Dual operative radar for vehicle to vehicle and vehicle to infrastructure communication

The research presented in this Thesis deals with the concepts of joint radar and communication system for automotive application. The novel systems developed include a joint radar and communication system based on the fractional Fourier transform (FrFT) and two interference mitigation frameworks. In the joint radar and communication system the FrFT is used to embed the data information into a radar waveform in order to obtain a signal sharing Linear Frequency Modulation (LFM) characteristics while allowing data transmission. Furthermore, in the proposed system multi user operations are allowed by assigning a specific order of the FrFT to each user. In this way, a fractional order division multiplexing can be implemented allowing the allocation of more than one user in the same frequency band with the advantage that the range resolution does not depend on the number of the users that share the same frequency band but only from the assigned of the FrFT. Remarkably, the predicted simulated radar performance of the proposed joint radar and communication system when using Binary Frequency Shift Keying (BFSK) encoding is not significantly affected by the transmitted data. In order to fully describe the proposed waveform design, the signal model when the bits of information are modulated using either BFSK or Binary Phase Shift Keying (BPSK) encoding is derived. This signal model will result also useful in the interference mitigation frameworks. In multi user scenarios to prevent mutual radar interference caused by users that share the same frequency band at the same time, each user has to transmit waveforms that are uncorrelated with those of other users. However, due to spectrum limitations, the uncorrelated property cannot always be satisfied even by using fractional order division multiplexing, thus interference is unavoidable. In order to mitigate the interference, two frameworks are introduced. In a joint radar communication system, the radar also has access to the communication data. With a near-precision reconstruction of the communication signal, this interference can be subtracted. In these two frameworks the interfering signal can be reconstructed using the derived mathematical model of the proposed FrFT waveform. In the first framework the subtraction between the received and reconstructed interference signals is carried out in a coherent manner, where the amplitude and phase of the two signals are taken into account. The performance of this framework is highly depend on the correct estimation of the Doppler frequency of the interfering user. A small error on the Doppler frequency can lead to a lack of synchronization between the received and reconstructed signal. Consequently, the subtraction will not be performed in a correct way and further interference components can be introduced. In order to solve the problem of the lack of the synchronization an alternative framework is developed where the subtraction is carried out in non-coherent manner. In the proposed framework, the subtraction is carried out after that the received radar signal and the reconstructed interference are processed, respectively. The performance is tested on simulated and real signals. The simulated and experimental results show that this framework is capable of mitigating the interference from other users successfully.The research presented in this Thesis deals with the concepts of joint radar and communication system for automotive application. The novel systems developed include a joint radar and communication system based on the fractional Fourier transform (FrFT) and two interference mitigation frameworks. In the joint radar and communication system the FrFT is used to embed the data information into a radar waveform in order to obtain a signal sharing Linear Frequency Modulation (LFM) characteristics while allowing data transmission. Furthermore, in the proposed system multi user operations are allowed by assigning a specific order of the FrFT to each user. In this way, a fractional order division multiplexing can be implemented allowing the allocation of more than one user in the same frequency band with the advantage that the range resolution does not depend on the number of the users that share the same frequency band but only from the assigned of the FrFT. Remarkably, the predicted simulated radar performance of the proposed joint radar and communication system when using Binary Frequency Shift Keying (BFSK) encoding is not significantly affected by the transmitted data. In order to fully describe the proposed waveform design, the signal model when the bits of information are modulated using either BFSK or Binary Phase Shift Keying (BPSK) encoding is derived. This signal model will result also useful in the interference mitigation frameworks. In multi user scenarios to prevent mutual radar interference caused by users that share the same frequency band at the same time, each user has to transmit waveforms that are uncorrelated with those of other users. However, due to spectrum limitations, the uncorrelated property cannot always be satisfied even by using fractional order division multiplexing, thus interference is unavoidable. In order to mitigate the interference, two frameworks are introduced. In a joint radar communication system, the radar also has access to the communication data. With a near-precision reconstruction of the communication signal, this interference can be subtracted. In these two frameworks the interfering signal can be reconstructed using the derived mathematical model of the proposed FrFT waveform. In the first framework the subtraction between the received and reconstructed interference signals is carried out in a coherent manner, where the amplitude and phase of the two signals are taken into account. The performance of this framework is highly depend on the correct estimation of the Doppler frequency of the interfering user. A small error on the Doppler frequency can lead to a lack of synchronization between the received and reconstructed signal. Consequently, the subtraction will not be performed in a correct way and further interference components can be introduced. In order to solve the problem of the lack of the synchronization an alternative framework is developed where the subtraction is carried out in non-coherent manner. In the proposed framework, the subtraction is carried out after that the received radar signal and the reconstructed interference are processed, respectively. The performance is tested on simulated and real signals. The simulated and experimental results show that this framework is capable of mitigating the interference from other users successfully

Advanced signal processing solutions for ATR and spectrum sharing in distributed radar systems

Previously held under moratorium from 11 September 2017 until 16 February 2022This Thesis presents advanced signal processing solutions for Automatic Target Recognition (ATR) operations and for spectrum sharing in distributed radar systems. Two Synthetic Aperture Radar (SAR) ATR algorithms are described for full- and single-polarimetric images, and tested on the GOTCHA and the MSTAR datasets. The first one exploits the Krogager polarimetric decomposition in order to enhance peculiar scattering mechanisms from manmade targets, used in combination with the pseudo-Zernike image moments. The second algorithm employs the Krawtchouk image moments, that, being discrete defined, provide better representations of targets’ details. The proposed image moments based framework can be extended to the availability of several images from multiple sensors through the implementation of a simple fusion rule. A model-based micro-Doppler algorithm is developed for the identification of helicopters. The approach relies on the proposed sparse representation of the signal scattered from the helicopter’s rotor and received by the radar. Such a sparse representation is obtained through the application of a greedy sparse recovery framework, with the goal of estimating the number, the length and the rotation speed of the blades, parameters that are peculiar for each helicopter’s model. The algorithm is extended to deal with the identification of multiple helicopters flying in formation that cannot be resolved in another domain. Moreover, a fusion rule is presented to integrate the results of the identification performed from several sensors in a distributed radar system. Tests performed both on simulated signals and on real signals acquired from a scale model of a helicopter, confirm the validity of the algorithm. Finally, a waveform design framework for joint radar-communication systems is presented. The waveform is composed by quasi-orthogonal chirp sub-carriers generated through the Fractional Fourier Transform (FrFT), with the aim of preserving the radar performance of a typical Linear Frequency Modulated (LFM) pulse while embedding data to be sent to a cooperative system. Techniques aimed at optimise the design parameters and mitigate the Inter-Carrier Interference (ICI) caused by the quasiorthogonality of the chirp sub-carriers are also described. The FrFT based waveform is extensively tested and compared with Orthogonal Frequency Division Multiplexing (OFDM) and LFM waveforms, in order to assess both its radar and communication performance.This Thesis presents advanced signal processing solutions for Automatic Target Recognition (ATR) operations and for spectrum sharing in distributed radar systems. Two Synthetic Aperture Radar (SAR) ATR algorithms are described for full- and single-polarimetric images, and tested on the GOTCHA and the MSTAR datasets. The first one exploits the Krogager polarimetric decomposition in order to enhance peculiar scattering mechanisms from manmade targets, used in combination with the pseudo-Zernike image moments. The second algorithm employs the Krawtchouk image moments, that, being discrete defined, provide better representations of targets’ details. The proposed image moments based framework can be extended to the availability of several images from multiple sensors through the implementation of a simple fusion rule. A model-based micro-Doppler algorithm is developed for the identification of helicopters. The approach relies on the proposed sparse representation of the signal scattered from the helicopter’s rotor and received by the radar. Such a sparse representation is obtained through the application of a greedy sparse recovery framework, with the goal of estimating the number, the length and the rotation speed of the blades, parameters that are peculiar for each helicopter’s model. The algorithm is extended to deal with the identification of multiple helicopters flying in formation that cannot be resolved in another domain. Moreover, a fusion rule is presented to integrate the results of the identification performed from several sensors in a distributed radar system. Tests performed both on simulated signals and on real signals acquired from a scale model of a helicopter, confirm the validity of the algorithm. Finally, a waveform design framework for joint radar-communication systems is presented. The waveform is composed by quasi-orthogonal chirp sub-carriers generated through the Fractional Fourier Transform (FrFT), with the aim of preserving the radar performance of a typical Linear Frequency Modulated (LFM) pulse while embedding data to be sent to a cooperative system. Techniques aimed at optimise the design parameters and mitigate the Inter-Carrier Interference (ICI) caused by the quasiorthogonality of the chirp sub-carriers are also described. The FrFT based waveform is extensively tested and compared with Orthogonal Frequency Division Multiplexing (OFDM) and LFM waveforms, in order to assess both its radar and communication performance

Frequency Diverse Array Radar: Signal Characterization and Measurement Accuracy

Radar systems provide an important remote sensing capability, and are crucial to the layered sensing vision; a concept of operation that aims to apply the right number of the right types of sensors, in the right places, at the right times for superior battle space situational awareness. The layered sensing vision poses a range of technical challenges, including radar, that are yet to be addressed. To address the radar-specific design challenges, the research community responded with waveform diversity; a relatively new field of study which aims reduce the cost of remote sensing while improving performance. Early work suggests that the frequency diverse array radar may be able to perform several remote sensing missions simultaneously without sacrificing performance. With few techniques available for modeling and characterizing the frequency diverse array, this research aims to specify, validate and characterize a waveform diverse signal model that can be used to model a variety of traditional and contemporary radar configurations, including frequency diverse array radars. To meet the aim of the research, a generalized radar array signal model is specified. A representative hardware system is built to generate the arbitrary radar signals, then the measured and simulated signals are compared to validate the model. Using the generalized model, expressions for the average transmit signal power, angular resolution, and the ambiguity function are also derived. The range, velocity and direction-of-arrival measurement accuracies for a set of signal configurations are evaluated to determine whether the configuration improves fundamental measurement accuracy

Seventy Years of Radar and Communications: The Road from Separation to Integration

Radar and communications (R&C) as key utilities of electromagnetic (EM) waves have fundamentally shaped human society and triggered the modern information age. Although R&C have been historically progressing separately, in recent decades they have been moving from separation to integration, forming integrated sensing and communication (ISAC) systems, which find extensive applications in next-generation wireless networks and future radar systems. To better understand the essence of ISAC systems, this paper provides a systematic overview on the historical development of R&C from a signal processing (SP) perspective. We first interpret the duality between R&C as signals and systems, followed by an introduction of their fundamental principles. We then elaborate on the two main trends in their technological evolution, namely, the increase of frequencies and bandwidths, and the expansion of antenna arrays. Moreover, we show how the intertwined narratives of R\&C evolved into ISAC, and discuss the resultant SP framework. Finally, we overview future research directions in this field