Passive tracking using TDOA for super-low-altitude target

Wu, Panglong; Bo, Yuming; Kong, Jian; Li, Xingxiu

doi:10.1109/icarcv.2010.5707261

Cited by 6 publications

(6 citation statements)

References 8 publications

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“…Consider every UAV 2 to receive the beacon signal from the moving target. Knowing the propagation speed of the signal c, the measurement at each UAV i (at time-instant k) is [6], [36], [68], [69],…”

Section: B Multilateration-based Measurementsmentioning

confidence: 99%

“…Let t i represent the time the beacon signal reached from the target to the UAV i ; then, the shared TOA is simply p(k) − p i (k) = ct i with p i as the position of the i th UAV and p as the target position. Then, the measurement h i (at UAV i ) can be expressed as a function of p and, in turn, as a function of x in (2) [69]:…”

Section: B Multilateration-based Measurementsmentioning

confidence: 99%

See 1 more Smart Citation

Distributed Estimation Approach for Tracking a Mobile Target via Formation of UAVs

Doostmohammadian

Taghieh

Zarrabi

2022

IEEE Trans. Automat. Sci. Eng.

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This paper considers distributed estimation methods to enable the formation of Unmanned-Aerial-Vehicles (UAVs) that track a moving target. The UAVs (or agents) are equipped with communication devices to receive a beacon signal from the target and share information with neighboring UAVs. The shared information includes the time-of-arrival (TOA) of the beacon signal from the target and estimates on the target's location. Every UAV processes the received information from the neighbors using a single-time-scale distributed estimation protocol. This differs from multi-time-scale protocols that require (i) many consensus iterations on a-priori estimates, (ii) fast communication among agents (in general, much faster than the sampling rate of the target dynamics), and thus, morecostly communication equipment and processing units. Further, our approach outperforms most single-time-scale methods in terms of observability assumption as these methods assume that the target is observable via the measurement data received from neighboring UAVs (referred to as local-observability). This requires more communications among the sensors. In contrast, our approach is only based on global-observability assumption, and thus, requires less networking (only strong-connectivity) and communication traffic along with less computational load by data-processing once at the same time-scale of sampling target dynamics. We consider modified time-difference-of-arrival (TDOA) measurements with a constant output matrix for the linearized model. UAVs make a pre-specified formation, and by estimating the target's location via these measurements, move along with the target. Note to Practitioners-Inspired by recent development in indus-trial UAVs along with emerging progress in low-cost processing units, fog computing systems, and wireless communications, this paper considers mobile tracking of a moving target via a group of wireless-connected autonomous drones. In the classical tracking methods, which are prone to single-point-of-failure, all the sensors need to send their information to a ground central station over a long-range and costly data-transmission channel. In contrast, by collaborative tracking the processing and decision-making are distributed among a swarm of drones Manuscript

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Section: B Multilateration-based Measurementsmentioning

confidence: 99%

Section: B Multilateration-based Measurementsmentioning

confidence: 99%

Distributed Estimation Approach for Tracking a Mobile Target via Formation of UAVs

Doostmohammadian

Taghieh

Zarrabi

2022

IEEE Trans. Automat. Sci. Eng.

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“…. The TDOA range equation can be formulated as [11] Proceedings of the 33rd Chinese Control Conference July 28-30, 2014, Nanjing, China…”

Section: Location Principlementioning

confidence: 99%

Maneuvering target tracking using passive TDOA measurements

Guo

Zhang

et al. 2014

Proceedings of the 33rd Chinese Control Conference

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This paper proposes a novel interacting multiple model (IMM) algorithm to track a maneuvering target, with the aim of improving the tracking performance of a time difference of arrival (TDOA) passive tracking system. Under the architecture of the proposed algorithm, the multiple model deals with the model switching, while the iterated extended Kalman filter (EKF) accounts for non-linearity in the dynamic system models. The tracking performances of the proposed algorithm, EKF, IMM are compared via Monte Carlo simulations. Simulation results indicate that the proposed algorithm is an effective nonlinear filtering algorithm for TDOA passive tracking system, and has higher tracking precision than the IMM and EKF. The proposed algorithm can reduce nearly 6.75% and 61.23% of the positioning error than IMM and EKF algorithms.

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“…TDE can be commonly divided into two types, "active" [5] and "passive" [19]. In a passive system, i.e., passive target location estimation [24] and passive target tracking [27], passive time delay estimation is an important technique in multipath environment.…”

Section: Introductionmentioning

confidence: 99%

Passive Multipath Time Delay Estimation Using MCMC Methods

Zhu²,

Zhang

et al. 2015

Circuits Syst Signal Process

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Passive time delay estimation in multipath environments is studied in this paper. A novel restrained maximum likelihood (ML) estimator is proposed to estimate the multiple time delays. Unlike traditional ML function which has P global maximum values, restraint conditions limit the ML function of P paths time delays signal with only one global maximum value. Markov chain Monte Carlo (MCMC) algorithm is used to find the global maximum of the restrained likelihood function to avoid traditional complex multidimensional grid search, initialization-dependent iterative methods or methods using interpolation to enhance performance. Indeed, MCMC sampling technique for ML function has a lower computational complexity than importance sampling (IS), which needs to compute the required realizations before sampling. Furthermore, Cramer-Rao lower bound of this model is derived.

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Passive tracking using TDOA for super-low-altitude target

Cited by 6 publications

References 8 publications

Distributed Estimation Approach for Tracking a Mobile Target via Formation of UAVs

Distributed Estimation Approach for Tracking a Mobile Target via Formation of UAVs

Maneuvering target tracking using passive TDOA measurements

Passive Multipath Time Delay Estimation Using MCMC Methods

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