Thank you much bro. I'll datamining my old archives and try to publish the tech or links how it can be used. Sagol.
Friends from Eskisehir Base told me sometime somewhere that we can watch almost to Gibraltar and boarders of Atlantic Ocean.
@cabatli_53 I will try to get in contact with Pieotr, I just saw that they have a global patent already.
They began to work on it more then 10 years ago.
Summary:
This work summarizes the author's research on radar applications of methods resulting from the assumption of signal sparsity. The term sparsity means that a signal under investigation may be modeled with a small number of components taken from a large dictionary. This property makes it possible to employ a new class of mathematical methods, recently made known as Compressive Sensing framework, for recovering the signal from the measured samples. The main feature of sparsity-based methods is that they can recover a signal uniquely from much fewer samples than methods derived from the classical sampling theory. However, this is possible only if me sparse model is adequate and if the model dictionary and measurement process conform to the specific requirements of the mathematical framework. In the present work, the author demonstrates how the mathematical theory of sparse representation and recovery may be applied to practical problems arising in radar signal processing. An overall purpose of radar signal processing is to acquire the knowledge of the radar scene from the received echo of a radio frequency signal which illuminates the investigated area. This is a problem generally belonging to the class of inverse problems, which may be ill-conditioned and ambiguous. The assumption of the sparse model of the received signal is an innovative idea that opens new possibilities of resolving ambiguities. The aim of this work was to demonstrate by means of practical examples that sparse reconstruction methods are capable of solving a series of important problems in different areas of radar signal processing. Also, more detailed research was done on these cases, including the study on sampling requirements as well as simulations of the algorithms used. The ideas and methods were verified with the use of live recorded signals wherever possible. In the examples presented in this work, sparsity of the signal model is the key assumption to enable the solution of relevant inverse problems. The application areas described here are closely related to the author's experience with existing radar systems, including those currently under research or development at the Warsaw University of Technology. They cover a wide range of radar types and processing modes, including active and passive radars as well as surveillance and imaging ones. The author proposed applications of sparsity-based methods for active radars with a noise waveform, for classical MTI radars, and for imaging radars, using either die synthetic aperture (SAR) technique with noise illumination, or the inverse synthetic aperture (ISAR) technique with passive illumination from a GSM transmitter. The ideas and methods were verified with the use of live recorded signals wherever possible. In the examples presented in this work, sparsity of the signal model is the key assumption to enable the solution of relevant inverse problems. The application areas described here are closely related to the author's experience with existing radar systems, including those currently under research or development at the Warsaw University of Technology. They cover a wide range of radar types and processing modes, including active and passive radars as well as surveillance and imaging ones. The author proposed applications of sparsity-based methods for active radars with a noise waveform, for classical MTI radars, and for imaging radars, using either die synthetic aperture (SAR) technique with noise illumination, or the inverse synthetic aperture (ISAR) technique with passive illumination from a GSM transmitter. The ideas and methods were verified with the use of live recorded signals wherever possible. In the examples presented in this work, sparsity of the signal model is the key assumption to enable the solution of relevant inverse problems. The application areas described here are closely related to the author's experience with existing radar systems, including those currently under research or development at the Warsaw University of Technology. They cover a wide range of radar types and processing modes, including active and passive radars as well as surveillance and imaging ones. The author proposed applications of sparsity-based methods for active radars with a noise waveform, for classical MTI radars, and for imaging radars, using either die synthetic aperture (SAR) technique with noise illumination, or the inverse synthetic aperture (ISAR) technique with passive illumination from a GSM transmitter. The ideas and methods were verified with the use of live recorded signals wherever possible. In the examples presented in this work, sparsity of the signal model is the key assumption to enable the solution of relevant inverse problems. The application areas described here are closely related to the author's experience with existing radar systems, including those currently under research or development at the Warsaw University of Technology. They cover a wide range of radar types and processing modes, including active and passive radars as well as surveillance and imaging ones. The author proposed applications of sparsity-based methods for active radars with a noise waveform, for classical MTI radars, and for imaging radars, using either die synthetic aperture (SAR) technique with noise illumination, or the inverse synthetic aperture (ISAR) technique with passive illumination from a GSM transmitter. The ideas and methods were verified with the use of live recorded signals wherever possible. In the examples presented in this work, sparsity of the signal model is the key assumption to enable the solution of relevant inverse problems. The application areas described here are closely related to the author's experience with existing radar systems, including those currently under research or development at the Warsaw University of Technology. They cover a wide range of radar types and processing modes, including active and passive radars as well as surveillance and imaging ones. The author proposed applications of sparsity-based methods for active radars with a noise waveform, for classical MTI radars, and for imaging radars, using either die synthetic aperture (SAR) technique with noise illumination, or the inverse synthetic aperture (ISAR) technique with passive illumination from a GSM transmitter.The ideas and methods were verified with the use of live recorded signals wherever possible. In the examples presented in this work, sparsity of the signal model is the key assumption to enable the solution of relevant inverse problems. The application areas described here are closely related to the author's experience with existing radar systems, including those currently under research or development at the Warsaw University of Technology. They cover a wide range of radar types and processing modes, including active and passive radars as well as surveillance and imaging ones. The author proposed applications of sparsity-based methods for active radars with a noise waveform, for classical MTI radars, and for imaging radars, using either die synthetic aperture (SAR) technique with noise illumination, or the inverse synthetic aperture (ISAR) technique with passive illumination from a GSM transmitter. The ideas and methods were verified with the use of live recorded signals wherever possible. In the examples presented in this work, sparsity of the signal model is the key assumption to enable the solution of relevant inverse problems. The application areas described here are closely related to the author's experience with existing radar systems, including those currently under research or development at the Warsaw University of Technology. They cover a wide range of radar types and processing modes, including active and passive radars as well as surveillance and imaging ones. The author proposed applications of sparsity-based methods for active radars with a noise waveform, for classical MTI radars, and for imaging radars, using either die synthetic aperture (SAR) technique with noise illumination, or the inverse synthetic aperture (ISAR) technique with passive illumination from a GSM transmitter. vThe ideas and methods were verified with the use of live recorded signals wherever possible. In the examples presented in this work, sparsity of the signal model is the key assumption to enable the solution of relevant inverse problems. The application areas described here are closely related to the author's experience with existing radar systems, including those currently under research or development at the Warsaw University of Technology. They cover a wide range of radar types and processing modes, including active and passive radars as well as surveillance and imaging ones. The author proposed applications of sparsity-based methods for active radars with a noise waveform, for classical MTI radars, and for imaging radars, using either die synthetic aperture (SAR) technique with noise illumination, or the inverse synthetic aperture (ISAR) technique with passive illumination from a GSM transmitter. The ideas and methods were verified with the use of live recorded signals wherever possible. In the examples presented in this work, sparsity of the signal model is the key assumption to enable the solution of relevant inverse problems. The application areas described here are closely related to the author's experience with existing radar systems, including those currently under research or development at the Warsaw University of Technology. They cover a wide range of radar types and processing modes, including active and passive radars as well as surveillance and imaging ones. The author proposed applications of sparsity-based methods for active radars with a noise waveform, for classical MTI radars, and for imaging radars, using either die synthetic aperture (SAR) technique with noise illumination, or the inverse synthetic aperture (ISAR) technique with passive illumination from a GSM transmitter. The ideas and methods were verified with the use of live recorded signals wherever possible. In the examples presented in this work, sparsity of the signal model is the key assumption to enable the solution of relevant inverse problems. The application areas described here are closely related to the author's experience with existing radar systems, including those currently under research or development at the Warsaw University of Technology. They cover a wide range of radar types and processing modes, including active and passive radars as well as surveillance and imaging ones. The author proposed applications of sparsity-based methods for active radars with a noise waveform, for classical MTI radars, and for imaging radars, using either die synthetic aperture (SAR) technique with noise illumination, or the inverse synthetic aperture (ISAR) technique with passive illumination from a GSM transmitter.The ideas and methods were verified with the use of live recorded signals wherever possible. In the examples presented in this work, sparsity of the signal model is the key assumption to enable the solution of relevant inverse problems. The application areas described here are closely related to the author's experience with existing radar systems, including those currently under research or development at the Warsaw University of Technology. They cover a wide range of radar types and processing modes, including active and passive radars as well as surveillance and imaging ones. The author proposed applications of sparsity-based methods for active radars with a noise waveform, for classical MTI radars, and for imaging radars, using either die synthetic aperture (SAR) technique with noise illumination, or the inverse synthetic aperture (ISAR) technique with passive illumination from a GSM transmitter. The ideas and methods were verified with the use of live recorded signals wherever possible. In the examples presented in this work, sparsity of the signal model is the key assumption to enable the solution of relevant inverse problems. The application areas described here are closely related to the author's experience with existing radar systems, including those currently under research or development at the Warsaw University of Technology. They cover a wide range of radar types and processing modes, including active and passive radars as well as surveillance and imaging ones. The author proposed applications of sparsity-based methods for active radars with a noise waveform, for classical MTI radars, and for imaging radars, using either die synthetic aperture (SAR) technique with noise illumination, or the inverse synthetic aperture (ISAR) technique with passive illumination from a GSM transmitter.
For the Kalkan 3D Air Defence Radar, 120 km already mentioned by Aselsan engineer at idef2015
