ADS-B provides convenient means of air traffic control (ATC) for its low cost and simple ground station hardware. In a low percentage of cases, aircraft positions transmitted via ADS-B are error prone due to e.g. faulty wiring with onboard navigation systems. In the proposed approach, direction of arrival (DOA) estimation is used to verify ADS-B airborne positions. The potential positioning error of ADS-B is thereby evaluated by comparing DOA estimates to DOA values calculated from ADS-B references and the sensor position and orientation. To prove the accuracy of the applied DOA estimation sensor, an additional measurement campaign using a dedicated measurement aircraft has been conducted. I . I N T R O D U C T I O NDuring the last few decades a significant increase in air traffic is recognized. The high traffic load needs to be carefully coordinated by air traffic control (ATC) to satisfy rigorous security demands.To provide high-quality ATC, its operators are depending on information gathered by radar sensors. Classic primary surveillance radar (PSR) approaches require a large number of expensive and energy consuming ground stations. In order to cut down the number of primary radar stations, the nondependent use of secondary surveillance radar (SSR) transponders for aircraft positioning is evaluated by ATC organizations.The automatic-dependent surveillance broadcast (ADS-B) is based on the SSR Mode S protocol. Unlike a regular SSR system, which broadcasts radio telegrams mainly on prior request by ground stations, ADS-B uses spontaneous transponder broadcasts triggered randomly based on the Aloha-Protocol. Offering more information than just altitude and identification, ADS-B also transmits the carrying aircraft's position as it is gathered by its onboard navigation system. Additionally, ground speed, heading, and many other information are provided. As the number of aircraft equipped with ADS-B is rising (currently 65% of Mode S equipped aircraft [1]), the system becomes increasingly attractive to feed ATC displays.According to field studies [2], the most part of ADS-B transponders are broadcasting reliable positioning information, where positions' root mean-squared error (RMSE) is following a Rayleigh distribution with a mean value of around 250. This would on the one hand mean an acceptable error for monitoring en route traffic. On the other hand, there are some transponders that produce much larger errors. One possible problem leading to those large errors is improper wiring of the ADS-B transponder with the onboard navigation system or inertial measurement unit (IMU). This is one of the reasons why a standalone ADS-B solution without ground-based validation techniques is not considered reliable enough for ATC. Nevertheless, due to its immense cost the installation of PSR, SSR or multilateration is sometimes not economically justifiable. As shown in [3], the risk of relying on a pure ADS-B system is taken in such a case by NAV CANADA at Hudson Bay.The situation changes if the ADS-B ground station has the ...
The present paper presents a comparison of compression algorithms using the Discrete Cosine Transform -DCT (JPEG) and Discrete Wavelet Transform -DWT applied to remotely sensed images. The statistical behaviors of the DCT and DWT are addressed and the implications for the performance of the image compression algorithms are compared for optical and SAR images. These SAR images were despeckled during compression. Qualitative and quantitative results are presented. PRINCIPLES OF TRANSFORM CODINGTransform coding of signals is a lossy compression technique used by both JPEG and wavelet compression. .It is assumed that the original signal space is mapped through a given transformation operator into a new space characterized by low correlation of the transform samples and by maximum concentration of the energy in the fewest number of transform samples. Among the transformations, the orthogonal transformations are of primary interest due to their entropy conservation property. Any subsequent data compression and coding is performed in the transformed space. During compression, the coefficients representing the signal are quantized. This is lossy operation, the error is controlled by appropriate quantization. Within the next step, the quantized coefficients are scanned in an order to allow optimum zero run length coding. In the last step, an entropy coding is performed. The optimum quantization and scanning strategies depend on the properties of the applied transform and the statistics of the signal. The decompression is a signal reconstruction technique requiring an inverse transformation to recover the original representation. The transform coded images suffer from transform specific degradations. JPEGFor our investigation, the DCT will be analyzed based on its relationship with the Karhunen -Loeve Transformation (KLT). The KLT is the optimal transformation that fulfills the condition for transform coding: it completely decorrelates the signal samples in the transformed space, provides the minimum MSE during data compression by discarding the high index coefficients in the transformed space, maximizes the energy in the fewest number of transformed coefficients, and it is an orthogonal transformation. The matrix of the KLT has as columns the eigenvectors of the autocovariance matrix of the signal. The KLT is signal dependent and has no fast algorithm implementation. For this reason, suboptimal transformations that allow fast implementations are of interest. Considering further the class of stationary Markov-1 signals, the DCT matrix is defined as the limit of the KLT matrix when the correlation coefficient of the Markov process asymptotically tends to 1. For a correlation coefficient equal to 1, there is only one nonzero eigenvalue, the rank zero eigenvalue. The MSE is concentrated into one single DCT coefficient. If the correlation deviates from. 1, the MSE will be spread gradually among the other eigenvalues. This explains the asymptotic optimal performance of the DCT when applied to stationary Markov-1 signals...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.