基于IMMKF算法的ADS-B监视应用目标跟踪

目标跟踪是机载广播式自动相关监视（ADS-B）应用的基础功能，对提升航空器周边的弱机动民航飞机目标跟踪性能具有重要意义。提出一种基于交互式多模型卡尔曼滤波（IMMKF）算法的ADS-B 监视应用目标跟踪方法。首先，针对弱机动背景下的民航飞机的飞行特点，建立包含匀速模型和标准协同转弯模型的运动模型集，并对模型进行线性化近似；然后，将模型预测和ADS-B 状态矢量量测数据作为IMMKF 算法中多个并行卡尔曼滤波器的输入，进行并行滤波；最后，计算得到目标状态矢量的估计和模型近似概率，并作为下一次迭代的输入。结果表明：相比于基于匀速模型的卡尔曼滤波目标跟踪方法，IMMKF 方法的位置跟踪误差降低了59%，速度跟踪误差降低了77%，显著提升了状态估计性能，具备较高的跟踪精度、稳健性与计算效率，在ADS-B 监视应用中具有实际应用价值与借鉴意义。

ADS-B surveillance application target tracking based on IMMKF algorithm

Target tracking is the basic function of airborne ADS-B surveillance applications. Improving the target tracking performance of weak maneuvering airliner around the aircraft is of great significance for mastering the traffic situation and improving flight safety. Therefore, a target tracking method for ADS-B surveillance application based on interactive multiple model Kalman filter (IMMKF) algorithm is proposed. Firstly, aiming at the flight characteristics of airliner under the background of weak maneuver, a set of motion models including constant velocity model and standard coordinated turning model is established, and the models are linearized and approximated; Then, the model prediction and ADS-B state vector measurement data are used as the input of multiple parallel Kalman filters in IMMKF algorithm for parallel filtering; Finally, the estimation of the target state vector and the model approximation probability are calculated and used as input for the next iteration. The simulation results show that compared with the Kalman filter target tracking method based on the constant velocity model, the position tracking error of IMMKF method is reduced by 59%, and the velocity tracking error is reduced by 77%, which significantly improves the state estimation performance, and has high tracking accuracy, robustness and computational efficiency. It has practical application value and reference significance in ADS-B surveillance applications.