Kalman Filtering Techniques for Radar Tracking

Ramachandra, K. V.

doi:10.1201/9781482273113

Cited by 106 publications

(17 citation statements)

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“…This assumption is convenient and allowed us to derive an efficient algorithm. However, it also makes the LG-IEKF not resilient to outlier measurements (this is also a limitation of Kalman filtering on Euclidean spaces [53]). Consequently, we propose a statistical test on Lie groups to detect and remove outliers.…”

Section: Inlier Test Before Updatementioning

confidence: 99%

From Intrinsic Optimization to Iterated Extended Kalman Filtering on Lie Groups

et al. 2016

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In this paper, we propose a new generic filter called Iterated Extended Kalman Filter on Lie Groups. It allows to perform parameter estimation when the state and the measurements evolve on matrix Lie groups. The contribution of this work is threefold: 1) the proposed filter generalizes the Euclidean Iterated Extended Kalman Filter to the case where both the state and the measurements evolve on Lie groups, 2) this novel filter bridges the gap between the minimization of intrinsic non linear least squares criteria and filtering on Lie groups, 3) in order to detect and remove outlier measurements, a statistical test on Lie groups is proposed. In order to demonstrate the efficiency of the proposed generic filter, it is applied to the specific problem of relative motion averaging, both on synthetic and real data, for Lie groups SE(3) (rigid body motions), SL(3) (homographies) and Sim(3) (3D similarities). Typical applications of these problems are camera network calibration, image mosaicing and partial 3D reconstruction The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement 288199-Dem@Care

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Section: Inlier Test Before Updatementioning

confidence: 99%

From Intrinsic Optimization to Iterated Extended Kalman Filtering on Lie Groups

et al. 2016

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“…The sensor model is the usual sonar model [11]. If the sensor is oriented at an angle θ to the global x-axis the vehicle's measurement in the global frame is given by…”

Section: Simulation Resultsmentioning

confidence: 99%

Sensor scheduling algorithms requiring limited computation [vehicle sonar range-finder example]

Gupta

Chung

Hassibi

et al.

2004 IEEE International Conference on Acoustics, Speech, and Signal Processing

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In this paper, we consider the scenario where many sensors co-operate to estimate a process. Only one sensor can take a measurement at any time step. We wish to come up with optimal sensor scheduling algorithms. The problem is motivated by the use of sonar range-finders used by the vehicles on the Caltech Multi-Vehicle Wireless Testbed. We see that this problem involves searching a tree in general and propose and analyze two strategies for pruning the tree to keep the computation limited. The first is a sliding window strategy motivated by the Viterbi algorithm, and the second one uses thresholding. We also study a technique that employs choosing the sensors randomly from a probability distribution which can then be optimized. The performance of the algorithms are illustrated with the help of numerical examples.

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“…In this section, we propose a distributed tracking algorithm based on the Kalman filter [8], [9]. Based on the model (1) and (10) Kalman filter recursively updates the estimate of the state of the vector by processing successive measurements.…”

Section: Tracking Algorithm For Sensor Networkmentioning

confidence: 99%

Distributed and collaborative tracking for energy-constrained ad-hoc wireless sensor networks

Balasubramanian

Elangovan

Jayaweera

et al.

2004 IEEE Wireless Communications and Networking Conference (IEEE Cat. No.04TH8733)

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An energy-aware, collaborative tracking algorithm is proposed for ad-hoc wireless sensor networks. The algorithm is implemented distributively by passing sensing and computation operations from one sensor to another. In order to maximize the life-time of the network, this active sensor is chosen by a high end sensor in order to minimize the cost function based on the total energy spent by the network at each time instant. Assuming a wireless ad-hoc sensor network in which low end sensors are randomly distributed with data gathering node as a high end sensor, a state-space model for a target motion and sensor measurement and a cost function based on energy requirements for communication and sensing has been derived. Simulation results for the performance of the proposed scheme have also been presented.

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Kalman Filtering Techniques for Radar Tracking

Cited by 106 publications

References 0 publications

From Intrinsic Optimization to Iterated Extended Kalman Filtering on Lie Groups

From Intrinsic Optimization to Iterated Extended Kalman Filtering on Lie Groups

Sensor scheduling algorithms requiring limited computation [vehicle sonar range-finder example]

Distributed and collaborative tracking for energy-constrained ad-hoc wireless sensor networks

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