Tracking Multiple Marine Ships via Multiple Sensors with Unknown Backgrounds

Do, Cong-Thanh; Nguyen, Tran Thien Dat; Liu, Weifeng

doi:10.3390/s19225025

Cited by 6 publications

(5 citation statements)

References 47 publications

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“…To assess the performance of the R-MS-LMB filter, we consider a challenging scenario over 200 time steps, as shown in Figure 8a. We compare the proposed R-MS-LMB filter with several other filters, including the robust multi-sensor GLMB (R-MS-GLMB) filter used in [42,43], the standard multi-sensor LMB (Std-LMB) filter with true detection probabilities for the R-MS-LMB filter, the MS-LMB filter with fixed high detection probabilities, named the High-MS-LMB filter, and the MS-LMB filter with fixed low detection probabilities, named the Low-MS-LMB filter. Additionally, the R-MS-GLMB filter samples 200 components.…”

Section: Second Scenariomentioning

confidence: 99%

“…Although these filters provide reliable estimates of current target states, they do not produce target tracks. Therefore, a multi-sensor MTT filter that bootstraps the detection probability estimated from the CPHD filter into the GLMB filter to achieve MTT has been proposed [42,43]. Nevertheless, the complexity of the above filters increases exponentially as the sensor number grows; this is a crucial consideration when designing real-world MTT systems.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Target Tracking in Multi-Static Networks with Autonomous Underwater Vehicles Using a Robust Multi-Sensor Labeled Multi-Bernoulli Filter

Zhang

et al. 2023

JMSE

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This paper proposes a centralized MTT method based on a state-of-the-art multi-sensor labeled multi-Bernoulli (LMB) filter in underwater multi-static networks with autonomous underwater vehicles (AUVs). The LMB filter can accurately extract the number of targets and trajectories from measurements affected by noise, missed detections, false alarms and port–starboard ambiguity. However, its complexity increases as the number of sensors increases. In addition, due to the time-varying underwater environment, AUV detection probabilities are time-varying, and their mismatches often lead to poor MTT performance. Consequently, we detail a robust multi-sensor LMB filter that estimates detection probabilities and multi-target states simultaneously in real time. Moreover, we derive an effective approximate form of the multi-sensor LMB filter using Kullback–Leibler divergence and develop an efficient belief propagation (BP) implementation of the multi-sensor LMB filter. Our method scales linearly with the number of AUVs, providing good scalability and low computational complexity. The proposed method demonstrates superior performance in underwater multi-AUV network MTT simulations.

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Section: Second Scenariomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Target Tracking in Multi-Static Networks with Autonomous Underwater Vehicles Using a Robust Multi-Sensor Labeled Multi-Bernoulli Filter

Zhang

et al. 2023

JMSE

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“…This work illustrates and compares their performance in detecting, initiating, and terminating tracks with clutter presence. Also related to the same type of application, Do et al [ 16 ] propose a method for online tracking multiple targets for a naval area using a Generalized Labeled Multi-Bernoulli (GLMB) filter.…”

Section: Related Workmentioning

confidence: 99%

“…This manuscript deals with the application of the GM PHD algorithm in a distributed sensor network, which can be: Radars or cameras installed along the coast or area of interest; radars on patrol or larger ships; and sensors installed in smaller boats. In such a system, unlike those presented in other applications [ 14 , 15 , 16 ], each node has its processing and tracking solution. Furthermore, through a communication link, they send their tracking data to a central station responsible for gathering and processing the information obtained by the distributed components.…”

Section: Introductionmentioning

confidence: 99%

Cooperative-PHD Tracking Based on Distributed Sensors for Naval Surveillance Area

Lima¹,

Costa²

2022

Sensors

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Brazil has an extensive coastline and Exclusive Economic Zone (EEZ) area, which are of high economic and strategic importance. A Maritime Surveillance System becomes necessary to provide information and data to proper authorities, and target tracking is crucial. This paper focuses on a multitarget tracking application to a large-scale maritime surveillance system. The system is composed of a sensor network distributed over an area of interest. Due to the limited computational capabilities of nodes, the sensors send their tracking data to a central station, which is responsible for gathering and processing information obtained by the distributed components. The local Multitarget Tracking (MTT) algorithm employs a random finite set approach, which adopts a Gaussian mixture Probability Hypothesis Density (PHD) filter. The proposed data fusion scheme, utilized in the central station, consists of an additional step of prune & merge to the original GM PHD filter algorithm, which is the main contribution of this work. Through simulations, this study illustrates the performance of the proposed algorithm with a network composed of two stationary sensors providing the data. This solution yields a better tracking performance when compared to individual trackers, which is attested by the Optimal Subpattern Assignment (OSPA) metric and its location and cardinality components. The presented results illustrate the overall performance improvement attained by the proposed solution. Moreover, they also stress the need to resort to a distributed sensor network to tackle real problems related to extended targets.

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“…Additionally, amongst the many approaches to multi-object tracking, the labeled RFS approach has demonstrated the capability for characterising confidence/uncertainty on the inferred results [16]. Furthermore, RFS-based filters can be formulated to handle tracking problems with unknown clutter rate and detection probability [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Biological Cell Tracking And Lineage Inference Via Random Finite Sets

Nguyen

Shim

Kim

2021

2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)

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Automatic cell tracking has long been a challenging problem due to the uncertainty of cell dynamic and observation process, where detection probability and clutter rate are unknown and time-varying. This is compounded when cell lineages are also to be inferred. In this paper, we propose a novel biological cell tracking method based on the Labeled Random Finite Set (RFS) approach to study cell migration patterns. Our method tracks cells with lineage by using a Generalised Label Multi-Bernoulli (GLMB) filter with objects spawning, and a robust Cardinalised Probability Hypothesis Density (CPHD) to address unknown and time-varying detection probability and clutter rate. The proposed method is capable of quantifying the certainty level of the tracking solutions. The capability of the algorithm on population dynamic inference is demonstrated on a migration sequence of breast cancer cells.

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Tracking Multiple Marine Ships via Multiple Sensors with Unknown Backgrounds

Cited by 6 publications

References 47 publications

Multi-Target Tracking in Multi-Static Networks with Autonomous Underwater Vehicles Using a Robust Multi-Sensor Labeled Multi-Bernoulli Filter

Multi-Target Tracking in Multi-Static Networks with Autonomous Underwater Vehicles Using a Robust Multi-Sensor Labeled Multi-Bernoulli Filter

Cooperative-PHD Tracking Based on Distributed Sensors for Naval Surveillance Area

Biological Cell Tracking And Lineage Inference Via Random Finite Sets

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