&lt;title&gt;Multisensor-multitarget bias estimation of asynchronous sensors&lt;/title&gt;

Xiao, Lin; Bar‐Shalom, Yaakov; Kirubarajan, Thiagalingam

doi:10.1117/12.542272

Cited by 16 publications

(21 citation statements)

References 16 publications

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“…For multi-target tracking, Bar Shalom proposes a solution for updating with out-ofsequence measurements [6]. In [7] the authors prop'ose a solution to the multi-sensor bias estimation problem for general asynchronous sensors. The method is based on the computation of the time between two data to be fused.…”

Section: Interval Timestampingmentioning

confidence: 99%

Influence of timestamping error on data inaccuracy

Bezet

Cherfaoui

2005

2005 7th International Conference on Information Fusion

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This paper studies the influence of timestamping error on data inaccuracy. Timestamping error refers to all time imperfections inherent in data acquisition and distributed sensor architectures. It is represented using a bounded error model. In order to estimate the significance of this error in data estimation and data fusion processes, we propose a method to convert the timestamping error into data inaccuracy. We then quantify the proportion ofinaccuracy introduced by timestamping error Real data acquired using an instrumented vehicle are analyzed, and the timestamping error is seen to represent a significant proportion ofdata inaccuracy. All applications in dynamic environments where a high level of precision is required, as well as applications in distributed environments with clock imperfections, will be concerned by this study.

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Section: Interval Timestampingmentioning

confidence: 99%

Influence of timestamping error on data inaccuracy

Bezet

Cherfaoui

2005

2005 7th International Conference on Information Fusion

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“…The difference between (1) and (10) is that the latter has no bias term and, as a result, the local tracks are bias-ignorant [9]- [11]. Note that this mismatch should be compensated for.…”

Section: Review Of Synchronous Sensor Registrationmentioning

confidence: 99%

A practical bias estimation algorithm for multisensor-multitarget tracking

Taghavi

Kirubarajan

et al. 2016

IEEE Trans. Aerosp. Electron. Syst.

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Bias estimation or sensor registration is an essential step in ensuring the accuracy of global tracks in multisensor-multitarget tracking. Most previously proposed algorithms for bias estimation rely on local measurements in centralized systems or tracks in distributed systems, along with additional information like covariances, filter gains or targets of opportunity. In addition, it is generally assumed that such data are made available to the fusion center at every sampling time. In practical distributed multisensor tracking systems, where each platform sends local tracks to the fusion center, only state estimates and, perhaps, their covariances are sent to the fusion center at non-consecutive sampling instants or scans. That is, not all the information required for exact bias estimation at the fusion center is available in practical distributed tracking systems. In this paper, a new algorithm that is capable of accurately estimating the biases even in the absence of filter gain information from local platforms is proposed for distributed tracking systems with intermittent track transmission. Through the calculation of the Posterior Cramér-Rao lower bound and various simulation results, it is shown that the performance of the new algorithm, which uses the tracklet idea and does not require track transmission at every sampling time or exchange of filter gains, can approach the performance of the exact bias estimation algorithm that requires local filter gains.

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“…The second metric is based on the classic Cramer-Rao Lower Bound (CRLB) which provides a theoretical lower bound on achievable Mean Squares Error (MSE) of the estimated state. The CRLB has been extensively used in sensor management algorithms [ 10,11]. This information theoretic metric contains performance limits inherent to the problem and independent of any specific solution.…”

Section: Motivation and Contributionsmentioning

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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<title>Multisensor-multitarget bias estimation of asynchronous sensors</title>

Cited by 16 publications

References 16 publications

Influence of timestamping error on data inaccuracy

Influence of timestamping error on data inaccuracy

A practical bias estimation algorithm for multisensor-multitarget tracking

Robust sequential view planning for object recognition using multiple cameras

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