New Method of Moving Targets Passive Tracking by Single Moving Observer Based on Measurement Data Fusion

2012 ◽  
Vol 239-240 ◽  
pp. 942-945
Author(s):  
Jie Gui Wang
Keyword(s):  
Data Fusion ◽  
Computer Simulations ◽  
Data Association ◽  
Measurement Data ◽  
Synthetic Data ◽  
Difficult Problem ◽  
Moving Targets ◽  
Passive Tracking ◽  
Multiple Measurements ◽  
New Location

Moving targets passive tracking by single moving observer is a difficult problem. A new location method based on measurement data fusion is proposed in this paper. Firstly, the adaptive passive tracking initiation algorithm is introduced. Secondly, a new data association algorithm is proposed, based on the data fusion of multiple measurements, the decision of synthetic data association is made. Finally, with the help of computer simulations, the proposed algorithms are proven to be correct and effective.

scholarly journals A Separation Method of Superimposed Gratings in Double-Projector Fringe Projection Profilometry Using a Color Camera

2021 ◽  
Vol 11 (3) ◽  
pp. 890
Author(s):  
Yuanjun Zhang ◽  
Xinghua Qu ◽  
Yiming Li ◽  
Fumin Zhang
Keyword(s):  
Measurement Data ◽  
Difficult Problem ◽  
Fringe Projection ◽  
Field Range ◽  
Light Spectra ◽  
Grating Pattern ◽  
Measuring Efficiency ◽  
Multiple Measurements ◽  
Fringe Patterns ◽  
Fringe Projection Profilometry

Fringe projection profilometry has been intensively studied for several decades. However, due to the limitation of the field range of a single projector, when measuring objects with complex surfaces, there are always shadow areas in the captured images, resulting in missing measurement data in the dark areas. To solve this problem, systems with double projectors and single camera were employed. Not only were the shadow areas reduced, but system recalibration and multiple measurements were not needed, improving measuring efficiency. Nevertheless, separating the corresponding projection pattern from the superimposed fringe presented a difficult problem. A color camera has RGB three color channels. When the color camera is applied to fringe projection profilometry, the information obtained is three times as much as that of the monochrome camera. Due to the small overlap between the red- and blue-light spectra response of color cameras, the channel color crosstalk can be ignored. This paper proposes a method to project red and blue fringe patterns from two projectors and utilize the characteristics of the red and blue channels of the color camera to separate the superposition grating pattern. The original patterns can be recovered integrally and easily. To explain the effectiveness of superimposed fringe separation, a simulation and experiments were carried out. Both of them showed that the superimposed fringe can be separated correctly, proving that our method is feasible.

scholarly journals Stereo and ToF data fusion by learning from synthetic data

2019 ◽  
Vol 49 ◽  
pp. 161-173 ◽  
Author(s):  
Gianluca Agresti ◽  
Ludovico Minto ◽  
Giulio Marin ◽  
Pietro Zanuttigh
Keyword(s):  
Data Fusion ◽  
Synthetic Data

scholarly journals Passive Sonar Multiple-Target Tracking with Nonlinear Doppler and Bearing Measurements Using Multiple Sensors

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xiaohua Li ◽  
Bo Lu ◽  
Wasiq Ali ◽  
Jun Su ◽  
Haiyan Jin
Keyword(s):  
Target Tracking ◽  
Tracking System ◽  
Data Association ◽  
Measurement Data ◽  
Target Range ◽  
Multiple Target Tracking ◽  
Multiple Target ◽  
Multiple Sensors ◽  
Passive Sensors ◽  
Passive Measurements

The major advantage of the passive multiple-target tracking is that the sonars do not emit signals and thus they can remain covert, which will reduce the risk of being attacked. However, the nonlinearity of the passive Doppler and bearing measurements, the range unobservability problem, and the measurement to target data association uncertainty make the passive multiple-target tracking problem challenging. To deal with the target to measurement data association uncertainty problem from multiple sensors, this paper proposed a batch recursive extended Rauch-Tung-Striebel smoother- (RTSS-) based probabilistic multiple hypothesis tracker (PMHT) algorithm, which can effectively handle a large number of passive measurements including clutters. The recursive extended RTSS which consists of a forward filter and a backward smoothing is used to deal with the nonlinear Doppler and bearing measurements. The target range unobservability problem is avoided due to using multiple passive sensors. The simulation results show that the proposed algorithm works well in a passive multiple-target tracking system under dense clutter environment, and its computing cost is low.

scholarly journals Research on Target Tracking Algorithm Using Millimeter-Wave Radar on Curved Road

2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Shiping Song ◽  
Jian Wu ◽  
Sumin Zhang ◽  
Yunhang Liu ◽  
Shun Yang
Keyword(s):  
Target Tracking ◽  
Millimeter Wave ◽  
Data Association ◽  
Measurement Data ◽  
Tracking Algorithm ◽  
Computational Time ◽  
Millimeter Wave Radar ◽  
Wave Radar ◽  
Curved Road ◽  
Lane Position

Millimeter-wave radar has been widely used in intelligent vehicle target detection. However, there are three difficulties in radar-based target tracking in curves. First, there are massive data association calculations with poor accuracy. Second, the lane position relationship of target-vehicle cannot be identified accurately. Third, the target tracking algorithm has poor robustness and accuracy. A target tracking algorithm framework on curved road is proposed herein. The following four algorithms are applied to reduce data association calculations and improve accuracy. (1) The data rationality judgment method is employed to eliminate target measurement data outside the radar detection range. (2) Effective target life cycle rules are used to eliminate false targets and clutter. (3) Manhattan distance clustering algorithm is used to cluster multiple data into one. (4) The correspondence between the measurement data received by the radar and the target source is identified by the nearest neighbor (NN) data association. The following three algorithms aim to derive the position relationship between the ego-vehicle and the target-vehicles. (1) The lateral speed is obtained by estimating the state of motion of the ego-vehicle. (2) An algorithm for state compensation of target motion is presented by considering the yaw motion of the ego-vehicle. (3) A target lane relationship recognition model is built. The improved adaptive extended Kalman filter (IAEKF) is used to improve the target tracking robustness and accuracy. Finally, the vehicle test verifies that the algorithms proposed herein can accurately identify the lane position relationship. Experiments show that the framework has higher target tracking accuracy and lower computational time.

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