PLA-JPDA for Indoor Multi-Person Tracking Using IR-UWB Radars

Qian, Hongyu; Yang, Xiuzhu; Zhang, Xinyue; Ding, Yi; Zhang, Lin

doi:10.1109/radarconf2043947.2020.9266407

Cited by 10 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on the radar matrix

after preprocessing, the initial target location

is determined by identifying the maximum energy in the range dimension of the signal matrix. Subsequently, the location estimation is updated using the Kalman filter [ 18 ] based on the minimum mean square error (MMSE) approach. Firstly, the covariance matrix of the error

is determined from the following:

where Q is a constant that affects the weight of the predicted value.…”

Section: The Proposed Mlrt Methodsmentioning

confidence: 99%

Multi-Task Learning Radar Transformer (MLRT): A Personal Identification and Fall Detection Network Based on IR-UWB Radar

Jiang

Zhang

2023

Sensors

Self Cite

View full text Add to dashboard Cite

Radar-based personal identification and fall detection have received considerable attention in smart healthcare scenarios. Deep learning algorithms have been introduced to improve the performance of non-contact radar sensing applications. However, the original Transformer network is not suitable for multi-task radar-based applications to effectively extract temporal features from time-series radar signals. This article proposes the Multi-task Learning Radar Transformer (MLRT): a personal Identification and fall detection network based on IR-UWB radar. The proposed MLRT utilizes the attention mechanism of Transformer as its core to automatically extract features for personal identification and fall detection from radar time-series signals. Multi-task learning is applied to exploit the correlation between the personal identification task and the fall detection task, enhancing the performance of discrimination for both tasks. In order to suppress the impact of noise and interference, a signal processing approach is employed including DC removal and bandpass filtering, followed by clutter suppression using a RA method and Kalman filter-based trajectory estimation. An indoor radar signal dataset is generated with 11 persons under one IR-UWB radar, and the performance of MLRT is evaluated using this dataset. The measurement results show that the accuracy of MLRT improves by 8.5% and 3.6% for personal identification and fall detection, respectively, compared to state-of-the-art algorithms. The indoor radar signal dataset and the proposed MLRT source code are publicly available.

show abstract

“…Based on the radar matrix

after preprocessing, the initial target location

is determined from the following:

where Q is a constant that affects the weight of the predicted value.…”

Section: The Proposed Mlrt Methodsmentioning

confidence: 99%

Multi-Task Learning Radar Transformer (MLRT): A Personal Identification and Fall Detection Network Based on IR-UWB Radar

Jiang

Zhang

2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Both publications confirm that radar is not affected by degrading environmental conditions. Qian et al (2020) use Joint Probabilistic Data Association on ultra‐wideband (UWB) sensors for people tracking with noise suppression. UWB sensors have also been shown to be well applicable in firefighting (Tiemann et al, 2020).…”

Section: Related Workmentioning

confidence: 99%

Multiposture leg tracking for temporarily vision restricted environments based on fusion of laser and radar sensor data

Mandischer

Hou

Corves

2023

Journal of Field Robotics

View full text Add to dashboard Cite

Leg tracking is an established field in mobile robotics and machine vision in general.These algorithms, however, only distinguish the scene between leg and nonleg detections. In application fields like firefighting, where people tend to choose squatting or crouching over standing postures, those methods will inevitably fail.Further, tracking based on a single sensor system may reduce the overall reliability if brought to outdoor or complex environments with limited vision on the target objectives. Therefore, we extend our recent work to a multiposture detection system based on laser and radar sensors, that are fused to allow for maximal reliability and accuracy in scenarios as complex as indoor firefighting with vastly limited vision. The proposed tracking pipeline is trained and extensively validated on a new data set. We show that the radar tracker reaches state-of-the-art performance, and that laser and fusion tracker outperform recent methods.

show abstract