, “Tracking of Multiple Maneuvering Targets Using Multiscan JPDA and IMM Filtering,” IEEE Transactions on Aerospace and Electronic Systems, Vol. 43, No. 1, pp. 23-35, 2007.

[6] D. Sengupta and R.A. Iltis, “Neural Solution to the Multitarget Tracking Data Association Problem,” IEEE Transactions on Aerospace and Electronic Systems, Vol. 25, 1989, pp. 86-108.

[7] K.C. Chang, C.Y. Chong, and Y. Bar-Shalom, “Joint Probabilistic Data and Association Distributed Sensor Networks,” IEEE Transactions on Automatic Control, Vol. AC-31, pp.889-897, 1986.

[8] J.A. Roecker, “Multiple Scan Joint Probabilistic Data Association,” IEEE Transactions on Aerospace and Electronic Systems, Vol. 31, Issue 3, pp. 1204-1210, 1995.