Indoor tracking of multiple persons with a 77 GHz MIMO FMCW radar

Knudde, Nicolas; Vandersmissen, Baptist; Parashar, Karthick; Couckuyt, Ivo; Jalalvand, Azarakhsh; Bourdoux, André; Neve, Wesley De; Dhaene, Tom

doi:10.23919/eurad.2017.8249147

Cited by 29 publications

(16 citation statements)

References 12 publications

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“…The EKF and UKF methods are used as benchmarks and are implemented using a constant velocity model, which is customary in the person tracking application literature [1], [5], [16]. The numerical values for the process noise and the measurement covariance matrices are the same as in [16], evaluated empirically for walking human targets.…”

Section: Training and Tracking Resultsmentioning

confidence: 99%

“…Our aim is to obtain accurate positioning of the targets within the physical space. So far, the few available solutions to this problem [1], [5], have relied on a Bayesian approach using the extended Kalman filter (EKF) method [6]. Kalman filter (KF), however, is suitable for systems that follow a linear evolution model with Gaussian noise.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning for Accurate Indoor Human Tracking with a mm-Wave Radar

Pegoraro

Solimini

Matteo

et al. 2020

2020 IEEE Radar Conference (RadarConf20)

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We address the use of backscattered mm-wave radio signals to track humans as they move within indoor environments. The common approach in the literature leverages the extended Kalman filter (EKF) method, which however undergoes a severe performance degradation when the system evolution model is highly non-linear or presents long-term time dependencies among the system states. In this work, we propose an original model-free tracking procedure based on denoising autoencoders and sequence-to-sequence neural networks, showing its superior performance with respect to state-of-the-art methods. Our architecture can be trained in either a supervised or unsupervised manner, trading tracking accuracy for flexibility. The proposed system is tested on our own measurements, obtained with a 77 GHz radar on single and multiple subjects simultaneously moving in an indoor space. The results are compared against the ground truth trajectories from a motion tracking system, obtaining average tracking errors as low as 12 cm.

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Section: Training and Tracking Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning for Accurate Indoor Human Tracking with a mm-Wave Radar

Pegoraro

Solimini

Matteo

et al. 2020

2020 IEEE Radar Conference (RadarConf20)

View full text Add to dashboard Cite

show abstract

“…In order to deal with the MTT data association issues, we found in literature several methods are classified into Bayesian and other non-Bayesian filters, has been applied to address different scenarios, such as, Markov Chain Monte Carlo Data Association (MCMCDA) was proposed in [7] as a solution to replace the conventional method as known by The Multiple Hypothesis Tracking (MHT), to handle the low Signal-to-Noise Ratio (SNR) in the pre-processing phase. On the other hand, the Gaussian mixture (GM) combined with Probability Hypothesis Density (PHD), then the full GM-PHD algorithm [8] provides a promising framework to process the several measurements from multi sensors.…”

Section: Related Workmentioning

confidence: 99%

Improved Multi Target Tracking in MIMO Radar System Using New Hybrid Monte Carlo–PDAF Algorithm

Zarai¹,

Cherif²

2022

IoT Applications Computing

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This article deals with the multi-target tracking problem (MTT) in MIMO radar systems. As a result, this problem is now seen as a new technological challenge. Thus, in different tracking scenarios, measurements from sensors are usually subject to a complex data association issue. The MTT data association problem of assigning measurements-to-target or target-state-estimates becomes more complex in MIMO radar system, once the crossing target tracking scenario arises, hence the interference phenomenon may interrupt the received signal and miss the state estimation process. To avoid most of these problems, we have improved a new hybrid algorithm based on particle filter called “Monte Carlo” associated to Joint Probabilistic data Association filter (JPDAF), the whole approach named MC-JPDAF algorithm has been proposed to replace the traditional method as is known by the Extended KALMAN filter (EKF) combined with JPDAF method, such as EKF-JPDAF algorithm. The obtained experimental results showed a challenging remediation. Where, the MC-JPDAF converges towards the accurate state estimation. Thus, more efficient than EKF-JPDAF. The simulation results prove that the designed system meets the objectives set for MC-JPDA by referring to an experimental database using the MATLAB Software Development Framework.

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“…Finally, the authors of [19] designed a deep convolutional neural network (DCNN) resulting in an accuracy of 81.61% on lower-power radar data. Radar data can also be used for non-classification purposes such as person tracking [13] The structured inference network was proposed in [14]. The authors apply the model to the reconstruction of polyphonic music and the counterfactual prediction of electronic health records of patient data.…”

Section: Related Workmentioning

confidence: 99%

Structured Inference Networks Using High-Dimensional Sensors for Surveillance Purposes

Polfliet

Knudde

Vandersmissen

et al. 2018

Engineering Applications of Neural Networks

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Video cameras are arguably the world's most used sensors for surveillance systems. They give a highly detailed representation of a situation that is easily interpreted by both humans and computers. However, these representations can lose part of their representational value when being recorded in less than ideal circumstances. Bad weather conditions, low-light illumination or concealing objects can make the representation more opaque. A radar sensor is a potential solution for these situations, since it is unaffected by the light intensity and can sense through most concealing objects. In this paper, we investigate the performance of a structured inference network on data of a low-power radar device. A structured inference network applies automated feature extraction by creating a latent space out of which the observations can be reconstructed. A classification model can then be trained on this latent space. This methodology allows us to perform experiments for both person identification and action recognition, resulting in competitive error rates ranging from 0% to 6.5% for actions recognition and 10% to 12% for person identification. Furthermore, the possibility of a radar sensor being used as a complement to a camera sensor is investigated.

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Indoor tracking of multiple persons with a 77 GHz MIMO FMCW radar

Cited by 29 publications

References 12 publications

Deep Learning for Accurate Indoor Human Tracking with a mm-Wave Radar

Deep Learning for Accurate Indoor Human Tracking with a mm-Wave Radar

Improved Multi Target Tracking in MIMO Radar System Using New Hybrid Monte Carlo–PDAF Algorithm

Structured Inference Networks Using High-Dimensional Sensors for Surveillance Purposes

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