Aircraft Identification Research Articles

Identification of large aircraft

Large commercial aircraft are much more easily identified by radar than are fighter aircraft, because the former's two-dimensional images have sufficient details to let one recognize the aircraft's shape. The usual problem with identification based on shape is lack of knowledge of the aspect angle. As an indication of the relative ease with which commercial aircraft can be identified, this note describes a shape-based identification method that does not require knowledge of the aspect angle.

Team Decision-Making Strategies: Implications for Designing the Interface in Complex Tasks

Two longitudinal experiments were designed to challenge teams performing a time-critical aircraft identification task (Team Argus). In the first experiment, it was predicted that teams would use different strategies to maintain performance across increasing levels of time pressure. However, at some point these strategies would become inadequate, and performance would not be recoverable. The identification of these critical temporal stress levels and strategies suggested interface design modifications that would help teams maintain performance longer. The findings obtained were then used to inform improved interface designs, that would mediate the effects of time pressure. The successfulness of these interface changes were evaluated in the second experiment, comparing team performance on the same task using perceptually-aided and cognitively-aided interfaces and the original un-aided interface.

Visual Aircraft Identification as a Pursuit-Evasion Game

Visual Aircraft Identification as a Pursuit-Evasion Game

Object identification as a function of discriminability and learning presentations: The effect of stimulus similarity and canonical frame alignment on aircraft identification.

Aircraft that were relatively similar (homogeneous) and relatively dissimilar (heterogeneous) in appearance were studied at orientations either consistent (canonical) or inconsistent (noncanonical) with the environmental frame of reference. At test, participants' identification performance was measured with stimuli rotated to novel orientations within the picture plane. During learning and testing, identification of heterogeneous aircraft was better than that of homogeneous aircraft. At test, only identification of homogeneous aircraft revealed a strong linear degradation of performance as angular disparity between the novel test orientations and the original learning orientations increased. During learning and testing, identification was better for aircraft studied at canonical orientations than for those studied at noncanonical orientations. The results are discussed in terms of object identification, aircraft recognition training, categorization, mental representations, and visual mental rotation.

Nonlinear identification and control of aircraft

The major goal of this paper is to approach and solve a spectrum of extremely important problems in nonlinear analysis, identification, optimization, and control of current and next generation advanced aircraft. High-performance aircraft must satisfy the required performances in the specified operational flight envelope at various attitudes and velocities, high-angle-of-attack regimes, all-weather, day and night operation. To improve mission effectiveness, ensure the specified flying and handling qualities (agility, manoeuvrability, controllability, and other pilotage quantities), guarantee survivability, damage adaptation and recovery, this paper reports a new, completely automated realtime motion control conceptual framework with failure accommodation features (self-repairing flight control). Innovative highly efficient identification and design methods are applied and verified. To attain the desired aircraft performance and expand the operational flight envelope at high-angle-of-attack regimes, identification and constrained optimization problems are solved using the developed motion control framework. An example is thoroughly studied to demonstrate the practical use and capabilities of the reported nonlinear design and identification procedures.

Object identification as a function of discriminability and learning presentations: the effect of stimulus similarity and canonical frame alignment on aircraft identification.

Aircraft that were relatively similar (homogeneous) and relatively dissimilar (heterogeneous) in appearance were studied at orientations either consistent (canonical) or inconsistent (noncanonical) with the environmental frame of reference. At test, participants' identification performance was measured with stimuli rotated to novel orientations within the picture plane. During learning and testing, identification of heterogeneous aircraft was better than that of homogeneous aircraft. At test, only identification of homogeneous aircraft revealed a strong linear degradation of performance as angular disparity between the novel test orientations and the original learning orientations increased. During learning and testing, identification was better for aircraft studied at canonical orientations than for those studied at noncanonical orientations. The results are discussed in terms of object identification, aircraft recognition training, categorization, mental representations, and visual mental rotation.

Aircraft identification from RCS measurement using an orthogonal transform

A comparative study on target identification using the radar cross section (RCS) signature of an aircraft in both the frequency domain and the range domain is conducted. A maximum likelihood method is employed to perform the identification process. Generalised likelihood identification when the received RCS signal is attenuated by an unknown amount is also examined. Target identification could be quite computationally intensive since a large number of library reference signatures may have to be searched to declare an identification. The use of an orthogonal transform is proposed to reduce the computational requirement. It is found that the discrete cosine transform is very effective in compacting the RCS signature in the frequency domain, and the Haar transform is more efficient in the range domain. The application of orthogonal transforms can reduce the computational complexity by at least 50% while maintaining the same identification accuracy.

Two practical systems for classification of three-dimensional objects

The main goals of practical vision systems are the fast and accurate recovery of the three-dimensional description of an object that produced a two-dimensional image. In this paper, we propose two practical systems (called VISION1 and VISION2) for the classification of three-dimensional objects using a set of their two-dimensional images that adhere to these goals. VISION1 is an on-line recognition system utilizing a colored TV camera and the necessary interface equipment to supply a two-dimensional image to a digital computer for processing and classification. It essentially relies on shape-from-X techniques to compute the depth. In addition it exploits automatic rotation of the object to generate a training sample set, and extraction of 12 two-dimensional invariant features to train an artificial neural network (ANN) with. Then VISION1 recalls its trained ANN for classification of objects. The proposed VISION1 is simple, fast, uses less man-power and provides storage space at lower cost than comparable vision systems (Sahibsingh, A.D., Kenneth, J.B., Robert, B.M., Aircraft identification by moment invariants. IEEE Transactions on computers, vol. c-26 (1), pp. 39–45). On the other hand, VISION2 system follows the classical approach of vision theory developed by Marr, D., 1982 (Vision – a Computational Investigation into the Human Presentation and Processing of Visual Information. Freeman, San Francisco, CA). VISION2 depends upon image segmentation algorithms, shape-from-X techniques, and the extraction of 11 three-dimensional invariant features to train an ANN with. Thus VISION2 is very efficient even more accurate than VISION1. Both VISION1 and VISION2 may be used in different manufacturing environments for classification of objects. Experimental testing was conducted through a sample test of 112 real images for four different classes of objects. The reported results support our claim that the two proposed systems are practical, fast and accurate.

Robust statistical feature based aircraft identification

The statistical feature based (StaF) classifier is presented for robust high range resolution (HRR) radar aircraft identification (ID). HRR signature peak features are selected the fly with no a priori assumptions about the number or location of the features. Features extracted depends on the information content of the observed signature making the number, location, and amplitude of features random variables. A primary goal for this research is to increase classifier robustness by maintaining high known target ID while minimizing unknown target errors. Results are presented demonstrating that the StaF classifier can significantly reduce errors associated with unknown targets while maintaining a high probability of correct classification.

State-space multivariable non-linear identification and control of aircraft

In the design of commercial and military aircraft, flying and handling qualities, as well as performance (agility and manoeuvrability, controllability and stability) and safety, have received increased emphasis. The aircraft must exhibit acceptable handling qualities while performing manoeuvres, and different flight management systems have been designed. For advanced aircraft, the assumption that longitudinal and lateral dynamics can be decoupled leads to poor flying and handling characteristics owing to longitudinal-lateral cross-coupling. Aircraft stability severely degraded at low speed, high-angle-of-attack flight and gain-scheduled linear control algorithms cannot provide the desired performance (gain-scheduled control laws are used because they are certifiable). These have resulted in the critical need for significant improvements in flying and handling qualities as well as expansion of the flight envelope at low-speed, high-angle-of-attack flight by using robust non-linear controllers. Military aircraft must be capable of performing a diverse range of operational missions, which include air-to-ground and air-to-air combat. Mission effectiveness relies on flight management systems and control algorithms. Control system effectiveness can be achieved as the designer integrates non-linear flight dynamics and advanced optimization methods to design multivariable control algorithms. In this paper a study is made of non-linear identification and control methods which can be applied to multivariable aircraft using measured variables. By performing non-linear modelling, it is shown that the methods reported lead to very encouraging results.

Examining the effects of communication training and team composition on the decision making of Patriot air defense teams

An experiment investigating the effect of communication training and three group composition variables was performed with Patriot air defense teams for two different types of aircraft identification tasks. It was predicted that communication training would significantly enhance communication quantity and quality and, in turn, team performance for both tasks. Although the training did sometimes improve team communication processes, it did not improve team performance. The variable that had the biggest positive effect on communication quality and team performance was the number of hours a team had worked together. This effect was only found, however, for the type of task for which Patriot teams routinely train. It did not transfer to the less frequent and more cognitively stressing task where there is conflicting information about unknown aircraft, as in the USS Vincennes tragedy.

A target identification comparison of Bayesian and Dempster-Shafer multisensor fusion

This paper demonstrates how Bayesian and evidential reasoning can address the same target identification problem involving multiple levels of abstraction, such as identification based on type, class, and nature. In the process of demonstrating target identification with these two reasoning methods, we compare their convergence time to a long run asymptote for a broad range of aircraft identification scenarios that include missing reports and misassociated reports. Our results show that probability theory can accommodate all of these issues that are present in dealing with uncertainty and that the probabilistic results converge to a solution much faster than those of evidence theory.

Positive identification of aircraft on airport movement area-results of FAA trials

Current operational airport surface surveillance systems do not positively identify aircraft as unique targets. Air traffic controllers are instead presented with a primary radar picture showing all traffic on the airport movement area. The Federal Aviation Administration (FAA) is evaluating different types of systems that will add the aircraft's call-sign or flight number to the radar image thereby allowing controllers to positively identify individual aircraft at all times. This paper describes the development, implementation, and testing of the airport surface target identification system (ATIDS), and presents results of initial trials conducted at Atlanta Hartsfield International Airport.

X-31A system identification using single-surface excitation at high angles of attack

Parameter identification of the X-31A experimental aircraft was conducted throughout the envelope expansion using mainly pilot input maneuvers. The quality of the identification results, especially in the high-angle-of-attack regime, suffered from high correlations between the aircraft controls and states as well as from insufficient sideslip excitation. These problems are caused by the high feedback gains used in the flight control system to control the basically unstable airplane. Therefore, in 1994-1995 a dedicated flight test program using single surface excitation for parameter identification at high angles of attack was conducted. The results prove that identification of highly augmented aircraft benefits considerably from this technique. This article presents the realization of the single-surface excitation using the X-31A flutter test box. Also, the flight test program, the applied evaluation methods, and the identification models are discussed. Selected identification results from the single-surface excitation tests are presented and compared to those obtained from the pilot input maneuvers.

Modeling aircraft departure profile using ARTS radar data

The current method for the modeling of aircraft departure profiles is to use the standard departure profiles contained in the INM noise model. The INM model accounts for the different departure gradients based upon the flight distance to the aircraft’s destination. Aircraft flying a longer distance are assumed to be heavier and as a result, would climb at a lower gradient. New techniques involve using the actual flight profiles from the FAA’s ARTS radar data. State‐of‐the‐art airport noise monitoring systems collect flight track data [Aircraft identification, Type of operation, and aircraft position (X, Y and Z)] from the ARTS data. This information can be processed to determine the actual flight profiles for each aircaft type as well as a range of profiles for a specific aircraft. Using this technique, the departure profiles used in the model account for not only the departure distance, but also pilot techniques, the actual aircraft load as well as meteorological conditions such as altitude, density, and wind speed. The result is that the modeling of aircraft departure flight profiles are based upon actual data specific to the flights and conditions at the airport under study.

F.C. Williams

F.C. Williams spent his whole professional life in inventive research. He defined invention as a forward step which is not taken as the result of logical processes on the available data. In his career there were many such forward steps, including inventing circuits for aircraft identification using radar, the computer storage medium used extensively in the early days of computer memory, the structure of the operational amplifier, variable-speed machines and speed control systems. This paper descibes the work of F.C. Williams.

An equation decoupling technique for identification of unstable aircraft with unsteady aerodynamic modelling

AbstractA simplified trailing vortex concept has been used to model unsteady aerodynamic effects onto the mathematical model for an augmented unstable aircraft. An output error method with, and without, an equation decoupling technique in the frequency domain has been used for the estimation of parameters. The two approaches of estimation are compared for the cases where unsteady effects are included or excluded from the estimation model. It is shown that the incorporation of the equation decoupling technique makes the estimation algorithm robust with respect to initial guess values of the parameters. Finally, the inclusion of unsteady aerodynamics is shown to reduce the variation in the parameter estimates due to different control input forms.

Aircraft failure detection and identification using neural networks

In this paper, a neural network is proposed as an approach to the task of failure detection following damage to an aerodynamic surface of an aircraft flight control system. Several drawbacks of other failure detection techniques can be avoided by taking advantage of the flexible learning and generalization capabilities of a neural network. This structure, used for state estimation purposes, can be designed and trained on line in flight and generates a residual signal indicating the damage as soon as it occurs. From an analysis of the cross-correlation functions between some key state variables, the identification of the damage type can also be achieved. The results of a nonlinear numerical simulation for a damaged control surface are reported and discussed.

Correlation filters for aircraft identification from radar range profiles

The potential for identifying aircraft using one or more radar range profiles, in conjunction with a correlator, is investigated. Two types of filter which maximize the expected value of certain correlation peaks are described. The effectiveness of one type of filter was investigated in identification experiments using an extensive data set of real radar range profiles of 24 different aircraft. The results suggest that reliable identification is possible provided aircraft aspect information is used and identifications are based on multiple profiles. >

Comment on 'Equation decoupling - A new approach to the aerodynamic identification of unstable aircraft'

where cr ? is the variance of the measurement noise ny for ; = 1,2,3,4. The process noise will effect the resulting estimates. Accurate results cannot be obtained unless the measurement noise nt is very small and can be neglected. Real flight test data often contains measurement noise that cannot be neglected. The authors give the following suggestion: First, the equation decoupling technique is used to get Eq. (3), then, if the measurement noise cannot be neglected, a filter error method is applied to identify the parameters. Such a procedure makes use of the advantage of the reduction of the complexity of the sensitivity functions. Although this procedure is more time consuming and complex than Ref. 1, accurate results can be obtained.