Multiple Target Tracking Based on Multiple Hypotheses Tracking and Modified Ensemble Kalman Filter in Multi-Sensor Fusion

Abstract

In multi-sensor fusion (MSF), the integration of multi-sensor observation data with different observation errors to achieve more accurate positioning of the target has always been a research focus. In this study, a modified ensemble Kalman filter (EnKF) is presented to substitute the traditional Kalman filter (KF) in the multiple hypotheses tracking (MHT) to deal with the high nonlinearity that always shows up in multiple target tracking (MTT) problems. In addition, the multi-source observation data fusion is also realized by using the modified EnKF, which enables the low-precision observation data to be corrected by high-precision observation data, and the accuracy of the corrected data can be calibrated by the statistical information provided by the EnKF. Numerical studies are given to demonstrate the effectiveness of our proposed method and the results show that the MHT-EnKF method can achieve remarkable enhancement in dealing with nonlinear movement variation and positioning accuracy for MTT problems in MSF scenario.

Synthesis and thermal stability of ZrO2@SiO2 core–shell submicron particles

“…It is well known that sol-gel derived amorphous zirconia rst transitions from the amorphous state to the metastable tetragonal phase and, upon further increasing the temperature, to the stable monoclinic phase. 35,36,43,44,[55][56][57][58][59][60][61][62] The initial transition to the tetragonal phase is attributed to the local coordination environment and short-range order in the amorphous phase that is more similar to the tetragonal rather than the monoclinic polymorph. 36,56,59,[62][63][64] Thus, the initial crystallization yields the tetragonal phase.…”

A New Multiple Hypothesis Tracker Using Validation Gate with Motion Direction Constraint

“…In this section, a new multiple hypothesis tracker using validation gate with motion direction constraint (MHT-MDC) is proposed. The MHT-MDC flowchart that is based on the TOMHT logic [ 14 , 15 , 16 ] is shown in Figure 2 . We utilize the prior target maneuvering information to design the MDC gate, and then calculate the gate volume and estimate the clutter density in the gate.…”

Một thuật toán tối ưu bám quỹ đạo mục tiêu của bài toán quan sát đa mục tiêu trong trường hợp có mục tiêu bị che khuất

“…Các thuật toán chính hiện hành bao gồm: thuật toán lân cận gần nhất toàn cục (GNN: Global Nearest Neighbors) [1][2][3], thuật toán kết hợp dữ liệu xác suất đồng thời (JPDA: Joint Probabilistic Data Association) [4][5][6], thuật toán kết hợp dữ liệu đa giả thiết (MHT: Multiple Hypothesis Tracking) [7][8][9][10], thuật toán kết hợp dữ liệu xác suất đồng thời gần nhất (NNJPDA: Nearest Neighbor Joint Probabilistic Data Association) [11,12]. Các thuật toán này rất hiệu quả, đã và đang được sử dụng trong thực tế.…”