A single observer passive tracking algorithm based on maneuvering detection
01 Nov 2016-pp 1504-1508
TL;DR: A new tracking filter method was proposed for the high non-linear singer observer passive location and tracking (SOPLAT) system, where the traditional tracking filters are easy divergence and low tracking precision.
Abstract: Single-Station passive location is a hot topic in target tracking research. A new tracking filter method was proposed for the high non-linear singer observer passive location and tracking (SOPLAT) system, where the traditional tracking filters are easy divergence and low tracking precision. The proposed method used the sequential importance resampling particle filter(SIRPF) to track the target in the initial phase, and then switch to different filter algorithms (including extended Kalman Filter (EKF) and unscented Kalman Filter (UKF)) to keep the tracking of the target according to the maneuver values. At the same time monitoring the target when the target's maneuver values are larger than the critical value return to SIRPF algorithm recapture the target. The proposed algorithm improved the precision of the tracking for the EKF and UKF and reduced the computational complexity for the SIRPF. The computer simulations reveal that this algorithm is effective.
Citations
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TL;DR: In this paper, a joint filter-based algorithm was proposed to detect non-cooperative space targets in real-time target tracking, which is difficult to detect and thus greatly affects the accuracy of target tracking.
Abstract: Orbit maneuver of non‐cooperative space targets is difficult to detect, and thus greatly affects the accuracy of real‐time target tracking. To solve this problem, a joint filter‐based algo...
6 citations
Cites methods from "A single observer passive tracking ..."
...Furthermore, abnormal observations (outliers) in real-time tasks may lead to false alarms.(4,5) Two methods are used to construct detection statistics: one utilizes the change of measurement information for the determination, and the other utilizes the filter residuals/innovation, filter gain, or input estimation....
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References
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TL;DR: Both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters are reviewed.
Abstract: Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system. Moreover, it is typically crucial to process data on-line as it arrives, both from the point of view of storage costs as well as for rapid adaptation to changing signal characteristics. In this paper, we review both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters. Particle filters are sequential Monte Carlo methods based on point mass (or "particle") representations of probability densities, which can be applied to any state-space model and which generalize the traditional Kalman filtering methods. Several variants of the particle filter such as SIR, ASIR, and RPF are introduced within a generic framework of the sequential importance sampling (SIS) algorithm. These are discussed and compared with the standard EKF through an illustrative example.
11,409 citations
"A single observer passive tracking ..." refers methods in this paper
...The proposed method used the sequential importance resampling particle filter(SIRPF) to track the target in the initial phase, and then switch to different filter algorithms (including extended kalman filter (EKF) and unscented kalman filter (UKF)) to keep the tracking of the target according to…...
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08 Nov 2004
TL;DR: The motivation, development, use, and implications of the UT are reviewed, which show it to be more accurate, easier to implement, and uses the same order of calculations as linearization.
Abstract: The extended Kalman filter (EKF) is probably the most widely used estimation algorithm for nonlinear systems. However, more than 35 years of experience in the estimation community has shown that is difficult to implement, difficult to tune, and only reliable for systems that are almost linear on the time scale of the updates. Many of these difficulties arise from its use of linearization. To overcome this limitation, the unscented transformation (UT) was developed as a method to propagate mean and covariance information through nonlinear transformations. It is more accurate, easier to implement, and uses the same order of calculations as linearization. This paper reviews the motivation, development, use, and implications of the UT.
6,098 citations
"A single observer passive tracking ..." refers methods in this paper
...The proposed method used the sequential importance resampling particle filter(SIRPF) to track the target in the initial phase, and then switch to different filter algorithms (including extended kalman filter (EKF) and unscented kalman filter (UKF)) to keep the tracking of the target according to…...
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TL;DR: This tutorial serves two purposes: to survey the part of the theory that is most important for applications and to survey a number of illustrative positioning applications from which conclusions relevant for the theory can be drawn.
Abstract: The particle filter (PF) was introduced in 1993 as a numerical approximation to the nonlinear Bayesian filtering problem, and there is today a rather mature theory as well as a number of successful applications described in literature. This tutorial serves two purposes: to survey the part of the theory that is most important for applications and to survey a number of illustrative positioning applications from which conclusions relevant for the theory can be drawn. The theory part first surveys the nonlinear filtering problem and then describes the general PF algorithm in relation to classical solutions based on the extended Kalman filter (EKF) and the point mass filter (PMF). Tuning options, design alternatives, and user guidelines are described, and potential computational bottlenecks are identified and remedies suggested. Finally, the marginalized (or Rao-Blackwellized) PF is overviewed as a general framework for applying the PF to complex systems. The application part is more or less a stand-alone tutorial without equations that does not require any background knowledge in statistics or nonlinear filtering. It describes a number of related positioning applications where geographical information systems provide a nonlinear measurement and where it should be obvious that classical approaches based on Kalman filters (KFs) would have poor performance. All applications are based on real data and several of them come from real-time implementations. This part also provides complete code examples.
581 citations
"A single observer passive tracking ..." refers background in this paper
...Single-Station passive location is a popular research topic on account of its simplicity and low cost [1][10]....
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01 Jan 2010
481 citations
TL;DR: This paper considers the case where one transmitter and multiple, distributed, receivers are used to estimate the location of a passive (reflecting) object and proposes a novel, Two-Step estimation (TSE) algorithm for the localization of the object.
Abstract: Localization of objects is fast becoming a major aspect of wireless technologies, with applications in logistics, surveillance, and emergency response. Time-of-arrival (TOA) localization is ideally suited for high-precision localization of objects in particular in indoor environments, where GPS is not available. This paper considers the case where one transmitter and multiple, distributed, receivers are used to estimate the location of a passive (reflecting) object. It furthermore focuses on the situation when the transmitter and receivers can be synchronized, so that TOA (as opposed to time-difference-of-arrival (TDOA)) information can be used. We propose a novel, Two-Step estimation (TSE) algorithm for the localization of the object. We then derive the Cramer-Rao Lower Bound (CRLB) for TOA and show that it is an order of magnitude lower than the CRLB of TDOA in typical setups. The TSE algorithm achieves the CRLB when the TOA measurements are subject to small Gaussian-distributed errors, which is verified by analytical and simulation results. Moreover, practical measurement results show that the estimation error variance of TSE can be 33 dB lower than that of TDOA based algorithms.
282 citations
"A single observer passive tracking ..." refers background in this paper
...…based on maneuvering detection Wei Yang, Wei Zhang,Weihang Dong Research Institute of Electronic Science and Technology of UESTC, Chengdu, China 13018226729@163.com, Wzhang@uestc.edu.cn, weihangdong@foxmail.com Abstract—Single-Station passive location is a hot topic in target tracking research....
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...978-1-5090-1345-6/16/$31.00 ©2016 IEEE 1504 ICSP2016 A single observer passive tracking algorithm based on maneuvering detection Wei Yang, Wei Zhang,Weihang Dong Research Institute of Electronic Science and Technology of UESTC, Chengdu, China 13018226729@163.com, Wzhang@uestc.edu.cn, weihangdong@foxmail.com Abstract—Single-Station passive location is a hot topic in target tracking research....
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