Intent Inference with Path Prediction

Krozel, Jimmy; Andrisani, Dominick

doi:10.2514/1.14348

Cited by 40 publications

(19 citation statements)

References 7 publications

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“…In the following subsections, the constraints for each flight mode in the descent and approach phase are specified, and the corresponding continuous dynamics of the aircraft with wind disturbance are derived from the general equations of motion [Eqs. (4)(5)(6)(7)(8)] [17,18]. …”

Section: Continuous State Dynamics Of Aircraftmentioning

confidence: 99%

“…Because these models cannot accurately capture the maneuver uncertainty of the aircraft, they could cause large errors and only work for short look-ahead times. To improve the prediction accuracy, there have been efforts to incorporate flight intent information into prediction models [6][7][8]. If a specific flight intent is inferred from the current estimated states, then a specific prediction model corresponding to the inferred flight intent is chosen to predict the future trajectory.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid System Modeling and Estimation for Arrival Time Prediction in Terminal Airspace

Lee

Hwang

2016

Journal of Guidance, Control, and Dynamics

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Section: Continuous State Dynamics Of Aircraftmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid System Modeling and Estimation for Arrival Time Prediction in Terminal Airspace

Lee

Hwang

2016

Journal of Guidance, Control, and Dynamics

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“…The methods of flight intent inference based on the Maximum Likelihood principle have been proposed 11,15,16 where the most likely flight intent can be chosen as follows:Î…”

Section: Ib Previous Approaches For Conformance Monitoringmentioning

confidence: 99%

“…The aircraft which does not follow its flight plan, otherwise called non-conforming aircraft, is quite prevalent not only because the pilots misunderstand the controller's instructions via voice communications but also because the controllers do not update the currently issued clearances into their computer systems; in the latter case, a decision support tool may need to declare the aircraft as non-conforming its plan although the aircraft is following exactly to the voice clearance. Although more reliable and frequently updated information on flight plan is available in near future thank to the advanced data-link technologies such as Automatic Dependent Surveillance Broadcast (ADS-B), the needs for detecting non-conformance aircraft is still exist due to the following reasons: [10][11][12][13] 1) The filed or broadcasted flight plan information still needs to be independently verified for the safety and security reasons. Furthermore, an additional source of uncertainty on how the pilots actually execute the planned trajectory, e.g., "slow" to "fast" climb or descent to the cleared altitude, exists and needs to be identified.…”

Section: Ia 4-dimension Trajectory Prediction and Managementmentioning

confidence: 99%

A Bayesian Approach for Conformance Monitoring

Lee

Fukuda

2011

11th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference

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For safe and efficient management of air traffic, the prediction of aircraft trajectories into the future is necessary. Conformance monitoring is one of important functions in trajectory prediction to ensure that aircraft adhere to the assigned flight plan. One of challenges for timely detection of a non-conforming aircraft is that its trajectories cannot be fully specified due to uncertainty on how the aircraft executes the currently applied flight plan. This uncertainty, together with noisy measurements, can create a series of data poor situations which often results in a high level of false detection or detection delay. To overcome this problem, our paper proposes a new method of conformance monitoring by using a Bayesian formulation. A prior available information can be utilized in the proposed method to reduce false detections without an arbitrary increase in detection delay. Furthermore, the proposed method can provide actual probability of conformance which can bring various capabilities to trajectory prediction. The proposed method is illustrated with examples.Nomenclature z(k) measurement of aircraft's state at time k Z(k) a set of all measurements up to time k T CT trajectory change time for a non-conforming aircraft Ω a set two conformance statuses; ω 0 -conformance and ω 1 -non-conformance H a set of TCT hypotheses

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“…Moreover, the flight intention should also be considered. Krozel [8] proposed a method for inferring the intention and structured a process for obtaining the best fit to an intent model for observed aircraft motions, where the horizontal, vertical, and speed dimensions were investigated independently, and then combined to fully explain the three-dimensional guidance and navigation plan of an aircraft. Yepes [9] used a hybrid estimation algorithm to estimate the aircraft's state and flight mode, these estimates were then combined with the knowledge of air traffic control regulations, the aircraft's flight plan, and the environment to infer the pilot's intention.…”

Section: Introductionmentioning

confidence: 99%