Multi-sensor multi-target tracking using out-of-sequence measurements

Mallick, Mahendra; Krant, Jon; Bar‐Shalom, Yaakov

doi:10.1109/icif.2002.1021142

Cited by 39 publications

(28 citation statements)

References 15 publications

(14 reference statements)

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“…OOSM has been considered using interacting multiple model (IMM) [6,7,8]. In this paper we do not consider OOSM but, instead, consider "in-sequence" measurements with a fixed but unknown relative time-delay among sensor measurements.…”

Section: Distribution Statement a ^Nn?na?a A (\^mentioning

confidence: 99%

Multisensor Tracking of a Maneuvering Target in Clutter with Asychronous Measurements using IMMPDA Filtering and Parallel Detection Fusion

Jeong¹,

Tugnait²

2003

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We present a (suboptimal) filtering algorithm for tracking a highly maneuvering target in a cluttered environment using multiple sensors dealing with possibly asynchronous (time delayed) measurements. The filtering algorithm is developed by applying the basic Interacting Multiple Model (IMM) approach, the Probabilistic Data Association (PDA) technique, and asynchronous measurement updating for state-augmented system estimation for the target. A state augmented approach is developed to estimate the time delay between local and remote sensors. A multisensor probabiHstic data association filter is developed for parallel sensor processing for target tracking under clutter. The algorithm is illustrated via a highly maneuvering target tracking simulation example where two sensors, a radar and an infrared sensor, are used. Compared with an existing IMMPDA filtering algorithm with the assumption of synchronous (no delay) measurements sensor processing, the proposed algorithm achieves considerable improvement (especially in the case of larger delays) in the accuracy of track estimation.

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Section: Distribution Statement a ^Nn?na?a A (\^mentioning

confidence: 99%

Multisensor Tracking of a Maneuvering Target in Clutter with Asychronous Measurements using IMMPDA Filtering and Parallel Detection Fusion

Jeong¹,

Tugnait²

2003

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“…A globally optimal state trajectory update algorithm for a sequence with arbitrary delayed OOSMs including the case of interlaced OOSMs with less storages is given in [279]. Other publications classified with distributed OOSM are [61], [62], [63], [64], [81], [82], [83], [84], [85], [86], [87], [88], [89], [90], [141], [163], [165], [166], [167], [168], [188], [195], [207], [208], [224], [225], [225]- [229], [230], [231], [280], [301] and [302]. …”

Section: Distributed Oosmmentioning

confidence: 99%

Distributed Kalman Filtering

Khalid¹

2014

Networked Filtering and Fusion in Wireless Sensor Networks

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“…In [42], Mallick et al present a multi-lag, single-model algorithm that includes data association, likelihood computation and hypothesis management, and a particle filter for out-of-sequencemeasurement treatment in [41].…”

Section: Buff Approachmentioning

confidence: 99%

State-of-the-art Versus Time-triggered Object Tracking in Advanced Driver Assistance Systems

Koplin

Elmenreich

2013

International Journal of Advanced Robotic Systems

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Most state-of-the-art driver assistance systems cannot guarantee that real-time images of object states are updated within a given time interval, because the object state observations are typically sampled by uncontrolled sensors and transmitted via an indeterministic bus system such as CAN. To overcome this shortcoming, a paradigm shift toward time-triggered advanced driver assistance systems based on a deterministic bus system, such as FlexRay, is under discussion.In order to prove the feasibility of this paradigm shift, this paper develops different models of a state-of-the-art and a time-triggered advanced driver assistance system based on multi-sensor object tracking and compares them with regard to their mean performance. The results show that while the state-of-the-art model is advantageous in scenarios with low process noise, it is outmatched by the time-triggered model in the case of high process noise, i.e., in complex situations with high dynamic.

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Multi-sensor multi-target tracking using out-of-sequence measurements

Cited by 39 publications

References 15 publications

Multisensor Tracking of a Maneuvering Target in Clutter with Asychronous Measurements using IMMPDA Filtering and Parallel Detection Fusion

Multisensor Tracking of a Maneuvering Target in Clutter with Asychronous Measurements using IMMPDA Filtering and Parallel Detection Fusion

Distributed Kalman Filtering

State-of-the-art Versus Time-triggered Object Tracking in Advanced Driver Assistance Systems

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