A DOA-Based Factor Graph Technique for 3D Multi-Target Geolocation

Cheng, Meng; Aziz, Muhammad Reza Kahar; Matsumoto, Tad

doi:10.1109/access.2019.2928851

Cited by 10 publications

(6 citation statements)

References 28 publications

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“…In the 3D scenario, ϕ n is the same measurement as in the X-Y plane expressed by (1). Another measurement of DOA [21] is the elevation angle given by…”

Section: System Modelmentioning

confidence: 99%

A TOA-DOA Hybrid Factor Graph-Based Technique for Multi-Target Geolocation and Tracking

2021

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In this article, we propose a new distributed sensors-based multi-target geolocation and tracking technique. The proposed technique is a joint time-of-arrival (TOA) and direction-of-arrival (DOA) factor graph (FG) for multi-target geolocation (FG-GE), which is further combined with another FG for extend Kalman filtering (FG-GE-EKF) for tracking. Two-dimensional (2D) and three-dimensional (3D) scenarios are considered. In the FG-GE part, a new sensor association technique is proposed to solve the matching problem, which makes the right correspondence between the DOA/TOA information gathered by the distributed sensors and each target. With the proposed sensor association technique, the measured signals from targets can adequately be matched to their corresponding FGs. Thereby, the multi-target geolocation can be reduced to multiple independent single target geolocation. In addition, in the 3D scenario, each target is projected onto three orthogonal planes in the (x, y, z) coordinate. With this operation, the 3D geolocation is decomposed into three 2D geolocation problems. In the FG-GE-EKF part, the whole tracking system can be divided into two steps: prediction step and update step. In the prediction step, the predicted state is obtained from the previous state. Then, we utilize the predicted state as a prior information, and also to update the message exchanged in FG-GE. In the update step, the estimates obtained by FG-GE are regarded as observation state which is used to refine the predicted state, and acquire the current state. With proposed the FG-GE-EKF, the position estimation accuracy and tracking performance can be improved dramatically, without requiring excessively high computational effort. INDEX TERMS Factor graph (FG), time of arrival (TOA), direction of arrival (DOA), geolocation, extend Kalman filter (EKF), tracking, sensor association.

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“…In the 3D scenario, ϕ n is the same measurement as in the X-Y plane expressed by (1). Another measurement of DOA [21] is the elevation angle given by…”

Section: System Modelmentioning

confidence: 99%

A TOA-DOA Hybrid Factor Graph-Based Technique for Multi-Target Geolocation and Tracking

2021

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“…investigated, e.g. [2], [3]. In this paper, we investigate the outage probability of transmissions in the category of network information theory.…”

Section: Introductionmentioning

confidence: 99%

Outage Probability of One-Source-With-One- Helper Sensor Systems in Block Rayleigh Fading Multiple Access Channels

Song

Cheng

et al. 2021

IEEE Sensors J.

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Two correlated sources are transmitted over a block Rayleigh fading multiple access channel (MAC), where information recovery from only one of the sources is aimed at, and the other is used as a helper which helps recover the source sequences at the destination. In this paper, the successful transmission is defined as in the case the transmission rates satisfy the intersection of both the MAC region and the Slepian-Wolf (SW) region with a helper, referred to as h-SW region, even though it is a sufficient condition. Then, the outage is defined as the average probability that the two regions do not have an intersection. The explicit expressions of the outage probability of the system are derived, in the form of multiple integrals with respect to the probability density function of instantaneous signal-to-noise ratios of each link. To have an indepth insight regarding the contributions of some special cases to the successful transmission probability, the occurrence probabilities of these cases are also evaluated. The results indicate that outage probabilities decrease as the correlation between the source and helper information increases. The impacts of geometric gains of each link and the correlation of each link's fading variation are also considered. The most significant finding of this paper is that h-SW over a MAC improves the throughput efficiency compared to the orthogonal transmission both in independent and correlated block Rayleigh fading channels without sacrificing the outage probability.

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“…In [25], a DOA-based two-dimensional (2D) FG-GE is proposed to detect a single anonymous target, with lower root mean square error (RMSE) than the least square (LS) technique. This work has been extended to three-dimensional (3D) in [26].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, a sensor separation algorithm is proposed in [26] to solve the target-DOAs matching problem in the multi-target scenario. Since messages propagated over FG-GE are all Gaussian-approximated, only the mean and the variance are needed.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, a new DOA-based FG-GE is proposed to further reduce the computational cost of linear approximation. Compared with the conventional approaches in [25], [26], the proposed FG-GE always utilizes predicted state information, based on the tracker output at the previous timing. In this sense, geolocation and tracking jointly work in one framework, and form an integrated FG.…”

Section: Introductionmentioning

confidence: 99%

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Integrated Factor Graph Algorithm for DOA-Based Geolocation and Tracking

2020

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This paper proposes a new position tracking algorithm by integrating extended Kalman filter (EKF) and direction-of-arrival (DOA)-based geolocation into one factor graph (FG) framework. A distributed sensor network is assumed for detecting an anonymous target, where the process and observation equations in the state space model (SSM) are unknown. Importantly, the predicted state information can be utilized not only for filtering, but also for enhancing the observation process. To be specific, by taking the prediction into account as the a priori, a new FG scheme is proposed for GEolocation, denoted by FG-GE. The benefits are twofold , compared with the conventional geolocation scheme which does not rely on the a priori information. First of all, significant performance improvement can be observed, in terms of the root mean square error (RMSE), when severe sensing errors are suddenly encountered. Furthermore, the proposed FG-GE can achieve dramatic reduction of computational complexity. In addition, this paper also proposes the use of a predicted Cramer-Rao lower bound (P-CRLB) to dynamically estimate the observation error variance, which demonstrates more robust tracking performance than that with only fixed average variance approximation. INDEX TERMS Factor graph (FG), direction of arrival (DOA), extended Kalman filter (EKF), geolocation, tracking, complexity analysis, CRLB.

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A DOA-Based Factor Graph Technique for 3D Multi-Target Geolocation

Cited by 10 publications

References 28 publications

A TOA-DOA Hybrid Factor Graph-Based Technique for Multi-Target Geolocation and Tracking

A TOA-DOA Hybrid Factor Graph-Based Technique for Multi-Target Geolocation and Tracking

Outage Probability of One-Source-With-One- Helper Sensor Systems in Block Rayleigh Fading Multiple Access Channels

Integrated Factor Graph Algorithm for DOA-Based Geolocation and Tracking

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