A Novel Experiment-Free Site-Specific TDoA Localization Performance-Evaluation Approach

Ye, Xiaokang; Rodríguez-Piñeiro, José; Liu, Yuan; Yin, Xuefeng; Yuste, Antonio Pérez

doi:10.3390/s20041035

Cited by 6 publications

(3 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When developing methods for RF signal processing, the exact form of the input signal plays an important role. For the task of RF target tracking, the typical signal formats are time of arrival (TOA) [18], time difference of arrival (TDOA) [19], angle of arrival (AOA) [5], [6], and received signal strength (RSS or RSSI) [10], [20]. TOA, TDOA, and AOA based methods require either multiple antennas or specialized antenna [11] arrays to perform well.…”

Section: B Radio Frequency Signalsmentioning

confidence: 99%

Radio Frequency Signal Strength Based Multitarget Tracking With Robust Path Planning

2023

View full text Add to dashboard Cite

The proliferation of technologically advanced and mobile devices poses risks to public safety and security. These threats can be mitigated by systems equipped to perform timely identification and tracking of devices and their operators. In pursuit of these capabilities, we propose an automated sensing platform designed specifically for tracking multiple, mobile radio frequency (RF) targets. There are a number of challenges involved with tracking multiple moving RF sources. We formulate the task as an iterative state estimation and path planning process, whereby the sensor platform first estimates the positions of the targets through observation of the RF environment and then plans and executes a movement path. By developing a sensor model informed by RF propagation theory, we construct a particle filter based state estimator with the potential to track multiple targets using only signal strength observations. In addition, we propose a path planning technique rooted in uncertainty minimization and safety based constraints. Finally, we validate the proficiency of the proposed methods with simulated experiments. Through analysis of tracking metrics and localization performance we show the benefits of our proposed active sensing techniques as they apply to tracking multiple RF targets. We demonstrate the robustness of our method to various environmental scenarios by testing with a multitude of realistic and challenging experimental parameters (e.g., speed of the sensor platform, number of targets, speed of targets, level of signal-to-noise ratio (SNR)). The results indicate that our method performs better than other state-of-theart tracking methods, with significant improvements seen in the most difficult scenarios with higher speed targets. In these and other settings, our method is more often able to localize the targets and with less error and uncertainty in position estimation. We also show that our method is computationally efficient and scales well to an increasing number of targets. INDEX TERMSMultitarget tracking, path planning, radio frequency signals, particle filters I. INTRODUCTION

show abstract

Section: B Radio Frequency Signalsmentioning

confidence: 99%

Radio Frequency Signal Strength Based Multitarget Tracking With Robust Path Planning

2023

View full text Add to dashboard Cite

show abstract

“…The time-of-arrival (TOA) in [ 30 ] collects the absolute travel time of the signal from a reference node (base station, beacon, access point, and so on) to the user’s equipment and computes the user’s localization by trilateration. The time-difference-of-arrival (TDOA) [ 31 ] adopts the travel time difference instead, avoiding the complex synchronization task. Notice that the measurement of the range as well as the angle heavily relies on the line-of-sight (LOS) conditions, which are hardly satisfied in practical indoor applications.…”

Section: Related Workmentioning

confidence: 99%

Co-Occurrence Fingerprint Data-Based Heterogeneous Transfer Learning Framework for Indoor Positioning

Huang

Guo

et al. 2022

Sensors

View full text Add to dashboard Cite

Distribution discrepancy is an intrinsic challenge in existing fingerprint-based indoor positioning system(s) (FIPS) due to real-time environmental variations; thus, the positioning model needs to be reconstructed frequently based on newly collected training data. However, it is expensive or impossible to collect adequate training samples to reconstruct the fingerprint database. Fortunately, transfer learning has proven to be an effective solution to mitigate the distribution discrepancy, enabling us to update the positioning model using newly collected training data in real time. However, in practical applications, traditional transfer learning algorithms no longer act well to feature space heterogeneity caused by different types or holding postures of fingerprint collection devices (such as smartphones). Moreover, current heterogeneous transfer methods typically require enough accurately labeled samples in the target domain, which is practically expensive and even unavailable. Aiming to solve these problems, a heterogeneous transfer learning framework based on co-occurrence data (HTL-CD) is proposed for FIPS, which can realize higher positioning accuracy and robustness against environmental changes without reconstructing the fingerprint database repeatedly. Specifically, the source domain samples are mapped into the feature space in the target domain, then the marginal and conditional distributions of the source and target samples are aligned in order to minimize the distribution divergence caused by collection device heterogeneity and environmental changes. Moreover, the utilized co-occurrence fingerprint data enables us to calculate correlation coefficients between heterogeneous samples without accurately labeled target samples. Furthermore, by resorting to the adopted correlation restriction mechanism, more valuable knowledge will be transferred to the target domain if the source samples are related to the target ones, which remarkably relieves the “negative transfer" issue. Real-world experimental performance implies that, even without accurately labeled samples in the target domain, the proposed HTL-CD can obtain at least 17.15% smaller average localization errors (ALEs) than existing transfer learning-based positioning methods, which further validates the effectiveness and superiority of our algorithm.

show abstract

“…The passive localization technique copes with the measurements of electromagnetic signals such as time of arrival (TOA) [3], direction of arrival (DOA) [4,5], time difference of arrival (TDOA) [6], and received signal strength (RSS) [7,8]. The above measurements are obtained from sensors deployed over the region of interest (ROI).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Multiple Co-Channel Sources Localization Using Bernoulli Heatmap

Lin

Huang

et al. 2022

Electronics

View full text Add to dashboard Cite

Multiple sources localization (MSL) has received considerable attention in scenarios of commercial, industrial, and defense areas. In this paper, a novel deep learning-based approach with observations of received signal strength (RSS) is proposed for the localization of multiple co-channel sources. The proposed method, named MSLocNet, formulates the MSL problem as a Bernoulli heatmap regression problem, solved by a fully convolutional network (FCN). The proposed MSLocNet enables simultaneous localization of variable numbers of sources, and exhibits better localization performance. Simulations, under complex environments with shadow fading, are conducted to validate the improved localization accuracy of the proposed method over other benchmark schemes. Moreover, experiments are carried out in a real environment to verify the feasibility of the proposed method.

show abstract

A Novel Experiment-Free Site-Specific TDoA Localization Performance-Evaluation Approach

Cited by 6 publications

References 41 publications

Radio Frequency Signal Strength Based Multitarget Tracking With Robust Path Planning

Radio Frequency Signal Strength Based Multitarget Tracking With Robust Path Planning

Co-Occurrence Fingerprint Data-Based Heterogeneous Transfer Learning Framework for Indoor Positioning

Deep Learning-Based Multiple Co-Channel Sources Localization Using Bernoulli Heatmap

Contact Info

Product

Resources

About