聯邦無跡卡爾曼濾波器於雙頻紅外線搜索與追蹤感測器網路之應用研究

Application Study of Federated Unscented Kalman Filter on Dual-Band Infrared Search and Track Sensor Networks

近年來由於無人空中拍照飛行機器人日漸盛行，人員操作不當導致發生意外，甚至闖越軍事禁區拍攝也時有所聞。有鑑於此，政府修訂相關法律以規範禁止飛越區域。當執法時，現行政策須有民眾檢舉或由相關執法單位人員至相關地點巡查。為節省時間、人力、費用及掌握電子證據，需要採用多個觀測站（內含雙頻紅外線搜索與追蹤感測器），並藉由多觀測站組成的感測器網路之聯邦濾波器（Federated Filter）進行目標追蹤。這個方法主要使用雙頻紅外線搜索與追蹤感測器取得含雜訊的角度量測數據及含雜訊的被動距離量測數據，將這些數據傳送至區域處理器（聯邦子濾波器），該處理器則採用無跡卡爾曼濾波器（Unscented Kalman Filter, UKF）進行狀態估計，最後再傳送至全域處理器（聯邦主濾波器）進行資訊融合。在感測器層級方面，透過雙頻紅外線搜索與追蹤感測器在球面座標系中量測目標的被動測距、方位角與俯仰角。在區域處理器中，UKF係使用參考直角座標系對目標運動進行狀態估計的軌跡描述。UKF係經由非線性無跡轉換（Unscented Transformations, UT）來處理狀態向量與誤差共變異矩陣的遞推與更新，讓估計算法更接近系統的非線性本質。最後，再將各區域處理器的狀態資訊傳送至全域處理器，使用聯邦主濾波器進行資訊融合，得到最後目標狀態估計之航跡描述並同時進行資訊反饋。為驗證演算法的有效性，本研究使用蒙地卡羅電腦模擬的方法進行測試。經過重複模擬實驗，演算法在追蹤精確度上有極佳的表現，從模擬的結果可得知，本論文所建議的演算法，適用於飛行物體的追蹤。 In recent years, unmanned aerial photographing of flying robots has become increasingly popular, and accidents have occurred due to improper operation of personnel, and even shooting in military forbidden areas has been heard. In view of this, the government revised the relevant laws to regulate the prohibition of overflight. When enforcing the law, the current policy must be reported by the public or patrolled by relevant law enforcement agencies personnel to the relevant locations. In order to save time, manpower, cost, and control of electronic evidence, multiple stations including dual-band infrared search and track sensors are needed, and target tracking is performed through sensor network federated filter calculations. The method is developed to obtain noisy data of target in passive ranging and angles and then sends the measurement data to dedicated local processor (federated sub-filter). However in the sensor level, the target range and the angles of azimuth and elevation are measured by each dual-band infrared search and track sensor in the sphere coordinate system. Each local processor uses the Unscented Kalman Filter (UKF) to perform state estimation and finally transmits the information to the global processor for information fusion. The target state is estimated by UKF in the reference rectangular coordinate system. Then the UKF handles the recursion and update of the state mean vector and the error covariance matrix via the Unscented Transformations (UT), which makes the estimation algorithm closer to the nonlinear nature of the system. Finally, the state information of each local processor is transmitted to the global processor (federated master filter) to integrate these state estimations to a final track for system state output and information feedback. To test the effectiveness of the algorithm, Monte Carlo computer simulation technique is adopted. After repeated simulation experiments, the algorithm has excellent performance in tracking accuracy. Therefore, from the results of the simulation, it can be known that the proposed algorithm is suitable for the tracking of flying objects.

In recent years, unmanned aerial photographing of flying robots has become increasingly popular, and accidents have occurred due to improper operation of personnel, and even shooting in military forbidden areas has been heard. In view of this, the government revised the relevant laws to regulate the prohibition of overflight. When enforcing the law, the current policy must be reported by the public or patrolled by relevant law enforcement agencies personnel to the relevant locations. In order to save time, manpower, cost, and control of electronic evidence, multiple stations including dual-band infrared search and track sensors are needed, and target tracking is performed through sensor network federated filter calculations. The method is developed to obtain noisy data of target in passive ranging and angles and then sends the measurement data to dedicated local processor (federated sub-filter). However in the sensor level, the target range and the angles of azimuth and elevation are measured by each dual-band infrared search and track sensor in the sphere coordinate system. Each local processor uses the Unscented Kalman Filter (UKF) to perform state estimation and finally transmits the information to the global processor for information fusion. The target state is estimated by UKF in the reference rectangular coordinate system. Then the UKF handles the recursion and update of the state mean vector and the error covariance matrix via the Unscented Transformations (UT), which makes the estimation algorithm closer to the nonlinear nature of the system. Finally, the state information of each local processor is transmitted to the global processor (federated master filter) to integrate these state estimations to a final track for system state output and information feedback. To test the effectiveness of the algorithm, Monte Carlo computer simulation technique is adopted. After repeated simulation experiments, the algorithm has excellent performance in tracking accuracy. Therefore, from the results of the simulation, it can be known that the proposed algorithm is suitable for the tracking of flying objects.