IMMJPDA versus MHT and Kalman filter with NN correlation: performance comparison

Feo, Marco de; Graziano, A.; Miglioli, R.; Farina, A.

doi:10.1049/ip-rsn:19970976

Cited by 59 publications

(13 citation statements)

References 2 publications

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“…Many researchers proposed filtering algorithm, such as α−β filter, α−β−γ filter, least square algorithm, extended Kalman filter, and particle filter (Yoo and Kim, 2003;He et al, 2015;De Feo et al, 1997). These algorithms are only practical in the open sea, because assumptions on the status of real moving vessels have to be made.…”

Section: Discussionmentioning

confidence: 99%

A Recognition Approach of Radar Blips Based on Improved Fuzzy C Means

Liu

2017

EURASIA J MATH SCI T

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Maritime radar is the kernel sensor for tracking vessels in Vessel Traffic Service system, it is important for Maritime Situation Awareness. However, the images collected by the maritime radar are inundated with excessive noise blips, which bring variety of troubles in extraction of ship targets from the images. This paper proposes a radar target recognition method based on fuzzy C-means. First, the attributes of the blips in the sequential radar images, such as speed, direction and size, are quantified as three pieces of evidence to determine whether a radar blip is a moving vessel. Then, an artificial intelligence was built based on FCM. According to the three pieces of evidence, the possibility of a blip being a real vessel is computed with FCM. The main difficulty in building the FCM framework is to find an appropriate way to provide the classification coefficient C and the fuzzy coefficient m. Since the C in classification is finite, this study proposes a method to obtain C by assessing the Euclidean distance of the expected result. Since m is related to the discreteness of evidence and results, the coefficient can be assessed by Shannon entropy and gain. Field experiments suggest that the improved FCM is capable of classifying the radar blips accurately, and reducing the operational strength of the ship operators and improving the safety.

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Section: Discussionmentioning

confidence: 99%

A Recognition Approach of Radar Blips Based on Improved Fuzzy C Means

Liu

2017

EURASIA J MATH SCI T

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“…Other methods have various shortcomings. For example, the low track maintenance of the nearest neighbor association method, and the typical track switching behavior of the Joint Probabilistic Data Association method are not acceptable, see [19].…”

Section: Discussionmentioning

confidence: 99%

Parameter estimation of human nerve C-fibers using matched filtering and multiple hypothesis tracking

Hammarberg

Forster

Torebjörk

2002

IEEE Trans. Biomed. Eng.

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We describe how multiple-target tracking may be used to estimate conduction velocity changes and recovery constants of human nerve C-fibers. These parameters discriminate different types of C-fibers and pursuing this may promote new insights into differential properties of nerve fiber membranes. Action potentials (APs) were recorded from C-fibers in the peroneal nerve of awake human subjects. The APs were detected by a matched filter constituting a maximum-likelihood constant false-alarm rate detector. Using the multiple-hypothesis tracking method and Kalman filtering, the detected APs (targets) in each trace (scan) were associated to individual nerve fibers (tracks) by their typical conduction latencies in response to electrical stimulation. The measurements were one-dimensional (range only) and the APs were spaced in time with intersecting trajectories. In general, the AP amplitude of each C-fiber differed for different fibers. Amplitude estimation was therefore incorporated into the tracking algorithm to improve the performance. The target trajectory was modeled as an exponential decay with three unknowns. These parameters were estimated iteratively by applying the simplex method on the parameters that enter nonlinearly and the least squares method on the parameters that enter linearly.

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“…He et al (2015) designed the IMM-UKF-UPF algorithm which combines the Unscented Kalman Filter (UKF) algorithm with the Unscented Particle Filter (UPF) algorithm to reduce the influence of flicker noises for ground-based radar. De Feo et al (1997) compared MHT and IMM-JPDA with Kalman filtering in radar tracking. However, assumptions on the status of objects and noises have to be made in all the filtering algorithms.…”

Section: Filtering and Classification Methods On Radar Objectsmentioning

confidence: 99%

A novel marine radar targets extraction approach based on sequential images and Bayesian Network

Chen

Yan

et al. 2016

Ocean Engineering

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This research proposes a Bayesian Network-based methodology to extract moving vessels from a plethora of blips captured in frame-by-frame radar images. First of all, the inter-frame differences or graph characteristics of blips, such as velocity, direction, and shape, are quantified and selected as nodes to construct a Directed Acyclic Graph (DAG), which is used for reasoning the probability of a blip being a moving vessel. Particularly, an unequal-distance discretisation method is proposed to reduce the intervals of a blip's characteristics for avoiding the combinatorial explosion problem. Then, the undetermined DAG structure and parameters are learned from manually verified data samples. Finally, based on the probabilities reasoned by the DAG, judgments on blips being moving vessels are determined by an appropriate threshold on a Receiver Operating Characteristic (ROC) curve. The unique strength of the proposed methodology includes laying the foundation of targets extraction on original radar images and verified records without making any unrealistic assumptions on objects' states. A real case study has been conducted to validate the effectiveness and accuracy of the proposed methodology.

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IMMJPDA versus MHT and Kalman filter with NN correlation: performance comparison

Cited by 59 publications

References 2 publications

A Recognition Approach of Radar Blips Based on Improved Fuzzy C Means

A Recognition Approach of Radar Blips Based on Improved Fuzzy C Means

Parameter estimation of human nerve C-fibers using matched filtering and multiple hypothesis tracking

A novel marine radar targets extraction approach based on sequential images and Bayesian Network

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