On model, algorithms and experiment for micro-doppler based recognition of ballistic targets

The ability to discriminate between Ballistic Missile warheads and confusing objects is an important topic from different points of view. In particular, the high cost of the interceptors with respect to tactical missiles may lead to an ammunition problem. Moreover, since the time interval in which the defence system can intercept the missile is very short with respect to target velocities, it is fundamental to minimise the number of shoots per kill. For this reason a reliable technique to classify warheads and confusing objects is required. In the efficient warhead classification system presented in this paper a model and a robust framework is developed, which incorporates different microDoppler based classification techniques. The reliability of the proposed framework is tested on both simulated and real dat

Micro-doppler classification of ballistic threats using krawtchouk moments

The challenge of ballistic missiles classification is getting greater importance in last years. In fact, since the antimissile defence systems have generally a limited number of interceptors, it is important to distinguish between warheads and confusing objects that the missile releases during its flight, in order to maximize the interception success ratio. For this aim, a novel micro-Doppler based classification technique is presented in this paper characterized by the employment of Krawtchouk moments. Since the evaluation of the latter requires a low computational time, the proposed approach is suitable for real time applications. Finally, a comparison with the 2-dimensional Gabor filter based approach is described by testing both the techniques on real radar data

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

Two-dimensional Length Extraction of Ballistic Target from ISAR Images Using a New Scaling Method by Affine Registration

The length of ballistic target is one of the most important features for target recognition. It can be extracted from ISAR Images. Unlike from the optical image, the length extraction from ISAR image has two difficulties. The first one is that it is hard to get the actual position of scattering centres by the traditional target extraction method. The second one is that the ISAR image’s cross scale is not known because of the target’s complex rotation. Here we propose two methods to solve these problems. Firstly, we use clustering method to get scattering centers. Secondly we propose to get cross scale of the ISAR images by affine registration. Experiments verified that our approach is realisable and has good performance.Defence Science Journal, Vol. 64, No. 5, September 2014, pp.458-463, DOI:http://dx.doi.org/10.14429/dsj.64.500

Try Living in the Real World: the importance of experimental radar systems and data collection trials

While simulations of increasingly high fidelity are an important tool in radar science, experimentation is still needed as a source of validation for simulation, to explore complex phenomena which cannot be accurately simulated and ultimately in turning theory and simulation into a real world system with real world applications. Experimental systems can range from laboratory based, installations on the ground with limited fields of view all the way up to flying demonstrators which may be prototypes for radar products. In this paper we will discuss the importance of experimentation in the development of radar science and radar products with examples of systems used by a sub-set of the members of the UK EMSIG

Template free Micro Doppler Signature Classification for Wheeled and Tracked Vehicles

The micro-Doppler signature is a time-varying frequency modulation imparted on radar echo caused by target’s micro-motion. To save the trouble of constructing template in the target classification, this paper investigates the micro-Doppler signature of wheeled and tracked vehicles and proposes a template-free classification method. Firstly, the echo signature is established and the micro-Doppler difference of these two kinds of targets is analysed. Secondly, some new micro-Doppler features are defined according to their difference. The new defined features are micro-Doppler bandwidth, micro-Doppler expansion rate and micro-Doppler peak number. According to the characteristic of the micro-Doppler in the time-frequency domain, we proposed to realise the feature extraction by Hough transformation. Lastly, template-free subjection functions are proposed to define the relationship between the features and the vehicles. By fuzzy comprehensive evaluation, the final classification result is obtained by combining the subjection probabilities together. Experimental results based on the simulated data and measured data are presented, which prove that the algorithm has good performance

The University Defence Research Collaboration In Signal Processing

This chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations. The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour

Efficient range alignment algorithm for real-time range-Doppler algorithm

When deriving a range-Doppler image or a time-frequency image of a fast-maneuvering target at long range, existing range alignment methods yield poor results due to the large numbers of range profiles (RPs) and range bins that are required for this task. This paper proposes a three-step range alignment method to overcome the problems of these existing methods and to yield focused images: (1) coarse alignment using the interpolated center of mass of each RP, (2) fine alignment with an integer step using an entropy cost function, and (3) fine-tuning using particle swarm optimization. Compared to existing methods, the proposed method is computationally more efficient and provides better image focus. © 2017, Electromagnetics Academy. All rights reserved.11Yscopu

Understanding the potential of Self-Protection Jamming on board of miniature UAVs

Unarmed Aerial Vehicle (UAV) systems are very challenging targets due to their small size and ability to fly in low altitudes and speed. Particularly, in radar systems UAVs can exhibit similar radar cross section and Doppler parameters to clutter returns such as birds and trees. For this reason, often the micro-Doppler signature of the detected target is employed as discriminative characteristic. This work aims to examine micro-Doppler jamming solutions that could be implemented on board of miniature UAV platforms in order to deploy electronic countermeasures to radar sensors, with the aim to provide useful information to the radar community to counter these