Advanced signal processing tools for ballistic missile defence and space situational awareness

The research presented in this Thesis deals with signal processing algorithms for the classification of sensitive targets for defence applications and with novel solutions for the detection of space objects. These novel tools include classification algorithms for Ballistic Targets (BTs) from both micro-Doppler (mD) and High Resolution Range Profiles (HRRPs) of a target, and a space-borne Passive Bistatic Radar (PBR) designed for exploiting the advantages guaranteed by the Forward Scattering (FS) configuration for the detection and identification of targets orbiting around the Earth.;Nowadays the challenge of the identification of Ballistic Missile (BM) warheads in a cloud of decoys and debris is essential in order to optimize the use of ammunition resources. In this Thesis, two different and efficient robust frameworks are presented. Both the frameworks exploit in different fashions the effect in the radar return of micro-motions exhibited by the target during its flight.;The first algorithm analyses the radar echo from the target in the time-frequency domain, with the aim to extract the mD information. Specifically, the Cadence Velocity Diagram (CVD) from the received signal is evaluated as mD profile of the target, where the mD components composing the radar echo and their repetition rates are shown.;Different feature extraction approaches are proposed based on the estimation of statistical indices from the 1-Dimensional (1D) Averaged CVD (ACVD), on the evaluation of pseudo-Zerike (pZ) and Krawtchouk (Kr) image moments and on the use of 2-Dimensional (2D) Gabor filter, considering the CVD as 2D image. The reliability of the proposed feature extraction approaches is tested on both simulated and real data, demonstrating the adaptivity of the framework to different radar scenarios and to different amount of available resources.;The real data are realized in laboratory, conducting an experiment for simulating the mD signature of a BT by using scaled replicas of the targets, a robotic manipulator for the micro-motions simulation and a Continuous Waveform (CW) radar for the radar measurements.;The second algorithm is based on the computation of the Inverse Radon Transform (IRT) of the target signature, represented by a HRRP frame acquired within an entire period of the main rotating motion of the target, which are precession for warheads and tumbling for decoys. Following, pZ moments of the resulting transformation are evaluated as final feature vector for the classifier. The features guarantee robustness against the target dimensions and the initial phase and the angular velocity of its motion.;The classification results on simulated data are shown for different polarization of the ElectroMagnetic (EM) radar waveform and for various operational conditions, confirming the the validity of the algorithm.The knowledge of space debris population is of fundamental importance for the safety of both the existing and new space missions. In this Thesis, a low budget solution to detect and possibly track space debris and satellites in Low Earth Orbit (LEO) is proposed.;The concept consists in a space-borne PBR installed on a CubeSaT flying at low altitude and detecting the occultations of radio signals coming from existing satellites flying at higher altitudes. The feasibility of such a PBR system is conducted, with key performance such as metrics the minimumsize of detectable objects, taking into account visibility and frequency constraints on existing radio sources, the receiver size and the compatibility with current CubeSaT's technology.;Different illuminator types and receiver altitudes are considered under the assumption that all illuminators and receivers are on circular orbits. Finally, the designed system can represent a possible solution to the the demand for Ballistic Missile Defence (BMD) systems able to provide early warning and classification and its potential has been assessed also for this purpose.The research presented in this Thesis deals with signal processing algorithms for the classification of sensitive targets for defence applications and with novel solutions for the detection of space objects. These novel tools include classification algorithms for Ballistic Targets (BTs) from both micro-Doppler (mD) and High Resolution Range Profiles (HRRPs) of a target, and a space-borne Passive Bistatic Radar (PBR) designed for exploiting the advantages guaranteed by the Forward Scattering (FS) configuration for the detection and identification of targets orbiting around the Earth.;Nowadays the challenge of the identification of Ballistic Missile (BM) warheads in a cloud of decoys and debris is essential in order to optimize the use of ammunition resources. In this Thesis, two different and efficient robust frameworks are presented. Both the frameworks exploit in different fashions the effect in the radar return of micro-motions exhibited by the target during its flight.;The first algorithm analyses the radar echo from the target in the time-frequency domain, with the aim to extract the mD information. Specifically, the Cadence Velocity Diagram (CVD) from the received signal is evaluated as mD profile of the target, where the mD components composing the radar echo and their repetition rates are shown.;Different feature extraction approaches are proposed based on the estimation of statistical indices from the 1-Dimensional (1D) Averaged CVD (ACVD), on the evaluation of pseudo-Zerike (pZ) and Krawtchouk (Kr) image moments and on the use of 2-Dimensional (2D) Gabor filter, considering the CVD as 2D image. The reliability of the proposed feature extraction approaches is tested on both simulated and real data, demonstrating the adaptivity of the framework to different radar scenarios and to different amount of available resources.;The real data are realized in laboratory, conducting an experiment for simulating the mD signature of a BT by using scaled replicas of the targets, a robotic manipulator for the micro-motions simulation and a Continuous Waveform (CW) radar for the radar measurements.;The second algorithm is based on the computation of the Inverse Radon Transform (IRT) of the target signature, represented by a HRRP frame acquired within an entire period of the main rotating motion of the target, which are precession for warheads and tumbling for decoys. Following, pZ moments of the resulting transformation are evaluated as final feature vector for the classifier. The features guarantee robustness against the target dimensions and the initial phase and the angular velocity of its motion.;The classification results on simulated data are shown for different polarization of the ElectroMagnetic (EM) radar waveform and for various operational conditions, confirming the the validity of the algorithm.The knowledge of space debris population is of fundamental importance for the safety of both the existing and new space missions. In this Thesis, a low budget solution to detect and possibly track space debris and satellites in Low Earth Orbit (LEO) is proposed.;The concept consists in a space-borne PBR installed on a CubeSaT flying at low altitude and detecting the occultations of radio signals coming from existing satellites flying at higher altitudes. The feasibility of such a PBR system is conducted, with key performance such as metrics the minimumsize of detectable objects, taking into account visibility and frequency constraints on existing radio sources, the receiver size and the compatibility with current CubeSaT's technology.;Different illuminator types and receiver altitudes are considered under the assumption that all illuminators and receivers are on circular orbits. Finally, the designed system can represent a possible solution to the the demand for Ballistic Missile Defence (BMD) systems able to provide early warning and classification and its potential has been assessed also for this purpose

Numerical and experimental comparison among a new hybrid FT-music technique and existing algorithms for through-the-wall radar imaging

A fast low-cost through-the-wall radar imaging (TWRI) system, based on a vector network analyzer (VNA), a couple of switches and an array of Vivaldi antennas, has been designed, realized, and tested. To solve the TWRI inversion problem, an original theoretical modeling for a class of TWRI techniques whose basic functions are the cross-range Fourier transform (FT) of the scattered field and its covariance operator has been proposed. Using these functions, four conventional algorithms, namely the delay and sum (DAS), the FT, the multiple signal classification (MUSIC), the hybrid DAS-MUSIC and a new algorithm, the hybrid FT-MUSIC, have been derived. All these techniques have been implemented and their accuracy and field of view have been tested on canonical scatterers. Then, the algorithms have been applied to measured data collected in different scenarios constituted by a metallic bar or a human subject in the absence and in the presence of a wall between the antenna and the considered targets. Using the proposed TWRI system, it has been possible to detect a subject located up to 5-m away from the radar antenna array through a tuff wall. The proposed FT-MUSIC algorithm has evidenced performances similar to those of the DAS-MUSIC but with significantly lower execution times. Finally, FT-MUSIC performances in terms of field of view and immunity to disturbances are better compared to those of the MUSIC algorithm

Study of processing techniques for radar non-cooperative target recognition.

Radar is a powerful tool for detecting and tracking airborne targets such as aircraft and missiles by day and night. Nowadays, it is seen as a genuine solution to the problem of target recognition. Recent events showed that cooperative means of identification such as the IFF transponders carried by most aircraft are not entirely reliable and can be switched off by terrorists. For this reason, it is important that target identification be obtained through measurements and reconnaissance based on non-cooperative techniques. In practice, recognition is achieved by comparing the electromagnetic sig nature of a target to a set of others previously collected and stored in a library. Such signatures generally represent the targets reflectivity as a function of space. A common representation is known as one-dimensional high-resolution range-profile (HRRP) and can be described as the projection of the reflectivity along the direction of propagation of the wave. When the measured signature matches a template, the target is identified. The main drawback of this technique is that signatures greatly vary with aspect-angle so that measurements must be made for many angles and in three dimensions. This implies a potentially large cost as large datasets must be created, stored and processed. Besides, any modification of the target structure may yield incorrect classification results. Instead, other processing techniques exist that rely on recent mathematical algorithms. These techniques can be used to extract target features directly from the radar data. Because of the direct relation with target geometry, these feature-based methods seem to be suitable candidates for reducing the need of large databases. However, their performances and their domains of validity are not known. This is especially true when it comes to real targets for at least three reasons. First, the performance of the methods varies with the signal-to-noise ratio. Second, man-made targets arc often more complex than just a set of independent theoretical point-like scatterers. Third, these targets are made up of a large number of scattering elements so that mathematical assumptions are not met. In conclusion, the physical correctness of the computational models are questionable. This thesis investigates the processing techniques that can be used for non-cooperative target recognition. It demonstrates that the scattering-centre extraction is not suitable for the model-based approach. In contrast, it shows that the technique can be used with the feature-based approach. In particular, it investigates the recognition when achieved directly in the z-domain and proposes a novel algorithm that exploits the information al ready in the database for identifying the signal features that corresponds to physical scatterers on the target. Experiments involving real targets show that the technique can enhance the classification performance and therefore could be used for non-cooperative target recognition