The Radar Support System (RSS): A tool for siting radars and predicting their performance

O'Hern, B.; Mack, Jeffrey; Pieramico, Alan; Rugger, D.; Moyer, Lee R.

doi:10.1109/nrc.1997.588306

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Section: Introductionmentioning

confidence: 99%

“…There are systems that predict the performances of sensors based on their modeling. Some current examples of systems of this type are the computer-aided radar performance evaluation tool (CARPET) for assessment and design of surfacebased radars or the Radar Support System (RSS) for siting radars and predicting their performance [4].Other systems aim at evaluating the actual operational behavior of a sensor, which in some cases implies the use of dedicated hardware for their evaluations. In this group we can find radar analysis support system for sites (RASS-S), a radar manufacturer-independent system for evaluating the different elements of radar, or a radar monitoring display (RMD), which also has the capability of processing any surveillance data.…”

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ATC trajectory reconstruction for automated evaluation of sensor and tracker performance

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Trescastro-López

Miguel

et al. 2013

IEEE Aerosp. Electron. Syst. Mag.

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Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. INTRODUCTIONCurrently most air traffic controller decisions are based on the information provided by the ground support tools provided by automation systems, based on a network of surveillance sensors and the associated tracker. To guarantee surveillance integrity, it is clear that performance assessments of the different elements of the surveillance system are necessary. Due to the evolution suffered by the surveillance processing chain in the recent past, its complexity has been increased by the integration of new sensor types (e.g., automatic dependent surveillance-broadcast [ADS-B] [1], Mode S radars [2], and wide area multilateration [WAM] [3]), data link applications, and networking technologies. With new sensors, there is a need for system-level performance evaluations as well as methods for establishing assessment at each component of the tracking evaluation.Initially, the focus of surveillance assessment was placed on the performance evaluation of sensors (mainly radars). There are systems that predict the performances of sensors based on their modeling. Some current examples of systems of this type are the computer-aided radar performance evaluation tool (CARPET) for assessment and design of surfacebased radars or the Radar Support System (RSS) for siting radars and predicting their performance [4].Other systems aim at evaluating the actual operational behavior of a sensor, which in some cases implies the use of dedicated hardware for their evaluations. In this group we can find radar analysis support system for sites (RASS-S), a radar manufacturer-independent system for evaluating the different elements of radar, or a radar monitoring display (RMD), which also has the capability of processing any surveillance data. In general, those approaches are extensions of radar data acquisition converters (RADAC) such as the one described in [5], which is a radar data acquisition and analysis platform.The complete assessment of multisensor surveillance system performance is a complex problem. In fact, there is not a single figure of merit describing the quality of such a system as described in [6] or [7]. So assessing it would need defining metrics over different aspects as C sensor detection (false alarms, splits, losses of detections, measurement rates, sensor coverage, etc.);C sensor measurement error (biases, noise variances, etc.);C monosensor and multisensor tracks initiation (initiation delays, multisensor system coverage, etc.);C association (incorrect associations);C tracking accuracy (track RMS errors in horizontal and vertical directions, maneuvering biases);C tracking robustness (appearance of gaps, track splitting, near track mixing, etc.);C mode of flight (MoF) detection (uni...

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%