Performance measure for Markovian switching systems using best-fitting Gaussian distributions

Hernandez, Marcel L.; Ristić, Branko; Farina, A.; Sathyan, T.; Kirubarajan, T.

doi:10.1109/taes.2008.4560217

Cited by 39 publications

(26 citation statements)

References 29 publications

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“…Φ(k) and ε(k) are chosen so that at each time step the distribution of x(k + 1) has the same mean and covariance under each system. Although it is clearly that the above approximation does not fully capture the characteristics of the distribution of the true target state, which may be multi-modal due to model switching, it is demonstrated in [15] that the corresponding moment-matched state distribution fits the true distribution well and can be used to provide predictive measures that is in close agreement with the performance of state-of-the-art multiple model estimator with a very low computational load, which is important in time critical scenarios. Let M j (k) denote the event that M k = j, the predicted model probability µ j ( k + 1| k) can be computed by…”

Section: Gaussian Fitting Of Target Dynamics Via Moment Matchingmentioning

confidence: 98%

“…In this paper, further focuses are devoted to prior threshold optimization for the maneuvering target tracking in clutter. Our work differs from that in [14] in that we compute the objective function of threshold optimization by approximating the multimodal prior target probability density function with a best-fitting Gaussian (BFG) distribution [15] at each time step to estimate the performance measure for tracking maneuvering targets with linear Markovian switching dynamics. A more reasonable and efficient adaptive detection threshold optimization method for maneuvering target tracking in clutter is proposed and extended to the case with the nonlinear measurement equation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Efficient Adaptive Detection Threshold Optimization for Tracking Maneuvering Targets in Clutter

Wang¹,

Wang²,

Qin³

et al. 2012

PIER B

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Abstract-In this paper, we focus on the adaptive prior detection threshold setting problem to optimize the overall performance of the joint detection-tracking system for maneuvering target tracking in clutter. It is shown that our problem can be reduced to the information reduction factor (IRF) maximization by Gaussian fitting of maneuvering target Markovian switching dynamics via moment matching, even for the case with the nonlinear measurement equation. Our proposed adaptive threshold setting method outperforms the conventional threshold setting approaches greatly and also exhibits a mildly improvement in comparison with the earlier method for this problem in terms of tracking performance, especially in track loss percentage (TLP). However the computational burden of our method is reduced significantly because in our method generally only one IRF corresponding to the common validation region, not the every IRF corresponding to the individual model-conditioned validation region, is needed for threshold optimization at each time step and an approximate closed-form solution can also be obtained for the special case of the Neyman-Pearson (NP) detector.

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Section: Gaussian Fitting Of Target Dynamics Via Moment Matchingmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Efficient Adaptive Detection Threshold Optimization for Tracking Maneuvering Targets in Clutter

Wang¹,

Wang²,

Qin³

et al. 2012

PIER B

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show abstract

“…The EBCRB has a disadvantage in that it ignores uncertainties in the mode sequence R i k . In situations where those uncertainties significantly deteriorate the performance of the unconditional estimator, the EBCRB will be far from the optimal performance, see [23], [24] for illustrating examples. In [20], another type of BCRB for JMSs was proposed, which assumed R i k unknown, but which is still sometimes more optimistic than the EBCRB.…”

Section: Enumeration Bayesian Cramér-rao Boundmentioning

confidence: 99%

The Marginal Enumeration Bayesian Cramér–Rao Bound for Jump Markov Systems

Fritsche¹,

Orguner

Svensson

et al. 2014

IEEE Signal Process. Lett.

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Abstract-A marginal version of the enumeration BayesianCramér-Rao Bound (EBCRB) for jump Markov systems is proposed. It is shown that the proposed bound is at least as tight as EBCRB and the improvement stems from better handling of the nonlinearities. The new bound is illustrated to yield tighter results than BCRB and EBCRB on a benchmark example. , the development of bounds on the estimation performance is still emerging. In [19], a recursive BCRB conditioned on a specific model sequence is proposed, which explores the information contained in the entire state and measurement sequence. The unconditional BCRB is then found by taking the expected value of the conditional BCRB with respect to all possible mode sequences. Even though this bound, herein after referred to as enumeration BCRB (EBCRB), will give a lower bound on the estimation performance, it is often overoptimistic and can not predict attainable estimation performance. In [20], another type of unconditional BCRB has been formulated for JMS, that is similar to the EBCRB as it also evaluates the information contained in the entire state and measurement sequence, but avoids the conditioning on the model sequence. However, it was shown in [20], that this bound is sometimes even more overoptimistic than the EBCRB. In this paper, another type of BCRB is developed which builds Index Terms-Jump

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“…For maneuvering target tracking, the jump Markov system (JMS) has proved to be an effective method, which switches among a set of candidate models in a Markovian fashion [20,21]. Pasha et al introduced the linear JMS into the PHD filters and derived a closed-form solution for the PHD recursion in [22].…”

Section: Introductionmentioning

confidence: 99%

Adaptive parameter particle CBMeMBer tracker for multiple maneuvering target tracking

Yang

Xiao

et al. 2016

EURASIP J. Adv. Signal Process.

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Cardinality-balanced multi-target multi-Bernoulli (CBMeMBer) filter has been demonstrated as a promising algorithm for multi-target tracking, and the multi-model (MM) method has been incorporated into the CBMeMBer filter to solve the problem of multiple maneuvering target tracking. However, it is difficult to construct a proper set of models due to the unknown maneuvering parameters of the targets. Moreover, the number of models may increase exponentially if more unknown parameters have to be taken into account to match the target motion modes, which may lead to prohibitive computational complexity. To address this aspect, this paper proposes to incorporate the adaptive parameter estimation (APE) method in the framework of CBMeMBer filters, so that the model with unknown maneuvering parameter can be modified adaptively by using the selected parameter particles. Moreover, a particle labeling technique is introduced in the proposed algorithm in order to obtain the individual target track, which results in the adaptive parameter particle filter CBMeMBer (APPF-CBMeMBer) tracker. Simulation results show that the proposed algorithm can effectively track multiple maneuvering targets with abruptly changing parameters and exhibit better robustness than those of the well-known MM-based approaches.

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Performance measure for Markovian switching systems using best-fitting Gaussian distributions

Cited by 39 publications

References 29 publications

Efficient Adaptive Detection Threshold Optimization for Tracking Maneuvering Targets in Clutter

Efficient Adaptive Detection Threshold Optimization for Tracking Maneuvering Targets in Clutter

The Marginal Enumeration Bayesian Cramér–Rao Bound for Jump Markov Systems

Adaptive parameter particle CBMeMBer tracker for multiple maneuvering target tracking

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