Multiple model particle filter track-before-detect for range ambiguous radar

Tan, Shuncheng; Guan, Chun; Wang, Na; Liu, Zhaolei

doi:10.1016/j.cja.2013.07.033

Cited by 24 publications

(17 citation statements)

References 18 publications

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“…To fit our use-case (detection and tracking of a pedestrian) a filter should not only track an object, but also report a detection confidence that there is an object to track. [23], [24], [25], [26] give solutions to include this existence probability into Particle Filters.…”

Section: Related Workmentioning

confidence: 99%

Occlusion aware sensor fusion for early crossing pedestrian detection

Palffy

Kooij

Gavrila

2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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Early and accurate detection of crossing pedestrians is crucial in automated driving to execute emergency manoeuvres in time. This is a challenging task in urban scenarios however, where people are often occluded (not visible) behind objects, e.g. other parked vehicles. In this paper, an occlusion aware multi-modal sensor fusion system is proposed to address scenarios with crossing pedestrians behind parked vehicles. Our proposed method adjusts the detection rate in different areas based on sensor visibility. We argue that using this occlusion information can help to evaluate the measurements. Our experiments on real world data show that fusing radar and stereo camera for such tasks is beneficial, and that including occlusion into the model helps to detect pedestrians earlier and more accurately.

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Section: Related Workmentioning

confidence: 99%

Occlusion aware sensor fusion for early crossing pedestrian detection

Palffy

Kooij

Gavrila

2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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“…Synthetic Aperture Radar (SAR) has been widely used due to its high resolution and penetrating ability [1][2][3]. SAR image automatic target recognition technology (SAR-ATR) is one of the research hotspots in the field of image cognitive learning [4,5].…”

Section: Introductionmentioning

confidence: 99%

A Novel Semi-Supervised Convolutional Neural Network Method for Synthetic Aperture Radar Image Recognition

et al. 2019

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Background / introduction: SAR image automatic target recognition technology (SAR-ATR) is one of the research hotspots in the field of image cognitive learning. Inspired by the human cognitive process, experts have designed convolutional neural networks (CNN) based methods and successfully applied the methods to SAR-ATR. However, the performance of CNNs significantly deteriorates when the labelled samples are insufficient. Methods: To effectively utilize the unlabelled samples, a semi-supervised CNN method is proposed in this paper. First, CNN is used to extract the features of the samples, and subsequently the class probabilities of the unlabelled samples are computed using the softmax function. To improve the effectiveness of the unlabelled samples, we remove possible noise performing thresholding on the class probabilities. Afterwards, based on the remaining class probabilities, the information contained in the unlabelled samples is integrated with the scatter matrices of the standard linear discriminant analysis (LDA) method. The loss function of CNN consists of a supervised component and an unsupervised component, where the supervised component is created using the cross-entropy function and the unsupervised component is created using the scatter matrices. The class probabilities are utilized to control the impact of the unlabelled samples in the training process, and the reliability of the unlabelled samples is further improved. Results: We choose ten types of targets from the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. The experimental results show that the recognition accuracy of 2 our method is significantly higher than that of the supervised CNN method. Conclusions: It proves that our method can effectively improve the SAR-ATR accuracy despite the deficiency of the labelled samples.

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“…However, unlike those existing semi-supervised learning methods, we do not use unlabeled samples directly and, instead, look for safe and reliable unlabeled samples and then use these unlabeled samples to enhance the performance of the recognition algorithm. This is because the unlabeled radar 2 Complexity images need to go through the detection stage in the process of acquisition [9,10]. These samples may deteriorate the semi-supervised algorithms' learning, especially when the number of the labeled samples and that of the unlabeled samples are somehow unbalanced.…”

Section: Introductionmentioning

confidence: 99%

A Novel Semi‐Supervised Learning Method Based on Fast Search and Density Peaks

et al. 2019

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Radar image recognition is a hotspot in the field of remote sensing. Under the condition of sufficiently labeled samples, recognition algorithms can achieve good classification results. However, labeled samples are scarce and costly to obtain. Our major interest in this paper is how to use these unlabeled samples to improve the performance of a recognition algorithm in the case of limited labeled samples. This is a semi-supervised learning problem. However, unlike the existing semi-supervised learning methods, we do not use unlabeled samples directly and, instead, look for safe and reliable unlabeled samples before using them. In this paper, two new semi-supervised learning methods are proposed: a semi-supervised learning method based on fast search and density peaks (S2DP) and an iterative S2DP method (IS2DP). When the labeled samples satisfy a certain requirement, S2DP uses fast search and a density peak clustering method to detect reliable unlabeled samples based on the weighted kernel Fisher discriminant analysis (WKFDA). Then, a labeling method based on clustering information (LCI) is designed to label the unlabeled samples. When the labeled samples are insufficient, IS2DP is used to iteratively search for reliable unlabeled samples for semi-supervision. Then, these samples are added to the labeled samples to improve the recognition performance of S2DP. In the experiments, real radar images are used to verify the performance of our proposed algorithm in dealing with the scarcity of the labeled samples. In addition, our algorithm is compared against several semi-supervised deep learning methods with similar structures. Experimental results demonstrate that the proposed algorithm has better stability than these methods.

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Multiple model particle filter track-before-detect for range ambiguous radar

Cited by 24 publications

References 18 publications

Occlusion aware sensor fusion for early crossing pedestrian detection

Occlusion aware sensor fusion for early crossing pedestrian detection

A Novel Semi-Supervised Convolutional Neural Network Method for Synthetic Aperture Radar Image Recognition

A Novel Semi‐Supervised Learning Method Based on Fast Search and Density Peaks

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