Nonlinearity in the RF sensing chain: single and multiple channel signal processing solutions

Lead Research Organisation: University of Edinburgh
Department Name: Sch of Engineering


In today's digital world the radio frequency (RF) spectrum is becoming increasingly crowded. This poses a new interference challenge for conventional radars that operate within this RF spectrum. Any interference that is received by the radar is removed by filtering in the front end of the receiver. Historically this technique would be sufficient for screening out the majority interfering signals however this is no longer the case. The front-end of a radar receiver comprises of multiple amplifiers which are designed to have a highly-linear gain response. In a bid to reduce manufacturing costs cheaper amplifiers with considerably smaller linear regions are being used in the RF sensing chain. The linearity of the radar receiver has been sacrificed with potentially devastating effects for the sensor's overall performance. Before any detected interference is filtered out by the radar receiver the cheap amplifiers are forced into their nonlinear region. This is a regime the amplifiers have not been designed to operate in and therefore the signals become significantly distorted. While the outside interference is still being filtered out by the receiver its presence has generated unwanted effects that have not been removed.

Before any counter measures can be investigated the nonlinear distortion being generated must first be characterised. Radars are not designed to operate with their receivers in the nonlinear regime therefore very few attempts have previously been made to characterise their behaviour whilst in this state. This characterisation will be the first stage of the PhD and will involve combining hardware measurements with system modelling. Once the modulation effects are fully understood methods aimed at suppressing them can be studied. Most modern radars are designed to have multiple receive channels. The first mitigation technique designed in this PhD will aim to make use of these channels. Operating a receive channel with reduced gain would allow it to remain highly-linear even in the presence of interference. By configuring a few of the channels to be low gain an undistorted version of the signal could be captured and compared with the corrupted outputs. This signal comparison can be used to supress the overall distortion observed in the radar channels. This technique should be fairly mode independent but relies on deliberately reducing the performance of the radar. The second proposed mitigation technique aims to uses the receiver's nonlinear products to predict the original signal detected by the radar. It relies on having an extremely well characterised nonlinear receiver and exploits a unique property of the radar signal known as sparsity. This technique is expected to be mode specific and will be based around a lot of the concepts from compressive sensing.

It is predicted that a combination of the mitigation techniques will need to be employed to combat the distortion in a real scenario. While some of the mitigation ideas are being adapted from related fields there is very little published work surrounding this subject in the radar literature.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/N509644/1 01/10/2016 30/09/2021
1940665 Studentship EP/N509644/1 01/09/2017 28/02/2021 Euan Ward
Description This project aimed to develop novel techniques to mitigate front-end nonlinear effects in modern radar systems. If modern radars are to become one of the most widely used and important sensors in the future they must overcome this nonlinear challenge. With this being a new area of research in the field of radar the first task for this project was to develop a technique for characterising the sensors' nonlinear behaviour. A novel behavioural modelling technique capable of capturing both the subtle nonlinear phase and amplitude distortion effects was developed and the results of the study were presented at the 2019 IEEE Radar Conference in Boston, USA. The second significant outcome from this PhD project was a study into how these complex nonlinear effects degrade the performance of a typical radar system. The behavioural model described previously was incorporated into a sophisticated nonlinear radar simulator which in turn was used to study the performance of modern radars when nonlinear effects are present. Furthermore, a simple nonlinear mitigation technique was tested using the advanced radar simulator to see if the performance of the sensor could be restored. The results from this package of work suggest that more sophisticated mitigation techniques will have to be employed if the performance of modern radars are to be maintained in the presence of nonlinearities. This work has been accepted for lecture presentation at the 2020 IEEE International Radar Conference in Washington DC.
Exploitation Route Currently work is being conducted to develop more sophisticated mitigation algorithms that can correct for the subtle nonlinear effects in radar. This work will form another major outcome from the PhD project. Additionally, experimental work is being conducted in tandem with the theoretical work with the aim of validating the simulation results in real hardware. Implementing the theoretical work in real hardware will significantly increase the industrial impact of the research.

There is considerable scope for further work continuing on from this PhD research in both the academic and industrial setting. New signal processing techniques have been developed as part of this PhD which are being applied to radar systems but will most likely find application elsewhere as well. Further research will need to be conducted to develop these novel signal processing techniques so that they may be applied to a variety of different systems. Within the field of radar the impact of the research could prove to be significant. By exploiting commercial off-the-shelf (COTS) components companies can now produce compact economically viable systems that can easily be productionised. However, these modern mass produced system will most likely experience nonlinear effects which will need to be corrected if the sensor is to maintain acceptable performance. Companies will look for signal processing solutions to this nonlinear problem as they can be implemented in post processing. For companies to exploit the novel signal processing techniques developed in this PhD work will have to be done on fast implementations of these algorithm so that they can run in real time.
Sectors Aerospace, Defence and Marine,Digital/Communication/Information Technologies (including Software)