Information Theoretic Limits on Non-cooperative Airborne Target Recognition by Means of Radar Sensors

The main objective of this research is to demonstrate that information theory, and specifically the concept of mutual information (MI) can be used to predict the maximum target recognition performance for a given radar concept in combination with a given set of targets of interest. This approach also allows for the direct comparison of disparate approaches to designing a radar concept which is capable of target recognition without resorting to choosing specific feature extraction and classification algorithms. The main application area of the study is the recognition of fighter type aircraft using surface based radar systems, although the results are also applicable to airborne radars. Information theoretic concepts are developed mathematically for the analysis of the radar target recognition problem. The various forms of MI required for this application are derived in detail and are tested rigorously against results from digital communication theory. The results are also compared to Shannon’s channel capacity bound, which is the fundamental limit on the amount of information which can be transmitted over a channel. Several sets of simulation based experiments were conducted to demonstrate the insights achievable by applying MI concepts to quantitatively predict the maximum achievable performance of disparate approaches to the radar target recognition problem. Asymptotic computational electromagnetic code was applied to calculate the target’s response to the radar signal for freely available geometrical models of fighter aircraft. The calculated target responses were then used to quantify the amount of information which is transmitted back to the radar about the target as a function of signal to noise ratio (SNR). The information content of the F-14, F-15 and F-16 were evaluated for a 480 MHz bandwidth waveform at 10 GHz as a baseline. Several ultra-wideband (UWB) waveforms, spanning 2-10 GHz, 10- 18 GHz and 2-18 GHz, but which were highly range ambiguous, were evaluated and showed SNR gains of 0.5-2 dB relative to the baseline. The effect of sensing the full polarimetric response of an F-18 and F-35 was evaluated and SNR gains of 5-7 dB over a single linear polarisation were measured. A Boeing 707 scale model (1:25) was measured in the University of Pretoria’s compact range spanning 2-18 GHz and gains of 2 dB were observed between single and dual linear polarisations. This required numerical integration in 8004 dimensions, demonstrating the stability of the MI estimation algorithm in high dimensional signal spaces. The information gained by including the difference channel signal of an X-band monopulse radar for the F-14 data set was approximately 3 dB at 50 km and increased to 4.5 dB at 2 km due to the increased target extent relative to the antenna pattern. This experiment necessitated the use of target profiles which were matched to the range of the target to achieve maximum information transfer. Experiments were conducted to evaluate the loss in information due to envelope processing. For the baseline data set, SNR losses in the region of 7 dB were measured. Linear pre-processing using the fast Fourier transform (FFT) and principal component analysis (PCA), before envelope processing, were compared and the PCA algorithm outperformed the FFT by approximately 1 dB at high MI values. Finally, the expression for multi-target MI was applied in conjunction with Fano’s inequality to predict the probability of incorrectly classifying a target. Probability of error is a critical parameter for a radar user. For the baseline data set, at P(error) = 0.001, maximum losses in the region of 0.6 to 0.9 dB were measured. This result shows that these targets are easily separable in the signal space. This study was only the proverbial “tip of the iceberg” and future research could extend the results and applications of the techniques developed. The types of targets and configurations of the individual targets could be increased and analysed. The analysis should also be extended to describe effects internal to the radar such as phase noise, spurious signals and analogue to digital converters and external effects such as clutter and multipath. The techniques could also be applied to quantify the gains in target recognition performance achievable for multistatic radar, multiple input multiple output (MIMO) radar and more exotic concepts, such as the fusion of data from multiple monostatic microwave radars with multi-receiver multi-band passive bistatic radar (PBR) data

Multi-dimensional lattice equaliser for Q2 PSK

The aim of this dissertation was the design, implementation and performance evaluation of a Recursive Least Squares (RLS), lattice based, adaptive, multidimensional, decision feedback equaliser (DFE) for the spectrally efficient four-dimensional digital modulation technique, re¬ferred to as Quadrature-Quadrature Phase-Shift Keying, Q2pSK. Q2PSK constitutes a relatively new modulation technique, and the application of adaptive equalisation to this technique has not yet been considered in the open literature. This dissertation represents an in depth study into the Q2PSK modulation technique, as well as the optimal implementation, in simulation, of such a modem to aid the inclusion of the adap¬tive lattice DFE, for application to high speed mobile digital communication over the V /UHF channel in the presence of multi path propagation. Specific aspects of synchronization applicable to this modem platform are also addressed. An in depth study was also conducted into the realisation of a V /UHF channel simulation, capable of producing a Ricean and/or Rayleigh fad¬ing multipath propagation environment for the evaluation of the modem platform and adaptive equaliser structure. The theoretical analysis of the effect of multi path on a Q2PSK signal led to the correct design of the adaptive lattice structure, as well as the correct interfacing of the equaliser to the receiver platform. The performance of the proposed synchronisation strategies, in tandem with the equalisation technique were evaluated for several static, as well as fading multipath channels. The simulation results obtained show the equaliser operates correctly, and can give large performance gains over the static matched filter (matched to the transmitted waveform) implementation of the modem platform. Several simulations were specifically designed to highlight the performance limitations of the adaptive equalisation technique.Dissertation (MEng (Digital Communication))--University of Pretoria, 2006.Electrical, Electronic and Computer Engineeringunrestricte

Feedback-assisted automatic target and clutter discrimination using a Bayesian convolutional neural network for improved explainability in SAR applications

DATA AVAILABILITY STATEMENT : The NATO-SET 250 dataset is not publicly available; however, the MSTAR dataset can be found at the following url: https://www.sdms.afrl.af.mil/index.php?collection=mstar (accessed on 5 January 2022).In this paper, a feedback training approach for efficiently dealing with distribution shift in synthetic aperture radar target detection using a Bayesian convolutional neural network is proposed. After training the network on in-distribution data, it is tested on out-of-distribution data. Samples that are classified incorrectly with high certainty are fed back for a second round of training. This results in the reduction of false positives in the out-of-distribution dataset. False positive target detections challenge human attention, sensor resource management, and mission engagement. In these types of applications, a reduction in false positives thus often takes precedence over target detection and classification performance. The classifier is used to discriminate the targets from the clutter and to classify the target type in a single step as opposed to the traditional approach of having a sequential chain of functions for target detection and localisation before the machine learning algorithm. Another aspect of automated synthetic aperture radar detection and recognition problems addressed here is the fact that human users of the output of traditional classification systems are presented with decisions made by “black box” algorithms. Consequently, the decisions are not explainable, even to an expert in the sensor domain. This paper makes use of the concept of explainable artificial intelligence via uncertainty heat maps that are overlaid onto synthetic aperture radar imagery to furnish the user with additional information about classification decisions. These uncertainty heat maps facilitate trust in the machine learning algorithm and are derived from the uncertainty estimates of the classifications from the Bayesian convolutional neural network. These uncertainty overlays further enhance the users’ ability to interpret the reasons why certain decisions were made by the algorithm. Further, it is demonstrated that feeding back the high-certainty, incorrectly classified out-of-distribution data results in an average improvement in detection performance and a reduction in uncertainty for all synthetic aperture radar images processed. Compared to the baseline method, an improvement in recall of 11.8%, and a reduction in the false positive rate of 7.08% were demonstrated using the Feedback-assisted Bayesian Convolutional Neural Network or FaBCNN.The Radar and Electronic Warfare department at the CSIR.http://www.mdpi.com/journal/remotesensinghj2023Electrical, Electronic and Computer Engineerin

An active wideband reference target for the calibration of ground to air radar systems

The radar cross section (RCS) of passive calibration targets is relatively low, which leads to a commensurate increase in the uncertainty of the radar’s calibration. To address this deficiency, an active radar calibration (ARC) target was developed, which was mounted on a small remote controlled tri-copter and used for radar calibration. Results are presented for this relatively small, light weight and cost effective airborne ARC, which is suitable for the calibration of a ground to air RCS measurement system. The static RCS characteristics of the airborne ARC target were measured in a compact range and compared to outdoor measurements with the ARC target mounted on the tri-copter. The airborne capability of the calibration target was used to reduce the effects of multi-path and clutter.http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1098-27602017-05-31hb2016Electrical, Electronic and Computer Engineerin

Eksperimentele hemorragiese skok

Tesis (M.Sc. (fisiologie)) - Universiteit van Stellenbosch, 1974.Full text to be digitised and attached to bibliographic record

Eksperimentele hemorragiese skok

Tesis (M.Sc. (fisiologie)) - Universiteit van Stellenbosch, 1974.Full text to be digitised and attached to bibliographic record

Aspekte van die inflammatoriese proses in atopiese vel

Proefskrif (M.D.) -- Universiteit van Stellenbosch, 1993.Een kopie mikrofiche.Full text to be digitised and attached to bibliographic record

Feedback-Assisted Automatic Target and Clutter Discrimination Using a Bayesian Convolutional Neural Network for Improved Explainability in SAR Applications

In this paper, a feedback training approach for efficiently dealing with distribution shift in synthetic aperture radar target detection using a Bayesian convolutional neural network is proposed. After training the network on in-distribution data, it is tested on out-of-distribution data. Samples that are classified incorrectly with high certainty are fed back for a second round of training. This results in the reduction of false positives in the out-of-distribution dataset. False positive target detections challenge human attention, sensor resource management, and mission engagement. In these types of applications, a reduction in false positives thus often takes precedence over target detection and classification performance. The classifier is used to discriminate the targets from the clutter and to classify the target type in a single step as opposed to the traditional approach of having a sequential chain of functions for target detection and localisation before the machine learning algorithm. Another aspect of automated synthetic aperture radar detection and recognition problems addressed here is the fact that human users of the output of traditional classification systems are presented with decisions made by “black box” algorithms. Consequently, the decisions are not explainable, even to an expert in the sensor domain. This paper makes use of the concept of explainable artificial intelligence via uncertainty heat maps that are overlaid onto synthetic aperture radar imagery to furnish the user with additional information about classification decisions. These uncertainty heat maps facilitate trust in the machine learning algorithm and are derived from the uncertainty estimates of the classifications from the Bayesian convolutional neural network. These uncertainty overlays further enhance the users’ ability to interpret the reasons why certain decisions were made by the algorithm. Further, it is demonstrated that feeding back the high-certainty, incorrectly classified out-of-distribution data results in an average improvement in detection performance and a reduction in uncertainty for all synthetic aperture radar images processed. Compared to the baseline method, an improvement in recall of 11.8%, and a reduction in the false positive rate of 7.08% were demonstrated using the Feedback-assisted Bayesian Convolutional Neural Network or FaBCNN

A Wideband Beamformer Extended to MIMO Radar

5 pagesInternational audienceMIMO radar algorithms are the latest generation of techniques which can be applied to phased array radars. They offer the potential to improve the resolution, number of targets that can be identified, and flexibility in beampattern design. To date, most of the work on MIMO radar has been performed assuming the signals are narrowband. However, wideband signals can also improve radar resolution, among other benefits, and are sometimes unavoidable when stringent range resolution specifications must be met. This paper presents a method for extending the MIMO narrowband model to a wideband model. This is necessary to obtain improved results from parameter estimation when the transmitted signals are wideband. The results show that the method greatly improves the results compared to those obtained when no techniques are implemented to compensate for a wideband signal. However, best performance is still obtained with a narrowband signal, and therefore the technique presented might only be of interest when a wideband signal is required

Evaluation and Performance Comparison of Detection Algorithms in a Maritime Environment

In this paper, a framework will be proposed for the evaluation of detector algorithms in a maritime environment. Performance metrics and test cases will be defined to allow the impartial comparison of different detectors. In this framework the main approaches for detector comparison are numerical simulation and the use of recorded sea clutter and boat reflectivity data. Available data suitable to the fair comparison of different algorithms will be highlighted, with results for a selection of algorithms. The proposed framework, performance metrics and baseline cases give researchers and system engineers the ability to quantify system performance in a complex clutter environment and to evaluate the effectiveness of a particular detector (or radar design(s)) as compared to another