Mitigation of nonlinear receiver effects in modern radar: advanced signal processing techniques

This thesis presents a study into nonlinearities in the radar receiver and investigates advanced digital signal processing (DSP) techniques capable of mitigating the resultant deleterious effects. The need for these mitigation techniques has become more prevalent as the use of commercial radar sensors has increased rapidly over the last decade. While advancements in low-cost radio frequency (RF) technologies have made mass-produced radar systems more feasible, they also pose a significant risk to the functionality of the sensor. One of the major compromises when employing low-cost commercial off-theshelf (COTS) components in the radar receiver is system linearity. This linearity trade-off leaves the radar susceptible to interfering signals as the RF receiver can now be driven into the weakly nonlinear regime. Radars are not designed to operate in the nonlinear regime as distortion is observed in the radar output if they do. If radars are to maintain operational performance in an RF environment that is becoming increasingly crowded, novel techniques that allow the sensor to operate in the nonlinear regime must be developed. Advanced DSP techniques offer a low-cost low-impact solution to the nonlinear receiver problem in modern radar. While there is very little work published on this topic in the radar literature, inspiration can be taken from the related field of communications where techniques have been successfully employed. It is clear from the communications literature that for any mitigation algorithm to be successful, the mechanisms driving the nonlinear distortion in the receiver must be understood in great detail. Therefore, a behavioural modelling technique capable of capturing both the nonlinear amplitude and phase effects in the radar receiver is presented before any mitigation techniques are studied. Two distinct groups of mitigation algorithms are then developed specifically for radar systems with their performance tested in the medium pulse repetition frequency (MPRF) mode of operation. The first of these is the look-up table (LUT) approach which has the benefit of being mode independent and computationally inexpensive to implement. The limitations of this communications-based technique are discussed with particular emphasis placed on its performance against receiver nonlinearities that exhibit complex nonlinear memory effects. The second group of mitigation algorithms to be developed is the forward modelling technique. While this novel technique is both mode dependent and computationally intensive to implement, it has a unique formalisation that allows it to be extended to include nonlinear memory effects in a well-defined manner. The performance of this forward modelling technique is analysed and discussed in detail. It was shown in this study that nonlinearities generated in the radar receiver can be successfully mitigated using advanced DSP techniques. For this to be the case however, the behaviour of the RF receiver must be characterised to a high degree of accuracy both in the linear and weakly nonlinear regimes. In the case where nonlinear memory effects are significant in the radar receiver, it was shown that memoryless mitigation techniques can become decorrelated drastically reducing their effectiveness. Importantly however, it was demonstrated that the LUT and forward modelling techniques can both be extended to compensate for complex nonlinear memory effects generated in the RF receiver. It was also found that the forward modelling technique dealt with the nonlinear memory effects in a far more robust manner than the LUT approach leading to a superior mitigation performance in the memory rich case

Study on Reduction of Computational Complexity for Volterra Filters by Using Multi-rate Signal Processing and Its Application to Identification of Loudspeaker Systems

関西大

Epälineaarinen vääristymä laajakaistaisissa analogia-digitaalimuuntimissa

This thesis discusses nonlinearities of analog-to-digital converters (ADCs) and their mitigation using digital signal processing (DSP). Particularly wideband radio receivers are considered here including, e.g., the emerging cognitive radio applications. In this kind of receivers, a single ADC converts a mixture of signals at different frequency bands to digital domain simultaneously. Different signals may have considerably different power levels and hence the overall dynamic range can be very large (even 50–60 dB). Therefore, even the smallest ADC nonlinearities can produce considerable amount of nonlinear distortion, which may cause a strong signal to block significantly weaker signal bands. One concrete source of nonlinear distortion is waveform clipping due to improper signal conditioning in the input of an ADC. In the thesis, a mathematical model for this phenomenon is derived through Fourier analysis and is then used as a basis for an adaptive interference cancellation (AIC) method. This is a general method for reducing nonlinear distortion and besides clipping it can be used, e.g., to compensate integral nonlinearity (INL) originating from unintentional deviations of the quantization levels. Additionally, an interpolation method is proposed in this thesis to restore clipped waveforms and hence reduce nonlinear distortion. Through several computer simulations and corresponding laboratory radio signal measurements, the performance of the proposed post-processing methods is illustrated. It can be seen from the results that the methods are able to reduce nonlinear distortion from a weak signal band in a considerable manner when there are strong blocking signals in the neighboring channels. According to the results, the AIC method would be a highly recommendable post-processing technique for modern radio receivers due to its general ability to reduce nonlinear distortion regardless of its source. /Kir10Tässä työssä käsitellään analogia-digitaalimuuntimien (AD-muuntimien) epälineaarisuuksia ja niiden lieventämistä digitaalisen signaalinkäsittelyn (DSP) avulla. Tätä on tarkasteltu erityisesti laajakaistaisten radiovastaanottimien näkökulmasta, joka käsittää mm. tulevat kognitiiviseen radioon liittyvät sovellukset. Tällaisissa vastaanottimissa yksittäinen AD-muunnin muuntaa samanaikaisesti useita eri taajuuskaistoilla olevia signaaleita digitaaliseen muotoon, jolloin yhteenlaskettu dynaaminen alue voi olla hyvin suuri (jopa 50–60 dB). Tämän takia AD-muuntimen pienimmätkin epälineaarisuudet voivat aiheuttaa huomattavasti epälineaarista vääristymää, minkä vuoksi voimakas signaali saattaa häiriöllään peittää muilla taajuuskaistoilla olevia selkeästi heikompia signaaleja. Eräs konkreettinen epälineaarisen vääristymän aiheuttaja on aaltomuodon leikkaantuminen AD-muuntimen sisäänmenossa jännitealueen ylittymisen vuoksi. Tässä työssä johdetaan matemaattinen malli kyseiselle ilmiölle Fourier-analyysin avulla ja käytetään sitä lähtökohtana adaptiiviselle häiriönpoistomenetelmälle (AIC-menetelmä). Se on yleisluonteinen menetelmä epälineaarisen vääristymän vähentämiseksi, ja leikkaantumisen lisäksi sitä voidaan käyttää esimerkiksi kompensoimaan integraalista epälineaarisuutta (INL), joka on peräisin kvantisointitasojen tahattomista poikkeamista. Lisäksi tässä työssä esitellään interpolointimenetelmä leikkaantuneen aaltomuodon ehostamiseen siten, että epälineaarinen häiriö vähenee. Esiteltyjen jälkikäsittelymenetelmien suorituskykyä analysoidaan ja havainnollistetaan useilla tietokonesimulaatiolla sekä niitä vastaavilla radiosignaalien laboratoriomittauksilla. Tuloksista voidaan nähdä, että nämä menetelmät kykenevät poistamaan huomattavasti epälineaarista vääristymää heikolta signaalikaistalta silloin, kun naapurikaistoilla on voimakkaita häiriösignaaleja. Tulosten perusteella AIC-menetelmä olisi erittäin suositeltava jälkikäsittelytekniikka moderneihin radiovastaanottimiin, koska se pystyy yleisesti vähentämään epälineaarista vääristymää riippumatta häiriön alkuperästä

Circuit paradigm in the 21

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Graph Filters for Signal Processing and Machine Learning on Graphs

Filters are fundamental in extracting information from data. For time series and image data that reside on Euclidean domains, filters are the crux of many signal processing and machine learning techniques, including convolutional neural networks. Increasingly, modern data also reside on networks and other irregular domains whose structure is better captured by a graph. To process and learn from such data, graph filters account for the structure of the underlying data domain. In this article, we provide a comprehensive overview of graph filters, including the different filtering categories, design strategies for each type, and trade-offs between different types of graph filters. We discuss how to extend graph filters into filter banks and graph neural networks to enhance the representational power; that is, to model a broader variety of signal classes, data patterns, and relationships. We also showcase the fundamental role of graph filters in signal processing and machine learning applications. Our aim is that this article provides a unifying framework for both beginner and experienced researchers, as well as a common understanding that promotes collaborations at the intersections of signal processing, machine learning, and application domains

Nonlinear Distortion in Wideband Radio Receivers and Analog-to-Digital Converters: Modeling and Digital Suppression

Emerging wireless communications systems aim to flexible and efficient usage of radio spectrum in order to increase data rates. The ultimate goal in this field is a cognitive radio. It employs spectrum sensing in order to locate spatially and temporally vacant spectrum chunks that can be used for communications. In order to achieve that, flexible and reconfigurable transceivers are needed. A software-defined radio can provide these features by having a highly-integrated wideband transceiver with minimum analog components and mostly relying on digital signal processing. This is also desired from size, cost, and power consumption point of view. However, several challenges arise, from which dynamic range is one of the most important. This is especially true on receiver side where several signals can be received simultaneously through a single receiver chain. In extreme cases the weakest signal can be almost 100 dB weaker than the strongest one. Due to the limited dynamic range of the receiver, the strongest signals may cause nonlinear distortion which deteriorates spectrum sensing capabilities and also reception of the weakest signals. The nonlinearities are stemming from the analog receiver components and also from analog-to-digital converters (ADCs). This is a performance bottleneck in many wideband communications and also radar receivers. The dynamic range challenges are already encountered in current devices, such as in wideband multi-operator receiver scenarios in mobile networks, and the challenges will have even more essential role in the future.This thesis focuses on aforementioned receiver scenarios and contributes to modeling and digital suppression of nonlinear distortion. A behavioral model for direct-conversion receiver nonlinearities is derived and it jointly takes into account RF, mixer, and baseband nonlinearities together with I/Q imbalance. The model is then exploited in suppression of receiver nonlinearities. The considered method is based on adaptive digital post-processing and does not require any analog hardware modification. It is able to extract all the necessary information directly from the received waveform in order to suppress the nonlinear distortion caused by the strongest blocker signals inside the reception band.In addition, the nonlinearities of ADCs are considered. Even if the dynamic range of the analog receiver components is not limiting the performance, ADCs may cause considerable amount of nonlinear distortion. It can originate, e.g., from undeliberate variations of quantization levels. Furthermore, the received waveform may exceed the nominal voltage range of the ADC due to signal power variations. This causes unintentional signal clipping which creates severe nonlinear distortion. In this thesis, a Fourier series based model is derived for the signal clipping caused by ADCs. Furthermore, four different methods are considered for suppressing ADC nonlinearities, especially unintentional signal clipping. The methods exploit polynomial modeling, interpolation, or symbol decisions for suppressing the distortion. The common factor is that all the methods are based on digital post-processing and are able to continuously adapt to variations in the received waveform and in the receiver itself. This is a very important aspect in wideband receivers, especially in cognitive radios, when the flexibility and state-of-the-art performance is required