Novel classification algorithm for ballistic target based on HRRP frame

Nowadays the identification of ballistic missile warheads in a cloud of decoys and debris is essential for defence systems in order to optimize the use of ammunition resources, avoiding to run out of all the available interceptors in vain. This paper introduces a novel solution for the classification of ballistic targets based on the computation of the inverse Radon transform of the target signatures, represented by a high resolution range profile frame acquired within an entire period of the main rotation of the target. Namely, the precession for warheads and the tumbling for decoys are taken into account. Following, the pseudo-Zernike moments of the resulting transformation are evaluated as the final feature vector for the classifier. The extracted features guarantee robustness against target's dimensions and rotation velocity, and the initial phase of the target's motion. The classification results on simulated data are shown for different polarizations of the electromagnetic radar waveform and for various operational conditions, confirming the validity of the algorithm

Target recognition techniques for multifunction phased array radar

This thesis, submitted for the degree of Doctor of Philosophy at University College London, is a discussion and analysis of combined stepped-frequency and pulse-Doppler target recognition methods which enable a multifunction phased array radar designed for automatic surveillance and multi-target tracking to offer a Non Cooperative Target Recognition (NCTR) capability. The primary challenge is to investigate the feasibility of NCTR via the use of high range resolution profiles. Given stepped frequency waveforms effectively trade time for enhanced bandwidth, and thus resolution, attention is paid to the design of a compromise between resolution and dwell time. A secondary challenge is to investigate the additional benefits to overall target classification when the number of coherent pulses within an NCTR wavefrom is expanded to enable the extraction of spectral features which can help to differentiate particular classes of target. As with increased range resolution, the price for this extra information is a further increase in dwell time. The response to the primary and secondary challenges described above has involved the development of a number of novel techniques, which are summarized below: • Design and execution of a series of experiments to further the understanding of multifunction phased array Radar NCTR techniques • Development of a ‘Hybrid’ stepped frequency technique which enables a significant extension of range profiles without the proportional trade in resolution as experienced with ‘Classical’ techniques • Development of an ‘end to end’ NCTR processing and visualization pipeline • Use of ‘Doppler fraction’ spectral features to enable aircraft target classification via propulsion mechanism. Combination of Doppler fraction and physical length features to enable broad aircraft type classification. • Optimization of NCTR method classification performance as a function of feature and waveform parameters. • Generic waveform design tools to enable delivery of time costly NCTR waveforms within operational constraints. The thesis is largely based upon an analysis of experimental results obtained using the multifunction phased array radar MESAR2, based at BAE Systems on the Isle of Wight. The NCTR mode of MESAR2 consists of the transmission and reception of successive multi-pulse coherent bursts upon each target being tracked. Each burst is stepped in frequency resulting in an overall bandwidth sufficient to provide sub-metre range resolution. A sequence of experiments, (static trials, moving point target trials and full aircraft trials) are described and an analysis of the robustness of target length and Doppler spectra feature measurements from NCTR mode data recordings is presented. A recorded data archive of 1498 NCTR looks upon 17 different trials aircraft using five different varieties of stepped frequency waveform is used to determine classification performance as a function of various signal processing parameters and extent (numbers of pulses) of the data used. From analysis of the trials data, recommendations are made with regards to the design of an NCTR mode for an operational system that uses stepped frequency techniques by design choice

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

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

Waveform design and processing techniques in OFDM radar

Includes bibliographical referencesWith the advent of powerful digital hardware, software defined radio and radar have become an active area of research and development. This in turn has given rise to many new research directions in the radar community, which were previously not comprehensible. One such direction is the recently investigated OFDM radar, which uses OFDM waveforms instead of the classic linear frequency mod- ulated waveforms. Being a wideband signal, the OFDM symbol offers spectral efficiency along with improved range resolution, two enticing characteristics for radar. Historically a communication signal, OFDM is a special form of multi- carrier modulation, where a single data stream is transmitted over a number of lower rate carriers. The information is conveyed via sets of complex phase codes modulating the phase of the carriers. At the receiver, a demodulation stage estimates the transmitted phase codes and the information in the form of binary words is finally retrieved. In radar, the primary goal is to detect the presence of targets and possibly estimate some of their features through measurable quantities, e.g. range, Doppler, etc. Yet, being a young waveform in radar, more understanding is required to turn it into a standard radar waveform. Our goal, with this thesis, is to mature our comprehension of OFDM for radar and contribute to the realm of OFDM radar. First, we develop two processing alternatives for the case of a train of wideband OFDM pulses. In this, our first so-called time domain solution consists in applying a matched filter to compress the received echoes in the fast time before applying a fast Fourier transform in the slow time to form the range Doppler image. We motivate this approach after demonstrating that short OFDM pulses are Doppler tolerant. The merit of this approach is to conserve existing radar architectures while operating OFDM waveforms. The second so-called frequency domain solution that we propose is inspired from communication engineering research since the received echoes are tumbled in the frequency domain. After several manipulations, the range Doppler image is formed. We explain how this approach allows to retrieve an estimate of the unambiguous radial velocity, and propose two methods for that. The first method requires the use of identical sequence (IS) for the phase codes and is, as such, binding, while the other method works irrespective of the phase codes. Like the previous technique, this processing solution accommodates high Doppler frequencies and the degradation in the range Doppler image is negligible provided that the spacing between consecutive subcarriers is sufficient. Unfortunately, it suffers from the issue of intersymbol interference (ISI). After observing that both solutions provide the same processing gain, we clarify the constraints that shall apply to the OFDM signals in either of these solutions. In the first solution, special care has been employed to design OFDM pulses with low peak-to-mean power ratio (PMEPR) and low sidelobe level in the autocorrelation function. In the second solution, on the other hand, only the constraint of low PMEPR applies since the sidelobes of the scatterer characteristic function in the range Doppler image are Fourier based. Then, we develop a waveform-processing concept for OFDM based stepped frequency waveforms. This approach is intended for high resolution radar with improved low probability of detection (LPD) characteristics, as we propose to employ a frequency hopping scheme from pulse to pulse other than the conventional linear one. In the same way we treated our second alternative earlier, we derive our high range resolution processing in matrix terms and assess the degradation caused by high Doppler on the range profile. We propose using a bank of range migration filters to retrieve the radial velocity of the scatterer and realise that the issue of classical ambiguity in Doppler can be alleviated provided that the relative bandwidth, i.e. the total bandwidth covered by the train of pulses divided by the carrier frequency, is chosen carefully. After discussing a deterministic artefact caused by frequency hopping and the means to reduce it at the waveform design or processing level, we discuss the benefit offered by our concept in comparison to other standard wideband methods and emphasize on its LPD characteristics at the waveform and pulse level. In our subsequent analysis, we investigate genetic algorithm (GA) based techniques to finetune OFDM pulses in terms of radar requirements viz., low PMEPR only or low PMEPR and low sidelobe level together, as evoked earlier. To motivate the use of genetic algorithms, we establish that existing techniques are not exible in terms of the OFDM structure (the assumption that all carriers are present is always made). Besides, the use of advanced objective functions suited to particular configurations (e.g. low sidelobe level in proximity of the main autocorrelation peak) as well as the combination of multiple objective functions can be done elegantly with GA based techniques. To justify that solely phase codes are used for our optimisation(s), we stress that the weights applied to the carriers composing the OFDM signal can be spared to cope with other radar related challenges and we give an example with a case of enhanced detection. Next, we develop a technique where we exploit the instantaneous wideband trans- mission to characterise the type of the canonical scatterers that compose a target. Our idea is based on the well-established results from the geometrical theory of diffraction (GTD), where the scattered energy varies with frequency. We present the problem related to ISI, stress the need to design the transmitted pulse so as to reduce this risk and suggest having prior knowledge over the scatterers relative positions. Subsequently, we develop a performance analysis to assess the behaviour of our technique in the presence of additive white Gaussian noise (AWGN). Then, we demonstrate the merit of integrating over several pulses to improve the characterisation rate of the scatterers. Because the scattering centres of a target resonate variably at different frequencies, frequency diversity is another enticing property which can be used to enhance the sensing performance. Here, we exploit this element of diversity to improve the classification function. We develop a technique where the classification takes place at the waveform design when few targets are present. In our case study, we have three simple targets. Each is composed of perfectly electrically conducting spheres for which we have exact models of the scattered field. We develop a GA based search to find optimal OFDM symbols that best discriminate one target against any other. Thereafter, the OFDM pulse used for probing the target in the scene is constructed by stacking the resulting symbols in time. After discussing the problem of finding the best frequency window to sense the target, we develop a performance analysis where our figure of merit is the overall probability of correct classification. Again, we prove the merit of integrating over several pulses to reach classification rates above 95%. In turn, this study opens onto new challenges in the realm of OFDM radar. We leave for future research the demonstration of the practical applicability of our novel concepts and mention manifold research axes, viz., a signal processing axis that would include methods to cope with inter symbol interference, range migration issues, methods to raise the ambiguity in Doppler when several echoes from distinct scatterers overlap in the case of our frequency domain processing solutions; an algorithmic axis that would concern the heuristic techniques employed in the design of our OFDM pulses. We foresee that further tuning might help speeding up our GA based algorithms and we expect that constrained multi- objective optimisation GA (MOO-GA) based techniques shall benefit the OFDM pulse design problem in radar. A system design axis that would account for the hardware components' behaviours, when possible, directly at the waveform design stage and would include implementation of the OFDM radar system

Investigation of non-cooperative target recognition of small and slow moving air targets in modern air defence surveillance radar

This thesis covers research in the field of non-cooperative target recognition given the limitations of modern air defence surveillance radars. The potential presence of low observable manned or unmanned targets within the vast surveillance volume demand highly sensitive systems. This may again introduce unwanted detections of single birds of comparable radar cross section, previously avoided by use of wide clutter rejection filters and sensitivity time control. The demand for methods effectively separating between birds and slow moving manmade targets is evident. The research questions addressed are connected to identification of characteristic features of birds and manmade targets of comparable size. Ultimately the goal has been to find methods that can utilize such features to effectively distinguish between the classes. In contrast to the vast majority of non-cooperative target recognition publications, this thesis includes non-rigid targets covering a range of dielectric properties and targets falling in the resonant and Rayleigh scattering regions. These factors combined with insufficient spatial resolution for classification require alternative approaches such as utilization of periodic RCS modulation, micro-Doppler- and polarimetric signatures. Signatures of birds and UAVs are investigated through electromagnetic prediction and radar measurements. A flexible and fully polarimetric radar capable of simultaneous operation in both L- and S-band is developed for collection of relevant signatures. Inspired by the use of polarimetric radar for classification of precipitation covered in the weather radar literature, focus has been on using similar methods to recognize signatures of rotors, propellers and bird wings. Novel micro-Doppler signatures combining polarimetric information from this sensor is found to hold information about the orientation of such target parts. This information combined with several other features is evaluated for classification. The benefit from involving polarimetric measurements is especially investigated, and is found to be highly valuable when information provided by other methods is limited

The detection of unknown waveforms in ESM receivers: FFT-based real-time solutions

Radars and airborne electronic support measures (ESMs) systems are locked in a tactical battle to detect each other whilst remaining undetected. Traditionally, the ESM system has a range advantage. Low probability of intercept (LPI) waveform designers are, however, more heavily exploiting the matched filter radar advantage and hence degrading the range advantage. There have been literature and internal, SELEX Galileo proposals to regain some ESM processing gain of low probability of intercept (LPI) waveforms. This study, however, has sought digital signal processing (DSP) solutions which are: (1) computationally simple; (2) backward-compatible with existing SELEX Galileo digital receivers (DRxs) and (3) have low resource requirements. The two contributions are complementary and result in a detector which is suitable for detection of most radar waveforms. The first contribution is the application of spatially variant apodization (SVA) in a detection role. Compared to conventional window functions, SVA was found to be beneficial for the detection of sinusoidal radar waveforms as it surpassed the fixed window function detectors in all scenarios tested. The second contribution shows by simulation that simple spectral smoothing techniques improved DRx LPI detection capability to a level similar to more complicated non-parametric spectral estimators and far in excess of the conventional (modified) periodogram. The DSP algorithms were implemented using model-based design (MBD). The implication is that a detector with improved conventional and LPI waveform detection capability can be created from the intellectual property (IP). Estimates of the improvement in SELEX Galileo DRx system detection range are provided in the conclusion

Metrics to evaluate compressions algorithms for RAW SAR data

Modern synthetic aperture radar (SAR) systems have size, weight, power and cost (SWAP-C) limitations since platforms are becoming smaller, while SAR operating modes are becoming more complex. Due to the computational complexity of the SAR processing required for modern SAR systems, performing the processing on board the platform is not a feasible option. Thus, SAR systems are producing an ever-increasing volume of data that needs to be transmitted to a ground station for processing. Compression algorithms are utilised to reduce the data volume of the raw data. However, these algorithms can cause degradation and losses that may degrade the effectiveness of the SAR mission. This study addresses the lack of standardised quantitative performance metrics to objectively quantify the performance of SAR data-compression algorithms. Therefore, metrics were established in two different domains, namely the data domain and the image domain. The data-domain metrics are used to determine the performance of the quantisation and the associated losses or errors it induces in the raw data samples. The image-domain metrics evaluate the quality of the SAR image after SAR processing has been performed. In this study three well-known SAR compression algorithms were implemented and applied to three real SAR data sets that were obtained from a prototype airborne SAR system. The performance of these algorithms were evaluated using the proposed metrics. Important metrics in the data domain were found to be the compression ratio, the entropy, statistical parameters like the skewness and kurtosis to measure the deviation from the original distributions of the uncompressed data, and the dynamic range. The data histograms are an important visual representation of the effects of the compression algorithm on the data. An important error measure in the data domain is the signal-to-quantisation-noise ratio (SQNR), and the phase error for applications where phase information is required to produce the output. Important metrics in the image domain include the dynamic range, the impulse response function, the image contrast, as well as the error measure, signal-to-distortion-noise ratio (SDNR). The metrics suggested that all three algorithms performed well and are thus well suited for the compression of raw SAR data. The fast Fourier transform block adaptive quantiser (FFT-BAQ) algorithm had the overall best performance, but the analysis of the computational complexity of its compression steps, indicated that it is has the highest level of complexity compared to the other two algorithms. Since different levels of degradation are acceptable for different SAR applications, a trade-off can be made between the data reduction and the degradation caused by the algorithm. Due to SWAP-C limitations, there also remains a trade-off between the performance and the computational complexity of the compression algorithm.Dissertation (MEng)--University of Pretoria, 2019.Electrical, Electronic and Computer EngineeringMEngUnrestricte

Metrics to evaluate compressions algorithms for RAW SAR data

Modern synthetic aperture radar (SAR) systems have size, weight, power and cost (SWAP-C) limitations since platforms are becoming smaller, while SAR operating modes are becoming more complex. Due to the computational complexity of the SAR processing required for modern SAR systems, performing the processing on board the platform is not a feasible option. Thus, SAR systems are producing an ever-increasing volume of data that needs to be transmitted to a ground station for processing. Compression algorithms are utilised to reduce the data volume of the raw data. However, these algorithms can cause degradation and losses that may degrade the effectiveness of the SAR mission. This study addresses the lack of standardised quantitative performance metrics to objectively quantify the performance of SAR data-compression algorithms. Therefore, metrics were established in two different domains, namely the data domain and the image domain. The data-domain metrics are used to determine the performance of the quantisation and the associated losses or errors it induces in the raw data samples. The image-domain metrics evaluate the quality of the SAR image after SAR processing has been performed. In this study three well-known SAR compression algorithms were implemented and applied to three real SAR data sets that were obtained from a prototype airborne SAR system. The performance of these algorithms were evaluated using the proposed metrics. Important metrics in the data domain were found to be the compression ratio, the entropy, statistical parameters like the skewness and kurtosis to measure the deviation from the original distributions of the uncompressed data, and the dynamic range. The data histograms are an important visual representation of the effects of the compression algorithm on the data. An important error measure in the data domain is the signal-to-quantisation-noise ratio (SQNR), and the phase error for applications where phase information is required to produce the output. Important metrics in the image domain include the dynamic range, the impulse response function, the image contrast, as well as the error measure, signal-to-distortion-noise ratio (SDNR). The metrics suggested that all three algorithms performed well and are thus well suited for the compression of raw SAR data. The fast Fourier transform block adaptive quantiser (FFT-BAQ) algorithm had the overall best performance, but the analysis of the computational complexity of its compression steps, indicated that it is has the highest level of complexity compared to the other two algorithms. Since different levels of degradation are acceptable for different SAR applications, a trade-off can be made between the data reduction and the degradation caused by the algorithm. Due to SWAP-C limitations, there also remains a trade-off between the performance and the computational complexity of the compression algorithm.Dissertation (MEng)--University of Pretoria, 2019.TM2019Electrical, Electronic and Computer EngineeringMEngUnrestricte