A Comparison of Two CramÉr–Rao Bounds for Nonlinear Filtering with&amp;lt;tex&amp;gt;$rm P_d ≪ 1$&amp;lt;/tex&amp;gt;

Hernandez, Marcel L.; Ristić, Branko; Farina, A.; Timmoneri, L.

doi:10.1109/tsp.2004.831906

Cited by 66 publications

(62 citation statements)

References 21 publications

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“…where n s is the number of measurement sequence realisations considered, andJ r (X) is the conditional FIM for measurement sequence realisation r. This is referred to the "enumeration" bound [29] and has been shown to be the least optimistic formulation of the CRLB [30] for the case P d < 1. The CRLB location root mean square error (RMSE) is then given as follows:…”

Section: Cramér-rao Lower Boundmentioning

confidence: 99%

Novel maximum likelihood approach for passive detection and localisation of multiple emitters

Hernandez¹

2017

EURASIP J. Adv. Signal Process.

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In this paper, a novel target acquisition and localisation algorithm (TALA) is introduced that offers a capability for detecting and localising multiple targets using the intermittent "signals-of-opportunity" (e.g. acoustic impulses or radio frequency transmissions) they generate. The TALA is a batch estimator that addresses the complex multi-sensor/multi-target data association problem in order to estimate the locations of an unknown number of targets. The TALA is unique in that it does not require measurements to be of a specific type, and can be implemented for systems composed of either homogeneous or heterogeneous sensors. The performance of the TALA is demonstrated in simulated scenarios with a network of 20 sensors and up to 10 targets. The sensors generate angle-of-arrival (AOA), time-of-arrival (TOA), or hybrid AOA/TOA measurements. It is shown that the TALA is able to successfully detect 83-99% of the targets, with a negligible number of false targets declared. Furthermore, the localisation errors of the TALA are typically within 10% of the errors generated by a "genie" algorithm that is given the correct measurement-to-target associations. The TALA also performs well in comparison with an optimistic Cramér-Rao lower bound, with typical differences in performance of 10-20%, and differences in performance of 40-50% in the most difficult scenarios considered. The computational expense of the TALA is also controllable, which allows the TALA to maintain computational feasibility even in the most challenging scenarios considered. This allows the approach to be implemented in time-critical scenarios, such as in the localisation of artillery firing events. It is concluded that the TALA provides a powerful situational awareness aid for passive surveillance operations.

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Section: Cramér-rao Lower Boundmentioning

confidence: 99%

Novel maximum likelihood approach for passive detection and localisation of multiple emitters

Hernandez¹

2017

EURASIP J. Adv. Signal Process.

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“…CRLB, defined as the inverse of the Fisher Information Matrix, is used widely in nonlinear filtering where it offers the best attainable second-order error performance, while no closed-form solution exists for the nonlinear filtering problem [115][116][117].…”

Section: Applications Of Cramer-rao Lower Bound (Crlb)mentioning

confidence: 99%

“…Alternatively, the Fisher information matrix can be calculated using Riccati-like recursions [115,117], where, We employ the Vegas Adaptive Monte Carlo Algorithm [124,125] for calculating the expectation in (5.27). The implementation of this method is not as straightforward as that of the MI as the integrals involved must be computed during the recognition phase.…”

Section: A Recursive Formulation For Posterior Crlbmentioning

confidence: 99%

Robust sequential view planning for object recognition using multiple cameras

Farshidi

Sirouspour

Kirubarajan

2009

Image and Vision Computing

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In this thesis the problem of object recognition/pose estimation using active sensing is investigated. It is assumed that multiple cameras acquire images from different view angles of an object belonging to a set of a priori known objects. The eigenspace method is used to process the sensory observations and produce an abstract measurement vector. This step is necessary to avoid the manipulation of the original sensor data, i.e. large images, that can render the sensor modelling and matching process practically infeasible.The eigenspace representation is known to have shortcomings in dealing with structured noise such as occlusion. To overcome this problem, models of occlusions and sensor noise have been incorporated into the probabilistic model of sensor/object to increase robustness with respect to such uncertainties. The active recognition algorithm has also been modified to consider the possibility of occlusion, as well as variation in the occlusion levels due to camera movements.A recursive Bayesian state estimation problem is formulated to model the observation uncertainties through a probabilistic scheme. This enables us to identify the object and estimate its pose by fusing the information obtained from individual cameras. To this end, an extensive training step is performed, providing the system with the sensor model required for the Bayesian estimation. In order to enhance the quality A multi-camera active vision system has been developed in order to implement the ideas proposed in this thesis. Comparative Monte Carlo experiments conducted with the two-camera system demonstrate the effectiveness of the proposed methods in object classification/pose estimation in the presence of structured noise. Different concepts introduced in this work, i.e., the multi-camera data fusion, the occlusion modelling, and the active camera movement, all improve the recognition process significantly. Specifically, these approaches all increase the recognition rate, decrease the number of steps taken before recognition is completed, and enhance robustness with respect to partial occlusion considerably.iv

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“…The existing performance evaluation methods based on the Cramér-Rao lower bound (CRLB) [24][25][26] are not applicable for such a JDE algorithm since CRLB only considers the estimation error but not the detection error. Within the random finite set (RFS) framework, Rezaeian and Vo [27] derived the JDE error bound for a point target in the presence of clutters and missed detections.…”

Section: Introductionmentioning

confidence: 99%

Error bounds for joint detection and estimation of multiple unresolved target-groups

Liu

Duan

Yuan

et al. 2016

Digital Signal Processing

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A Comparison of Two CramÉr–Rao Bounds for Nonlinear Filtering with<tex>$rm P_d ≪ 1$</tex>

Cited by 66 publications

References 21 publications

Novel maximum likelihood approach for passive detection and localisation of multiple emitters

Novel maximum likelihood approach for passive detection and localisation of multiple emitters

Robust sequential view planning for object recognition using multiple cameras

Error bounds for joint detection and estimation of multiple unresolved target-groups

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