Comparison of Recursive Algorithms for Emitter Localisation using TDOA Measurements from a Pair of UAVs

Okello, N.; Fletcher, F.; Mušicki, D.; Ristić, Branko

doi:10.1109/taes.2011.5937261

Cited by 58 publications

(29 citation statements)

References 21 publications

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“…However, passive location and tracking technology has been developed. DTOA-EKF (difference time of arrive-extended Kalman filtering) algorithm has been applied widely [8], [9], which uses passive sensors' measurements to implement target tracking. In this paper, we utilize covariance control method [10], [11] to implement radar intermittent radiation.…”

Section: Introductionmentioning

confidence: 99%

A novel radar radiation control strategy based on passive tracking in multiple aircraft platforms

Wang

Zhou

Shi

2014

2014 IEEE China Summit &Amp; International Conference on Signal and Information Processing (ChinaSIP)

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This paper proposes a radar radiation control method based on cooperation tracking of multiple sensors. The main idea of the method is passive tracking in multiple platforms is used preferentially, while radar tracks target timely according to the tracking accuracy. In this paper, a tracking accuracy metric called interactive multiple model predicted covariance (IMMPC) is derived by IMM-EKF algorithm. By comparing with the pre-defined threshold, whether or not the radar transmitter radiates energy is determined. Simulation results show that the proposed radiation control method can reduce the radar radiation times effectively and have a good performance of low probability of interception (LPI).

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Section: Introductionmentioning

confidence: 99%

A novel radar radiation control strategy based on passive tracking in multiple aircraft platforms

Wang

Zhou

Shi

2014

2014 IEEE China Summit &Amp; International Conference on Signal and Information Processing (ChinaSIP)

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“…In this case, for the non-linearity between the target state and the passive sensor measurements, the target tracking problem can be cast into the non-linear state estimation problem in general [6][7][8][9][10][11][12]. To solve the problem, various non-linear filtering techniques such as maximum-likelihood estimator, extended Kalman filter (EKF) and unscented Kalman filter have been attempted so far.…”

Section: Introductionmentioning

confidence: 99%

Linear recursive passive target tracking filter for cooperative sea‐skimming anti‐ship missiles

Han

Sang

Whang

et al. 2014

IET Radar, Sonar & Navigation

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This study addresses the passive target tracking problem using the range difference (RD) information measured by seaskimming anti-ship missiles. Apart from the conventional non-linear filtering approaches, the RD-based passive target tracking problem is newly formulated within the framework of the recently developed non-conservative robust Kalman filter (NCRKF). By applying NCRKF, the authors are able to cope with the performance degradation in the process of adopting linear measurement model as well as to determine the desirable missile formation in view of target tracking performance. Moreover, the proposed filter might be adequate for the real-time multiple-missile applications for its linear recursive structure. Through the simulations for a typical missile homing scenario, it is shown that the proposed scheme could provide faster convergence rate and better target tracking performance than the previous methods.

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“…Non-linear measurements are often generated by passive sensors, including passive radars and sonars, time difference of arrival (TDOA) [1], frequency difference of arrival (FDOA) [2] etc. Measurement non-linearities are often handled by extensions of Kalman filter, including the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

“…The estimator specialisation of GMM-ITS has been proven using BO [6,14], TDOA [1] and TDOA/FDOA [2] sensors. The initial and partial version of GMM-ITS is introduced in [15].…”

Section: Introductionmentioning

confidence: 99%

Non-linear automatic target tracking in clutter using dynamic Gaussian mixture

Mušicki

Song

Kim

et al. 2012

IET Radar Sonar Navig.

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This study presents a complete algorithm for single target tracking in clutter, which addresses simultaneously: nonlinear measurements; uncertain target detections; presence of random clutter measurements; and uncertain target existence. Proposed algorithm generalises the integrated track splitting (ITS) filter by extending the ITS functionality to highly nonlinear measurements. The non-linear target tracking and estimation problems may also be solved by application of particle filters, albeit incurring a significant computational expense relative to proposed solution. In an environment without data association uncertainties proposed filter becomes a non-linear estimator.

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Comparison of Recursive Algorithms for Emitter Localisation using TDOA Measurements from a Pair of UAVs

Cited by 58 publications

References 21 publications

A novel radar radiation control strategy based on passive tracking in multiple aircraft platforms

A novel radar radiation control strategy based on passive tracking in multiple aircraft platforms

Linear recursive passive target tracking filter for cooperative sea‐skimming anti‐ship missiles

Non-linear automatic target tracking in clutter using dynamic Gaussian mixture

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