Design of Insect Target Tracking Algorithm in Clutter Based on the Multidimensional Feature Fusion Strategy

Entomological radar is an effective means of monitoring insect migration, and can realize long-distance and large-scale rapid monitoring. The stable tracking of individual insect targets is the basic premise underlying the identification of insect species and the study of insect migration mechanisms. However, the complex motion trajectory and large number of false measurements decrease the performance of insect target tracking. In this paper, an insect target tracking algorithm in clutter was designed based on the multidimensional feature fusion strategy (ITT-MFF). Firstly, multiple feature parameters of measurements were fused to calculate the membership of measurements and target, thereby improving the data association accuracy in the presence of clutter. Secondly, a distance-correction factor was introduced to the probabilistic data association (PDA) algorithm to accomplish multi-target data association with a low computational cost. Finally, simulation scenarios with different target numbers and clutter densities were constructed to verify the effectiveness of the proposed method. The tracking result comparisons of the experimental data acquired from a Ku-band entomological radar also indicate that the proposed method can effectively reduce computational cost while maintaining high tracking precision, and is suitable for engineering implementation.

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Infrared Target Tracking Algorithm Based on Multiple Feature Fusion and Region of Interest Prediction

ACTA PHOTONICA SINICA ◽

10.3788/gzxb20194807.0710004 ◽

2019 ◽

Vol 48 (7) ◽

pp. 710004 ◽

Cited By ~ 1

Author(s):

刘辉 LIU Hui ◽

何勇 HE Yong ◽

何博侠 HE Bo-xia ◽

刘志 LIU Zhi ◽

顾士晨 GU Shi-chen

Keyword(s):

Target Tracking ◽

Feature Fusion ◽

Region Of Interest ◽

Tracking Algorithm ◽

Multiple Feature ◽

Multiple Feature Fusion ◽

Interest Prediction

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

Mathematical Problems in Engineering ◽

10.1155/2020/3749759 ◽

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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Sensor Selection for Decentralized Large-Scale Multi-Target Tracking Network

Sensors ◽

10.3390/s18124115 ◽

2018 ◽

Vol 18 (12) ◽

pp. 4115 ◽

Cited By ~ 3

Author(s):

Feng Lian ◽

Liming Hou ◽

Bo Wei ◽

Chongzhao Han

Keyword(s):

Target Tracking ◽

Large Scale ◽

Sequential Monte Carlo ◽

Signal To Noise Ratio ◽

Computational Cost ◽

Gaussian Mixture ◽

Descent Method ◽

Sensor Selection ◽

Multi Target Tracking ◽

Finite Set

A new optimization algorithm of sensor selection is proposed in this paper for decentralized large-scale multi-target tracking (MTT) network within a labeled random finite set (RFS) framework. The method is performed based on a marginalized δ-generalized labeled multi-Bernoulli RFS. The rule of weighted Kullback-Leibler average (KLA) is used to fuse local multi-target densities. A new metric, named as the label assignment (LA) metric, is proposed to measure the distance for two labeled sets. The lower bound of LA metric based mean square error between the labeled multi-target state set and its estimate is taken as the optimized objective function of sensor selection. The proposed bound is obtained by the information inequality to RFS measurement. Then, we present the sequential Monte Carlo and Gaussian mixture implementations for the bound. Another advantage of the bound is that it provides a basis for setting the weights of KLA. The coordinate descent method is proposed to compromise the computational cost of sensor selection and the accuracy of MTT. Simulations verify the effectiveness of our method under different signal-to- noise ratio scenarios.

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Linear Multi-Target Integrated Probabilistic Data Association Filter With Automatic Track Management for Autonomous Vehicles

Volume 2: Control and Optimization of Connected and Automated Ground Vehicles; Dynamic Systems and Control Education; Dynamics and Control of Renewable Energy Systems; Energy Harvesting; Energy Systems; Estimation and Identification; Intelligent Transportation and Vehicles; Manufacturing; Mechatronics; Modeling and Control of IC Engines and Aftertreatment Systems; Modeling and Control of IC Engines and Powertrain Systems; Modeling and Management of Power Systems ◽

10.1115/dscc2018-8930 ◽

2018 ◽

Author(s):

Andinet Hunde ◽

Beshah Ayalew

Keyword(s):

Target Tracking ◽

Tracking System ◽

Data Association ◽

Tracking Algorithm ◽

Probabilistic Data ◽

Probabilistic Data Association ◽

Address Data ◽

Multi Target Tracking ◽

Track Management ◽

Track Initiation

Target tracking in public traffic calls for a tracking system with automated track initiation and termination facilities in a randomly evolving driving environment. In addition, the key problem of data association needs to be handled effectively considering the limitations in the computational resources onboard an autonomous car. In this paper, we discuss a multi-target tracking system that addresses target birth/initiation and death/termination processes with automatic track management feature. The tracking system is based on Linear Multi-target Integrated Probabilistic Data Association Filter (LMIPDAF), which is adapted to specifically include algorithms that handle track initiation and termination, clutter density estimation and track management. The performance of the proposed tracking algorithm is compared to other single and multi-target tracking schemes and is shown to have acceptable tracking error. It is further illustrated through multiple traffic simulations that the computational requirement of the tracking algorithm is less than that of optimal multi-target tracking algorithms that explicitly address data association uncertainties.

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