Precision tracking of ground targets

Shea, Peter; Zadra, T.; Klamer, Dale M.; Frangione, E.; Brouillard, Rebecca; Kastella, Keith

doi:10.1109/aero.2000.879873

Cited by 42 publications

(30 citation statements)

References 10 publications

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“…With the desire for more accurate target tracking (Smith and Singh, 2006) and the incorporation of terrain information into tracking systems (Shea et al, 2000) the use of Gaussian models for target tracks and their associated uncertainties became less desirable. While new methods for incorporating non-Gaussian measurements into track states were being developed, different metrics and association routines were being developed for measurements and tracks that were modeled as generic probability density functions.…”

Section: Data Association Using Fuzzy Logicmentioning

confidence: 99%

Data Association Techniques for Non-Gaussian Measurements

Stubberud¹,

Kramer²

2011

Sensor Fusion - Foundation and Applications

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Section: Data Association Using Fuzzy Logicmentioning

confidence: 99%

Data Association Techniques for Non-Gaussian Measurements

Stubberud¹,

Kramer²

2011

Sensor Fusion - Foundation and Applications

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“…Note that no approximations whatsoever were made in deriving (19)-(23), i.e., this method, unlike [18,10,16], is exact. As a special case of the above, where the measurement matrices H i (k) are same for different local trackers, we have the following simplifications:…”

Section: The Pseudomeasurement Of the Bias Vectormentioning

confidence: 99%

“…In order to avoid an augment state Kalman filter, which can be computational infeasible, the decoupled Kalman filtering is commonly used, as in [18,10,16]. The decoupled Kalman filtering used in [18] consists of the following three steps when the bias is modeled by the dynamic equation (35).…”

Section: The Decoupled Kalman Filteringmentioning

confidence: 99%

Exact multisensor dynamic bias estimation with local tracks

Xiao

Bar‐Shalom

Kirubarajan

2003

Sixth International Conference of Information Fusion, 2003. Proceedings of The

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Abstract-This paper provides the exact solution of multiple sensor bias estimation problem based on local tracks. It is shown that the bias estimate can be obtained dynamically using the outputs of the local (biased) state estimators. This is accomplished by manipulating the local state estimates such that they yield pseudomeasurements of the sensor biases with additive noises that are zero-mean, white and with easily calculated covariances. These results allow evaluation of the Cramer-Rao Lower Bound (CRLB) on the covariance of the bias estimate, i.e., the quantification of the available information about the biases in any scenario. Monte Carlo simulations show that this method has significant improvement of performance with reducing the RMS errors about 60-80% comparing to the commonly used decoupled Kalman filtering method. Furthermore, the new method is shown be statistically efficient, i.e., it meets the CRLB. The extension of the new technique for dynamically varying biases is also presented.

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“…Target tracking with road network information requires methodologies which can keep the inherent multi-modality of the underlying probability densities. The first attempts [36][37][38] used the jump Markov (non)linear systems in combination with the variable structure interacting multiple model (VS-IMM) algorithm [39,40]. Important alternatives to IMM based methods appear in [41,42] which propose variable structure multiple model particle filters (VS-MMPF) where road constraints are handled using the concept of directional process noise.…”

Section: Background and Literature Surveymentioning

confidence: 99%

Road Target Search and Tracking with Gimballed Vision Sensor on an Unmanned Aerial Vehicle

et al. 2012

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This article considers a sensor management problem where a number of road bounded vehicles are monitored by an unmanned aerial vehicle (UAV) with a gimballed vision sensor. The problem is to keep track of all discovered targets and simultaneously search for new targets by controlling the pointing direction of the vision sensor and the motion of the UAV. A planner based on a state-machine is proposed with three different modes; target tracking, known target search, and new target search. A high-level decision maker chooses among these sub-tasks to obtain an overall situational awareness. A utility measure for evaluating the combined search and target tracking performance is also proposed. By using this measure it is possible to evaluate and compare the rewards of updating known targets versus searching for new targets in the same framework. The targets are assumed to be road bounded and the road network information is used both to improve the tracking and sensor management performance. The tracking and search are based on flexible target density representations provided by particle mixtures and deterministic grids.

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Precision tracking of ground targets

Cited by 42 publications

References 10 publications

Data Association Techniques for Non-Gaussian Measurements

Data Association Techniques for Non-Gaussian Measurements

Exact multisensor dynamic bias estimation with local tracks

Road Target Search and Tracking with Gimballed Vision Sensor on an Unmanned Aerial Vehicle

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