Gaussian Sum FIR Filtering for 2D Target Tracking

Pak, Jung Min

doi:10.1007/s12555-018-0938-4

Cited by 7 publications

(4 citation statements)

References 30 publications

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“…Tracking is performed by calculating the distances of all tracking targets between each frame and joining the neighbours with the least distance between frames. In addition, a Gaussian sum finite impulse response filter using a constant velocity model is used to filter the measured signal as well as to produce predictions of positions for tracking [8].…”

Section: The Tracking Processmentioning

confidence: 99%

YSMR: a video tracking and analysis program for bacterial motility

et al. 2020

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Background Motility in bacteria forms the basis for taxis and is in some pathogenic bacteria important for virulence. Video tracking of motile bacteria allows the monitoring of bacterial swimming behaviour and taxis on the level of individual cells, which is a prerequisite to study the underlying molecular mechanisms. Results The open-source python program YSMR (Your Software for Motility Recognition) was designed to simultaneously track a large number of bacterial cells on standard computers from video files in various formats. In order to cope with the high number of tracked objects, we use a simple detection and tracking approach based on grey-value and position, followed by stringent selection against suspicious data points. The generated data can be used for statistical analyses either directly with YSMR or with external programs. Conclusion In contrast to existing video tracking software, which either requires expensive computer hardware or only tracks a limited number of bacteria for a few seconds, YSMR is an open-source program which allows the 2-D tracking of several hundred objects over at least 5 minutes on standard computer hardware. The code is freely available at https://github.com/schwanbeck/YSMR

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Section: The Tracking Processmentioning

confidence: 99%

YSMR: a video tracking and analysis program for bacterial motility

et al. 2020

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“…In addition, one of the assumptions is that range measurements with respect to the target are available, which is very difficult to obtain for non-cooperative targets. In [44], a Gaussian sum FIR filter (GSFF) to estimate the position of a target is presented. In this work, both estimation and tracking of the target are carried out only in a two-dimensional space.…”

Section: Objectives and Contributionsmentioning

confidence: 99%

Cooperative Visual-SLAM System for UAV-Based Target Tracking in GPS-Denied Environments: A Target-Centric Approach

et al. 2020

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Autonomous tracking of dynamic targets by the use of Unmanned Aerial Vehicles (UAVs) is a challenging problem that has practical applications in many scenarios. In this context, a fundamental aspect that must be addressed has to do with the position estimation of aerial robots and a target to control the flight formation. For non-cooperative targets, their position must be estimated using the on-board sensors. Moreover, for estimating the position of UAVs, global position information may not always be available (GPS-denied environments). This work presents a cooperative visual-based SLAM (Simultaneous Localization and Mapping) system that allows a team of aerial robots to autonomously follow a non-cooperative target moving freely in a GPS-denied environment. One of the contributions of this work is to propose and investigate the use of a target-centric SLAM configuration to solve the estimation problem that differs from the well-known World-centric and Robot-centric SLAM configurations. In this sense, the proposed approach is supported by theoretical results obtained from an extensive nonlinear observability analysis. Additionally, a control system is proposed for maintaining a stable UAV flight formation with respect to the target as well. In this case, the stability of control laws is proved using the Lyapunov theory. Employing an extensive set of computer simulations, the proposed system demonstrated potentially to outperform other related approaches.

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“…When PF algorithm fails under the harsh conditions mentioned above, the assisting FIR filter operates to recover the main filter from failures. The FIR filter [18][19][20][21][22][23][24][25][26][27] is generally less accurate than the PF in nonlinear state estimation problems; however, it has intrinsic robustness against model uncertainty and bounded-input bounded-output (BIBO) stability. Thus, the FIR filter is appropriate for the role of the assisting filter that operates under harsh conditions.…”

Section: Introductionmentioning

confidence: 99%

“…In the CV model, the process noise covariance Q plays a critical role; however, it is a very uncertain design parameter [28]. Thus, inappropriately selected Q values may worsen localization accuracy [20,26,27]. In cases where state-space models have uncertainties, multiplemodel approaches have been commonly used [16,17,28].…”

Section: Introductionmentioning

confidence: 99%

Multiple-model hybrid particle/FIR filter for indoor localization using wireless sensor networks

Pak

2020

IEICE Electron. Express

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This letter proposes a new state estimator called the multiplemodel hybrid particle/finite-impulse-response (FIR) filter (MMHPFF) for indoor localization using wireless sensor networks. In the proposed hybrid filtering algorithm, the multiple-model particle filter has the role of the main filter, and it overcomes uncertain process noise problems arising from the use of the constant velocity (CV) motion model in indoor localization. In addition, the multiple-model FIR filter is used as an assisting filter to overcome particle filter failures owing to the sample impoverishment phenomenon. Indoor localization simulations demonstrated that the proposed MMHPFF is more accurate and reliable than conventional algorithms.

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Gaussian Sum FIR Filtering for 2D Target Tracking

Cited by 7 publications

References 30 publications

YSMR: a video tracking and analysis program for bacterial motility

YSMR: a video tracking and analysis program for bacterial motility

Cooperative Visual-SLAM System for UAV-Based Target Tracking in GPS-Denied Environments: A Target-Centric Approach

Multiple-model hybrid particle/FIR filter for indoor localization using wireless sensor networks

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