Track-to-Track Association for Biased Data Based on the Reference Topology Feature

Tian, Wei; Wang, Yue; Shan, Xiuming; Yang, Jian

doi:10.1109/lsp.2014.2305305

Cited by 45 publications

(23 citation statements)

References 17 publications

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“…The proposed LTGP algorithm is used for T2TASB, and the results at time step k = 50 are given in Figure 5, which illustrates that the local tracks from the two sensors are associated correctly by the proposed LTGP method. The performance of the proposed method is demonstrated next relative to those of GNN without registration, the reference pattern-based algorithm [33], and the CPD algorithm [35]. All results are averaged over 50 Monte Carlo runs.…”

Section: Computer Simulationsmentioning

confidence: 99%

“…The online approach is a real-time implementation to perform track association with sensor bias. In [33], relative position information among neighboring tracks is analyzed, and a reference topology feature is derived for the absolute position information. An optimal sub-pattern assignment (OSPA) metric is also proposed to construct the association cost for T2TASB.…”

Section: Introductionmentioning

confidence: 99%

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Track-to-Track Association for Intelligent Vehicles by Preserving Local Track Geometry

Zhu

Freitas

et al. 2020

Sensors

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Track-to-track association (T2TA) is a challenging task in situational awareness in intelligent vehicles and surveillance systems. In this paper, the problem of track-to-track association with sensor bias (T2TASB) is considered. Traditional T2TASB algorithms only consider a statistical distance cost between local tracks from different sensors, without exploiting the geometric relationship between one track and its neighboring ones from each sensor. However, the relative geometry among neighboring local tracks is usually stable, at least for a while, and thus helpful in improving the T2TASB. In this paper, we propose a probabilistic method, called the local track geometry preservation (LTGP) algorithm, which takes advantage of the geometry of tracks. Assuming that the local tracks of one sensor are represented by Gaussian mixture model (GMM) centroids, the corresponding local tracks of the other sensor are fitted to those of the first sensor. In this regard, a geometrical descriptor connectivity matrix is constructed to exploit the relative geometry of these tracks. The track association problem is formulated as a maximum likelihood estimation problem with a local track geometry constraint, and an expectation-maximization (EM) algorithm is developed to find the solution. Simulation results demonstrate that the proposed methods offer better performance than the state-of-the-art methods.system in intelligent vehicles [7][8][9]. A multi-sensor multi-target tracking module jointly estimates the states and the number of targets from sensor measurements in intelligent vehicles, and it can be broadly categorized as centralized or distributed. The advantage of the distributed tracking systems is that they can provide a degree of scalability and robustness not achievable by traditional centralized tracking systems [1].Track-to-track association (T2TA) is a crucial task in distributed tracking to find the correspondence between local tracks from different sensors. It is commonly applied to combine the local tracks of a sensor with those of another sensor to form the global tracklist. For automotive applications, radar, lidar, and video sensors in environmental perception systems for intelligent vehicles use different coordinate systems and sampling frequencies. Therefore, a spatio-temporal calibration should be performed to align the detections from different sensors [1]. In practice, detection from radar, lidar, and video sensors cannot always be calibrated or aligned accurately [10]. Each sensor may cover a different part of the surveillance region with a detection probability of less than one. As a result, some local tracks from a sensor may not correspond to those of other sensors. The range, azimuth, and elevation biases of a radar sensor may lead to errors in the local tracks from that sensor. The relationship between radar sensor bias and local tracks is presented in Figure 1, where two radar sensors, A and B, and one target, T, are shown. The radar sensor bias leads to the reporting of the target T as tracks T A and...

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Section: Computer Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Track-to-Track Association for Intelligent Vehicles by Preserving Local Track Geometry

Zhu

Freitas

et al. 2020

Sensors

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“…Thus, some TTTA methods for radar systems based on angular and distance measurements become feasible. Based on small sensor biases, Tian [26] presented an optimal sub-pattern assignment (OSPA) metric to measure the distance between two reference topologies (RET). Previous studies [27,28] extended the work by taking missing detection and kinematic parameters into consideration.…”

Section: Introductionmentioning

confidence: 99%

A Sequential Two-Stage Track-to-Track Association Method in Asynchronous Bearings-Only Sensor Networks for Aerial Targets Surveillance

Yang

Hou

Zhang

et al. 2019

Sensors

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Successful track-to-track association (TTTA) in a multisensor and multitarget scenario is predicated on a reasonable likelihood function to evaluate the similarity of asynchronous mono tracks. To deal with the lack of synchronous data and prior knowledge of the targets in practical applications, this paper investigates a global optimization method with a novel likelihood function constructed by finite asynchronous measurements with joint temporal and spatial constraints (JTSC). For a scenario with more than two independent sensors, a sequential two-stage strategy is proposed to calculate the similarity of multiple asynchronous mono tracks. For the first stage, based on the temporal features of measurements from different sensors, a pairwise fusion model to estimate the position of the target with two mono tracks is established based on the asynchronous crossing location approach. For the other stage, to evaluate the similarity of the outputs, a pairwise similarity model is constructed by searching for the optimal matching points by setting temporal and spatial constraints. Thus, the likelihood of multiple asynchronous tracks is obtained. Simulations are performed to verify that the proposed method can achieve favorable performance without data-synchronization, and also demonstrate the superiority over the methods based on hinge angle differences (HADs) in some scenarios.

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“…To solve the problem of track‐to‐track association under sensor biases, algorithms based on the reference topology (RET) feature have been proposed [8–11]. In these algorithms, assumption is made that in the global Cartesian coordinate system, sensor biases cause uniform position deviations among different targets, therefore, sensor biases do not change the relative position between targets.…”

Section: Introductionmentioning

confidence: 99%

Anti‐bias track‐to‐track association algorithm based on distance detection

Dong

Liu

et al. 2017

IET Radar, Sonar & Navigation

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Track-to-Track Association for Biased Data Based on the Reference Topology Feature

Cited by 45 publications

References 17 publications

Track-to-Track Association for Intelligent Vehicles by Preserving Local Track Geometry

Track-to-Track Association for Intelligent Vehicles by Preserving Local Track Geometry

A Sequential Two-Stage Track-to-Track Association Method in Asynchronous Bearings-Only Sensor Networks for Aerial Targets Surveillance

Anti‐bias track‐to‐track association algorithm based on distance detection

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