Novel Adaptive Generalized Likelihood Ratio Detector with Application to Maneuvering Target Tracking

Dionne, Dany; Michalska, H.; Oshman, Yaakov; Shinar, J.

doi:10.2514/1.13447

Cited by 19 publications

(13 citation statements)

References 6 publications

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“…There are various statistical hypothesis testing algorithms that can be used, such as the sequential probability ratio test (SPRT) [33], the cumulative sum (CUSUM) [34], and the generalized likelihood ratio (GLR) test [35]. Figure 1 illustrates a typical cyber attack detection mechanism for the UAS.…”

Section: B Monitoring Systemmentioning

confidence: 99%

Analysis and Design of Stealthy Cyber Attacks on Unmanned Aerial Systems

Kwon

Liu

Hwang

2014

Journal of Aerospace Information Systems

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Cyber security has emerged as one of the most important issues in unmanned aerial systems for which the functionality heavily relies on onboard automation and intervehicle communications. In this paper, potential cyber threats and vulnerabilities in the unmanned aerial system's state estimator to stealthy cyber attacks are identified, which can avoid being detected by the monitoring system. Specifically, this paper investigates the worst stealthy cyber attack that can maximize the state estimation error of the unmanned aerial system's state estimator while not being detected. First, the condition that the system is vulnerable to the stealthy cyber attacks is derived, and then an analytical method is provided to identify the worst stealthy cyber attack. The proposed cyber attack analysis methods are demonstrated with illustrative examples of an onboard unmanned aerial system navigation system and an unmanned aerial system tracking application. Nomenclature A, B = system matrices a c , a o = cyber attack vectors B c , B o = cyber attack matrices C = observation matrix E 1 , E 2 = noise input matrices e a = estimation error subject to cyber attacks F = equality constraint matrix h = threshold value J, Φ = objective functions k = time index L = steady-state Kalman gain L = Lagrange function N = discrete-time horizon Q = process noise covariance matrix Q oa , Q ca = controllability matrices R = measurement noise covariance matrix r = residual vector u = input vector v = measurement noise vector W c = controllability gramian w = process noise vector x a = state vector subject to cyber attackŝ x a = state estimate subject to cyber attacks y a = output vector subject to cyber attacks μ, ν = Lagrange multipliers Σ = estimation error covariance matrix Σ p = predicted error covariance matrix Σ r = residual covariance matrix X = optimization variable Ω = inequality constraint function

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Section: B Monitoring Systemmentioning

confidence: 99%

Analysis and Design of Stealthy Cyber Attacks on Unmanned Aerial Systems

Kwon

Liu

Hwang

2014

Journal of Aerospace Information Systems

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“…11b, the minimum discrimination delay is 0.07 s which is acquired by the "7/7" detection of^: s and the maximum delay does not exceed 0.26 s. The mean delay keeps about 0.15 s at all switch instants. Compared with the classical innovation-based maneuver detector, such as adaptive-H0 and the standard GLR detectors in [26], the mean delay in the same detection probability is an almost linear function of t sw monotonically decreasing from 0.35 (at t sw = 0.2 s) to 0.16 s (at t sw = 0.8 s). The reason can be attributed to the constant angular noise, and the displacement noise is proportional to the range.…”

Section: Discrimination Statisticsmentioning

confidence: 99%

Target maneuver discrimination using ISAR image in interception

Fan

Xiao

Fan

et al. 2016

EURASIP J. Adv. Signal Process.

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Discrimination for target maneuver magnitude and direction switching during the endgame is significant for interception performance improvement. Inverse synthetic aperture radar (ISAR) images carry the information related to target motion parameters. It is feasible to use them to discriminate the maneuver. An imaging model in interception is first formulated. The principle of maneuver discrimination using the ISAR images is then fully explored. A novel and practical discriminator is developed with a rigorous analysis of the scenario characteristics. The discriminator parameter selection and some important factors affecting the discrimination performance are discussed comprehensively. Finally, a simulation environment with software tools capable of generating target-realistic ISAR images is developed. The simulation results confirm the rationality of the design procedure and demonstrate that the proposed discriminator performs better than the classical innovation-based maneuver discriminator.

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“…Next, the bank of models is constructed on-line by selecting at each time instant k the most likely models from within the parametric families. The selection of the most likely models, along with the calculation of their a posteriori probabilities and modelmatched estimates, is achieved by employing an adaptive-H 0 GLR algorithm [11]. The resulting A-GLR estimator is recursive and the computational effort involved increases only linearly with the number of models.…”

Section: Description Of the A-glr Estimatormentioning

confidence: 99%

“…The adaptive-H 0 GLR algorithm is an improved version of the GLR algorithm of [12] and was first introduced in [11] for the purpose of fault detection in systems with one unknown input. The adaptive-H 0 GLR algorithm provides an estimate of the reference realization,ẑ H , and a maximum likelihood estimate,ẑ ML i , for the realization of each hypothesis H k i .…”

Section: B the Adaptive-h 0 Glr Algorithmmentioning

confidence: 99%

“…Hence, the single, adaptive, reference realization requires less information about the hybrid stochastic input process than a bank of pre-specified reference realizations and is more efficient from a numerical point of view. The adaptation of the reference realization employs a sequential probability ratio test (SPRT) and is based on the adaptive-H 0 GLR algorithm presented in [11]. The adaptive-H 0 GLR algorithm is an extension of the GLR algorithm of [12] to parametric family of reference realizations.…”

Section: Introductionmentioning

confidence: 99%

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An adaptive GLR estimator for state estimation of a maneuvering target

Dionne

Michalska

Proceedings of the 2005, American Control Conference, 2005.

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This paper presents a novel adaptive generalized likelihood ratio (A-GLR) state estimator applied to rapidly maneuvering targets in pursuit-evasion scenarios. The A-GLR estimator employs a bank of adaptive models which is constructed and updated on-line. The state estimate is a probabilistic mixture of the model-matched estimates. The adaptation of the models, the model-matched estimates, and the a-posteriori probabilities of the models are calculated recursively by employing a previously developed adaptive-H 0 GLR algorithm. Numerical simulations of a maneuvering target (a ballistic missile) show that the A-GLR estimator delivers state estimates characterized by a smaller average error and a smaller covariance as compared with those obtained using the interacting multiple model (IMM) estimator.

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Novel Adaptive Generalized Likelihood Ratio Detector with Application to Maneuvering Target Tracking

Cited by 19 publications

References 6 publications

Analysis and Design of Stealthy Cyber Attacks on Unmanned Aerial Systems

Analysis and Design of Stealthy Cyber Attacks on Unmanned Aerial Systems

Target maneuver discrimination using ISAR image in interception

An adaptive GLR estimator for state estimation of a maneuvering target

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