A novel efficient track initiation method is proposed for the harsh underwater target tracking environment(heavy clutter and large measurement errors): track splitting, evaluating, pruning and merging method(TSEPM). Track initiation demands that the method should determine the existence and initial state of a target quickly and correctly.Heavy clutter and large measurement errors certainly pose additional difficulties and challenges, which deteriorate and complicate the track initiation in the harsh underwater target tracking environment. There are three primary shortcomings for the current track initiation methods to initialize a target:(a) they cannot eliminate the turbulences of clutter effectively;(b) there may be a high false alarm probability and low detection probability of a track;(c) they cannot estimate the initial state for a new confirmed track correctly. Based on the multiple hypotheses tracking principle and modified logic-based track initiation method, in order to increase the detection probability of a track,track splitting creates a large number of tracks which include the true track originated from the target. And in order to decrease the false alarm probability, based on the evaluation mechanism, track pruning and track merging are proposed to reduce the false tracks. TSEPM method can deal with the track initiation problems derived from heavy clutter and large measurement errors, determine the target’s existence and estimate its initial state with the least squares method. What’s more, our method is fully automatic and does not require any kind manual input for initializing and tuning any parameter. Simulation results indicate that our new method improves significantly the performance of the track initiation in the harsh underwater target tracking environment.
近年来数据挖掘技术的快速发展使得利用水下机器人作业过程中积累的大量数据进行故障诊断成为可能;基于数据挖掘的故障诊断技术能够从数据中获取潜在的诊断知识;针对水下机器人推进器系统数据特征,提出一种基于聚类和距离的离群点检测方法(outlier detection based on dbscan and distance,ODDD);首先,对数据进行粗聚类,然后采用剪枝规则进行离群点检测,来实现故障诊断;仿真实验结果表明算法能够实现水下机器人快速有效的故障检测。
Inherent flaws in the extended Kalman filter(EKF) algorithm were pointed out and unscented Kalman filter(UKF) was put forward as an alternative.Furthermore,a novel adaptive unscented Kalman filter(AUKF) based on innovation was developed.The three data-fusing approaches were analyzed and evaluated in a mathematically rigorous way.Field experiments conducted in lake further demonstrate that AUKF reduces the position error approximately by 65% compared with EKF and by 35% UKF and improves the robust performance.
为了进一步提高自主水下机器人(AUV)纯方位目标跟踪能力,从AUV轨迹优化方面进行了研究.采用基于距离的分段轨迹优化方法:在跟踪目标的初始阶段以定位的位置误差GDOP(geometrical dilution of precision)作为优化对象,以期在定位跟踪的各个时刻能得到最优的定位精度;针对目标运动要素(位置、速度、航向等)估计趋于收敛的情况,提出了一种基于短期预测的轨迹优化方法,AUV根据物理条件限制预测双方短期状态,计算能够反映跟踪态势特征的收益函数,根据收益函数对自身某状态进行评估,估算出自身各个预测状态的综合收益后,选出综合收益最大的那个状态作为短期目标,执行能到达该状态的行为.目标运动要素估计中使用扩展卡尔曼滤波(EKF).最后,将该轨迹优化方法与基于GDOP的轨迹优化进行仿真对比,结果表明该方法能够实现AUV与目标较快汇合.
