Baseband version of the bat-inspired spectrogram correlation and transformation receiver

Poster presentation at the 2016 Defence and Security Doctoral Symposium. Echolocating bats have evolved an excellent ability to detect, resolve and discriminate targets in highly challenging environments. They have had more than 50 million years of evolution to optimise their echolocation system and behavioural experiments have shown their exceptional ability to detect and classify targets even in highly cluttered surroundings. Behavioural experiments have demonstrated that bats are able to resolve closely located scatterers: •a two-point resolution of 2÷10 μs with waveforms of a bandwidth of 85 kHz (Eptesicus fuscus) • discriminate between two phantom target echoes separated by a time-delay of about 1  μs with waveforms of a bandwidth of up to 100 kHz (Megaderma lyra) •higher range resolution performance with respect to the conventional matched filter. The way bats process target echoes is different from the standard processing techniques used in radar and sonar, and there may be lessons to learn by investigating differences and similarities. The Spectrogram Correlation And Transformation receiver (SCAT) is an existing model of the bat auditory system that takes into account the physiology and underlying neural organisation in bats that emit chirped signals. The aims of this work are: •develop a baseband receiver equivalent to the SCAT to    - allow the application of biologically inspired signal processing to radar baseband signals  - enable further theoretical analysis of the key concepts, advantages and limitations of the “bat signal processing” •carry out simulations and experimen ts to investigate differences and similarities between the output (the frequency interference pattern for two closely located scatterers) of the original SCAT and that of the proposed baseband version

Biologically inspired processing of radar and sonar target echoes

Modern radar and sonar systems rely on active sensing to accomplish a variety of tasks, including detection and classification of targets, accurate localization and tracking, autonomous navigation and collision avoidance. Bats have relied on active sensing for over 50 million years and their echolocation system provides remarkable perceptual and navigational performance that are of envy to synthetic systems. The aim of this study is to investigate the mechanisms bats use to process echo acoustic signals and investigate if there are lessons that can be learned and ultimately applied to radar systems. The basic principles of the bat auditory system processing are studied and applied to radio frequencies. A baseband derivative of the Spectrogram Correlation and Transformation (SCAT) model of the bat auditory system, called Baseband SCAT (BSCT), has been developed. The BSCT receiver is designed for processing radio-frequency signals and to allow an analytical treatment of the expected performance. Simulations and experiments have been carried out to confirm that the outputs of interest of both models are “equivalent”. The response of the BSCT to two closely spaced targets is studied and it is shown that the problem of measuring the relative distance between two targets is converted to a problem of measuring the range to a single target. Nearly double improvement in the resolution between two close scatterers is achieved with respect to the matched filter. The robustness of the algorithm has been demonstrated through laboratory measurements using ultrasound and radio frequencies (RF). Pairs of spheres, flat plates and vertical rods were used as targets to represent two main reflectors

Baseband version of the bat-inspired spectrogram correlation and transformation receiver

Echolocating bats have evolved an excellent ability to detect and discriminate targets in highly challenging environments. They have had more than 50 million years of evolution to optimise their echolocation system with respect to their surrounding environment. Behavioural experiments have shown their exceptional ability to detect and classify targets even in highly cluttered surroundings. The way bats process signals is not exactly the same as in radar and hence it can be useful to investigate the differences. The Spectrogram Correlation And Transformation receiver (SCAT) is an existing model of the bat auditory system that takes into account the physiology and underlying neural organisation in bats which emit chirped signals. In this paper, we propose a baseband receiver equivalent to the SCAT. This will allow biologically inspired signal processing to be applied to radar baseband signals. It will also enable further theoretical analysis of the key concepts, advantages and limitations of the "bat signal processing" for the purpose of target detection, localisation and resolution. The equivalence is demonstrated by comparing the output of the original SCAT to that of our proposed baseband version using both simulated and experimental target echoes. Results show that the baseband receiver provides compatible frequency interference pattern for two closely located scatterers

Bio-inspired two target resolution at radio frequencies

Echolocating bats show a unique ability to detect, resolve and discriminate targets. The Spectrogram Correlation and Transformation (SCAT) receiver is a model of the Eptesicus fuscus auditory system that presents key signal processing differences compared to radar which may offer useful lessons for improvement. A baseband version of the SCAT is used to investigate advantages and disadvantages of bat-like signal processing against the task of target resolution. The baseband receiver is applied to RF experimental data and results show higher range resolution than the reciprocal of the transmitted bandwidth can be achieved for two closely spaced scatterers

Bio-inspired processing of radar target echoes

Echolocating bats have evolved the ability to detect, resolve and discriminate targets in highly challenging environments using biological sonar. The way bats process signals in the receiving auditory system is not the same as that of radar and sonar and hence investigating differences and similarities might provide useful lessons to improve synthetic sensors. The Spectrogram Correlation And Transformation (SCAT) receiver is an existing model of the bat auditory system that takes into account the physiology and the neural organisation of bats that emit broadband signals. In this study, the authors present a baseband receiver equivalent to the SCAT that allows an analysis of target echoes at baseband. The baseband SCAT (BSCT) is used to investigate the output of the bat-auditory model for two closely spaced scatterers and to carry out an analysis of range resolution performance and a comparison with the conventional matched filter. Results firstly show that the BSCT provides improved resolution performance. It is then demonstrated that the output of the BSCT can be obtained with an equivalent matched-filter based receiver. The results are verified with a set of laboratory experiments at radio frequencies in a high signal-to-noise ratio

Modeling of aircraft jet noise in airports

A mathematical model with 4 degree of freedom created in Matlab for aircraft final landing trajectory is described in this paper. A midsize commercial passenger aircraft similar to an Airbus A320 has been chosen as a reference aircraft. The parameters of model are obtained from Airbus, Eurocontrol and the approach procedure at the Munich airport is selected up from Jeppesen Airway manual. A semi-empirical model of Stone for predicting the jet noise has been used. The proposed model was validated against 10 real flights obtained from Aircraft noise monitoring at Munich airport. The computed error between the real data and modelling is reported on. Obtained results are presented numerical and graphically. The observed effects of aircraft speed, aircraft angle of descent and aircraft weight for reduction of aircraft jet noise in airports represent subjects of discussions in the paper

Predicting Financial Time Series for Value Investment

Value investment is an attractive paradigm for individual investors. It involves different steps including evaluating past performance that could be challenging. We propose a representation for financial time series in a form appropriate for both human interpretation and automatic processing. We design a model for predicting sequence of values as opposed to point values. Combined with application of encoder-decoder type of neural network model architecture this allows interpretation of model parameters and intermediate activations by domain experts. We show that predictions better than the trivial last observed value are possible. Therefore, informed investment decisions can be supported by neural network models and the proposed representation and model interpretation. &nbsp