Mulgrew, Bernard 1958
Overview
Works:  20 works in 42 publications in 2 languages and 467 library holdings 

Roles:  Author 
Classifications:  TK7872.F5, 621.3815324 
Publication Timeline
.
Most widely held works by
Bernard Mulgrew
Adaptive filters and equalisers by
Bernard Mulgrew(
Book
)
6 editions published in 1988 in English and held by 240 WorldCat member libraries worldwide
6 editions published in 1988 in English and held by 240 WorldCat member libraries worldwide
Digital signal processing : concepts and applications by
Bernard Mulgrew(
Book
)
18 editions published between 1998 and 2003 in English and held by 206 WorldCat member libraries worldwide
This book is a comprehensive introduction to digital signal processing which is a growing and important area for all aspiring electronics or communications engineers
18 editions published between 1998 and 2003 in English and held by 206 WorldCat member libraries worldwide
This book is a comprehensive introduction to digital signal processing which is a growing and important area for all aspiring electronics or communications engineers
Shu wei xun hao chu li by
Bernard Mulgrew(
Book
)
1 edition published in 2001 in Chinese and held by 4 WorldCat member libraries worldwide
Ben shu jie shao le xun hao yu xi tong xiang ying, zi liao qu yang xi tong, sui ji xun hao fen xi, pin pu fen xi de ji chu deng nei
1 edition published in 2001 in Chinese and held by 4 WorldCat member libraries worldwide
Ben shu jie shao le xun hao yu xi tong xiang ying, zi liao qu yang xi tong, sui ji xun hao fen xi, pin pu fen xi de ji chu deng nei
Special section on nonlinear and nonGaussian signal processing(
Book
)
1 edition published in 2004 in English and held by 1 WorldCat member library worldwide
1 edition published in 2004 in English and held by 1 WorldCat member library worldwide
Analogue and digital signal processing and coding(
Visual
)
1 edition published in 1989 in English and held by 1 WorldCat member library worldwide
1 edition published in 1989 in English and held by 1 WorldCat member library worldwide
Analogue and digital signal processing and coding(
Visual
)
1 edition published in 1989 in English and held by 1 WorldCat member library worldwide
1 edition published in 1989 in English and held by 1 WorldCat member library worldwide
Nonlinear noise cancellation by
Paul E Strauch(
)
1 edition published in 1997 in English and held by 1 WorldCat member library worldwide
Noise or interference is often assumed to be a random process. Conventional linear filtering, control or prediction techniques are used to cancel or reduce the noise. However, some noise processes have been shown to be nonlinear and deterministic. These nonlinear deterministic noise processes appear to be random when analysed with second order statistics. As nonlinear processes are widespread in nature it may be beneficial to exploit the coherence of the nonlinear deterministic noise with nonlinear filtering techniques. The nonlinear deterministic noise processes used in this thesis are generated from nonlinear difference or differential equations which are derived from real world scenarios. Analysis tools from the theory of nonlinear dynamics are used to determine an appropriate sampling rate of the nonlinear deterministic noise processes and their embedding dimensions. Nonlinear models, such as the Volterra series filter and the radial basis function network are trained to model or predict the nonlinear deterministic noise process in order to reduce the noise in a system. The nonlinear models exploit the structure and determinism and, therefore, perform better than conventional linear techniques. These nonlinear techniques are applied to cancel broadband nonlinear deterministic noise which corrupts a narrowband signal. An existing filter method is investigated and compared with standard linear techniques. A new filter method is devised to overcome the restrictions of the existing filter method. This method combines standard signal processing concepts (filterbanks and multirate sampling) with linear and nonlinear modelling techniques. It overcomes the restrictions associated with linear techniques and hence produces better performance. Other schemes for cancelling broadband noise are devised and investigated using quantisers and cascaded radial basis function networks. Finally, a scheme is devised which enables the detection of a signal of interest buried in heavy chaotic noise. Active noise control is another application where the acoustic noise may be assumed to be a nonlinear deterministic process. One of the problems in active noise control is the inversion process of the transfer function of the loudspeaker. This transfer function may be nonminimum phase. Linear controllers only perform suboptimally in modelling the noncausal inverse transfer function. To overcome this problem in conjunction with the assumption that the acoustic noise is nonlinear and deterministic a combined linear and nonlinear controller is devised. A mathematical expression for the combined controller is derived which consists of a linear system identification part and a nonlinear prediction part. The traditional filteredx least mean squares scheme in active noise control does not allow the implementation of a nonlinear controller. Therefore, a control scheme is devised to allow a nonlinear controller in conjunction with an adaptive block least squares algorithm. Simulations demonstrate that the combined linear and nonlinear controller outperforms the conventional linear controller
1 edition published in 1997 in English and held by 1 WorldCat member library worldwide
Noise or interference is often assumed to be a random process. Conventional linear filtering, control or prediction techniques are used to cancel or reduce the noise. However, some noise processes have been shown to be nonlinear and deterministic. These nonlinear deterministic noise processes appear to be random when analysed with second order statistics. As nonlinear processes are widespread in nature it may be beneficial to exploit the coherence of the nonlinear deterministic noise with nonlinear filtering techniques. The nonlinear deterministic noise processes used in this thesis are generated from nonlinear difference or differential equations which are derived from real world scenarios. Analysis tools from the theory of nonlinear dynamics are used to determine an appropriate sampling rate of the nonlinear deterministic noise processes and their embedding dimensions. Nonlinear models, such as the Volterra series filter and the radial basis function network are trained to model or predict the nonlinear deterministic noise process in order to reduce the noise in a system. The nonlinear models exploit the structure and determinism and, therefore, perform better than conventional linear techniques. These nonlinear techniques are applied to cancel broadband nonlinear deterministic noise which corrupts a narrowband signal. An existing filter method is investigated and compared with standard linear techniques. A new filter method is devised to overcome the restrictions of the existing filter method. This method combines standard signal processing concepts (filterbanks and multirate sampling) with linear and nonlinear modelling techniques. It overcomes the restrictions associated with linear techniques and hence produces better performance. Other schemes for cancelling broadband noise are devised and investigated using quantisers and cascaded radial basis function networks. Finally, a scheme is devised which enables the detection of a signal of interest buried in heavy chaotic noise. Active noise control is another application where the acoustic noise may be assumed to be a nonlinear deterministic process. One of the problems in active noise control is the inversion process of the transfer function of the loudspeaker. This transfer function may be nonminimum phase. Linear controllers only perform suboptimally in modelling the noncausal inverse transfer function. To overcome this problem in conjunction with the assumption that the acoustic noise is nonlinear and deterministic a combined linear and nonlinear controller is devised. A mathematical expression for the combined controller is derived which consists of a linear system identification part and a nonlinear prediction part. The traditional filteredx least mean squares scheme in active noise control does not allow the implementation of a nonlinear controller. Therefore, a control scheme is devised to allow a nonlinear controller in conjunction with an adaptive block least squares algorithm. Simulations demonstrate that the combined linear and nonlinear controller outperforms the conventional linear controller
Analogue and digital signal processing and coding(
Visual
)
1 edition published in 1989 in English and held by 1 WorldCat member library worldwide
1 edition published in 1989 in English and held by 1 WorldCat member library worldwide
A Bayesian framework for multiple acoustic source tracking by Xionghu Zhong(
)
1 edition published in 2010 in English and held by 1 WorldCat member library worldwide
Acoustic source (speaker) tracking in the room environment plays an important role in many speech and audio applications such as multimedia, hearing aids and handsfree speech communication and teleconferencing systems; the position information can be fed into a higher processing stage for highquality speech acquisition, enhancement of a specific speech signal in the presence of other competing talkers, or keeping a camera focused on the speaker in a videoconferencing scenario. Most of existing systems focus on the single source tracking problem, which assumes one and only one source is active all the time, and the state to be estimated is simply the source position. However, in practical scenarios, multiple speakers may be simultaneously active, and the tracking algorithm should be able to localise each individual source and estimate the number of sources. This thesis contains three contributions towards solutions to multiple acoustic source tracking in a moderate noisy and reverberant environment. The first contribution of this thesis is proposing a timedelay of arrival (TDOA) estimation approach for multiple sources. Although the phase transform (PHAT) weighted generalised crosscorrelation (GCC) method has been employed to extract the TDOAs of multiple sources, it is primarily used for a single source scenario and its performance for multiple TDOA estimation has not been comprehensively studied. The proposed approach combines the degenerate unmixing estimation technique (DUET) and GCC method. Since the speech mixtures are assumed windowdisjoint orthogonal (WDO) in the timefrequency domain, the spectrograms can be separated by employing DUET, and the GCC method can then be applied to the spectrogram of each individual source. The probabilities of detection and false alarm are also proposed to evaluate the TDOA estimation performance under a series of experimental parameters. Next, considering multiple acoustic sources may appear nonconcurrently, an extended Kalman particle filtering (EKPF) is developed for a special multiple acoustic source tracking problem, namely "nonconcurrent multiple acoustic tracking (NMAT)". The extended Kalman filter (EKF) is used to approximate the optimum weights, and the subsequent particle filtering (PF) naturally takes the previous position estimates as well as the current TDOA measurements into account. The proposed approach is thus able to lock on the sharp change of the source position quickly, and avoid the trackinglag in the general sequential importance resampling (SIR) PF. Finally, these investigations are extended into an approach to track the multiple unknown and timevarying number of acoustic sources. The DUETGCC method is used to obtain the TDOA measurements for multiple sources and a random finite set (RFS) based Raoblackwellised PF is employed and modified to track the sources. Each particle has a RFS form encapsulating the states of all sources and is capable of addressing source dynamics: source survival, new source appearance and source deactivation. A data association variable is defined to depict the source dynamic and its relation to the measurements. The Raoblackwellisation step is used to decompose the state: the source positions are marginalised by using an EKF, and only the data association variable needs to be handled by a PF. The performances of all the proposed approaches are extensively studied under different noisy and reverberant environments, and are favorably comparable with the existing tracking techniques
1 edition published in 2010 in English and held by 1 WorldCat member library worldwide
Acoustic source (speaker) tracking in the room environment plays an important role in many speech and audio applications such as multimedia, hearing aids and handsfree speech communication and teleconferencing systems; the position information can be fed into a higher processing stage for highquality speech acquisition, enhancement of a specific speech signal in the presence of other competing talkers, or keeping a camera focused on the speaker in a videoconferencing scenario. Most of existing systems focus on the single source tracking problem, which assumes one and only one source is active all the time, and the state to be estimated is simply the source position. However, in practical scenarios, multiple speakers may be simultaneously active, and the tracking algorithm should be able to localise each individual source and estimate the number of sources. This thesis contains three contributions towards solutions to multiple acoustic source tracking in a moderate noisy and reverberant environment. The first contribution of this thesis is proposing a timedelay of arrival (TDOA) estimation approach for multiple sources. Although the phase transform (PHAT) weighted generalised crosscorrelation (GCC) method has been employed to extract the TDOAs of multiple sources, it is primarily used for a single source scenario and its performance for multiple TDOA estimation has not been comprehensively studied. The proposed approach combines the degenerate unmixing estimation technique (DUET) and GCC method. Since the speech mixtures are assumed windowdisjoint orthogonal (WDO) in the timefrequency domain, the spectrograms can be separated by employing DUET, and the GCC method can then be applied to the spectrogram of each individual source. The probabilities of detection and false alarm are also proposed to evaluate the TDOA estimation performance under a series of experimental parameters. Next, considering multiple acoustic sources may appear nonconcurrently, an extended Kalman particle filtering (EKPF) is developed for a special multiple acoustic source tracking problem, namely "nonconcurrent multiple acoustic tracking (NMAT)". The extended Kalman filter (EKF) is used to approximate the optimum weights, and the subsequent particle filtering (PF) naturally takes the previous position estimates as well as the current TDOA measurements into account. The proposed approach is thus able to lock on the sharp change of the source position quickly, and avoid the trackinglag in the general sequential importance resampling (SIR) PF. Finally, these investigations are extended into an approach to track the multiple unknown and timevarying number of acoustic sources. The DUETGCC method is used to obtain the TDOA measurements for multiple sources and a random finite set (RFS) based Raoblackwellised PF is employed and modified to track the sources. Each particle has a RFS form encapsulating the states of all sources and is capable of addressing source dynamics: source survival, new source appearance and source deactivation. A data association variable is defined to depict the source dynamic and its relation to the measurements. The Raoblackwellisation step is used to decompose the state: the source positions are marginalised by using an EKF, and only the data association variable needs to be handled by a PF. The performances of all the proposed approaches are extensively studied under different noisy and reverberant environments, and are favorably comparable with the existing tracking techniques
Analogue and digital signal processing and coding(
Visual
)
1 edition published in 1989 in English and held by 1 WorldCat member library worldwide
1 edition published in 1989 in English and held by 1 WorldCat member library worldwide
Nonlinear processing of nonGaussian stochastic and chaotic deterministic time series by Mark Cowper(
)
1 edition published in 2000 in English and held by 1 WorldCat member library worldwide
1 edition published in 2000 in English and held by 1 WorldCat member library worldwide
Development of fuzzy system based channel equalisers by Sarat Kumar Patra(
)
1 edition published in 1998 in English and held by 1 WorldCat member library worldwide
1 edition published in 1998 in English and held by 1 WorldCat member library worldwide
Bearing estimation techniques for improved performance spread spectrum receivers by
John S Thompson(
)
1 edition published in 1996 in English and held by 1 WorldCat member library worldwide
The main topic of this thesis is the use of bearing estimation techniques combined with multiple antenna elements for spread spectrum receivers. The motivation behind this work is twofold: firstly, this type of receiver structure may offer the ability to locate the position of a mobile radio in an urban environment. Secondly, these algorithms permit the application of space division multiple access (SDMA) to cellular mobile radio, which can offer large system capacity increases. The structure of these receivers may naturally be divided into two parts: signal detection and spatial filtering blocks. The signal detection problem involves locating the bearings of the multipath components which arise from the transmission of the desired user's signal. There are a number of approaches to this problem, but here the MUSIC algorithm will be adopted. This algorithm requires an initial estimate of the number of signals impinging on the receiver, a task which can be performed by model order determination techniques. A major deficiency of MUSIC is its inability to resolve the highlycorrelated and coherent multipath signals which frequently occur in a spread spectrum system. One of the simplest ways to overcome this problem is to employ spatial smoothing techniques, which trade the size of the antenna array for the ability to resolve coherent signals. The minimum description length (MDL) is one method for determining the signal model order and it can easily be extended to calculating the required degree of spatial smoothing. In this thesis, an approach to analysing the probability of correct model order determination for the MDL with spatial smoothing is presented. The performance of MUSIC, combined with spatial smoothing, is also of great significance. Two smoothing algorithms, spatial smoothing and forwardbackward spatial smoothing, are analysed to compare their performance. If SDMA techniques are to be deployed in cellular systems, it is important to first estimate the performance improvements available from applying antenna array spatial filters. Initially, an additive white Gaussian noise channel is used for estimating the capacity of a perfect powercontrolled code division multiple access system with SDMA techniques. Results suggest that the mean interference levels are almost halved as the antenna array size doubles, permitting large capacity increases. More realistic multipath models for urban cellular radio channels are also considered. If the transmitter gives rise to a number of point source multipath components, the bearing estimation receiver is able to capture the signal energy of each multipath. However, when a multipath component has significant angular spread, bearing estimation receivers need to combine separate directional components, at an increased cost in complexity, to obtain similar results to a matched filter. Finally, a source location algorithm for urban environments is presented, based on bearing estimation of multipath components. This algorithm requires accurate knowledge of the positions of the major multipath reflectors present in the environment. With this knowledge it is possible to determine the position of a transmitting mobile unit. Simulation results suggest that the algorithm is very sensitive to angular separation of the multipath components used for the source location technique
1 edition published in 1996 in English and held by 1 WorldCat member library worldwide
The main topic of this thesis is the use of bearing estimation techniques combined with multiple antenna elements for spread spectrum receivers. The motivation behind this work is twofold: firstly, this type of receiver structure may offer the ability to locate the position of a mobile radio in an urban environment. Secondly, these algorithms permit the application of space division multiple access (SDMA) to cellular mobile radio, which can offer large system capacity increases. The structure of these receivers may naturally be divided into two parts: signal detection and spatial filtering blocks. The signal detection problem involves locating the bearings of the multipath components which arise from the transmission of the desired user's signal. There are a number of approaches to this problem, but here the MUSIC algorithm will be adopted. This algorithm requires an initial estimate of the number of signals impinging on the receiver, a task which can be performed by model order determination techniques. A major deficiency of MUSIC is its inability to resolve the highlycorrelated and coherent multipath signals which frequently occur in a spread spectrum system. One of the simplest ways to overcome this problem is to employ spatial smoothing techniques, which trade the size of the antenna array for the ability to resolve coherent signals. The minimum description length (MDL) is one method for determining the signal model order and it can easily be extended to calculating the required degree of spatial smoothing. In this thesis, an approach to analysing the probability of correct model order determination for the MDL with spatial smoothing is presented. The performance of MUSIC, combined with spatial smoothing, is also of great significance. Two smoothing algorithms, spatial smoothing and forwardbackward spatial smoothing, are analysed to compare their performance. If SDMA techniques are to be deployed in cellular systems, it is important to first estimate the performance improvements available from applying antenna array spatial filters. Initially, an additive white Gaussian noise channel is used for estimating the capacity of a perfect powercontrolled code division multiple access system with SDMA techniques. Results suggest that the mean interference levels are almost halved as the antenna array size doubles, permitting large capacity increases. More realistic multipath models for urban cellular radio channels are also considered. If the transmitter gives rise to a number of point source multipath components, the bearing estimation receiver is able to capture the signal energy of each multipath. However, when a multipath component has significant angular spread, bearing estimation receivers need to combine separate directional components, at an increased cost in complexity, to obtain similar results to a matched filter. Finally, a source location algorithm for urban environments is presented, based on bearing estimation of multipath components. This algorithm requires accurate knowledge of the positions of the major multipath reflectors present in the environment. With this knowledge it is possible to determine the position of a transmitting mobile unit. Simulation results suggest that the algorithm is very sensitive to angular separation of the multipath components used for the source location technique
Nonlinear and nonGaussian signal processing(
Book
)
1 edition published in 2004 in English and held by 1 WorldCat member library worldwide
1 edition published in 2004 in English and held by 1 WorldCat member library worldwide
Cyclostationary blind equalisation in mobile communications by Jon Altuna(
)
1 edition published in 1998 in English and held by 1 WorldCat member library worldwide
Blind channel identification and equalisation are the processes by which a channel impulse response can be identified and proper equaliser filter coefficients can be obtained, without knowledge of the transmitted signal. Techniques that exploit cyclostationarity can reveal information about systems which are nonminimum phase; nonminimum phase channels cannot be identified using only secondorder statistics (SOS), because these do not contain the necessary phase information. Cyclostationary blind equalisation methods exploit the fact that, sampling the received signal at a rate higher than the transmitted signal symbol rate, the received signal becomes cyclostationary. In general, cyclostationary blind equalisers can identify a channel with less data than higherorder statistics (HOS) methods, and unlike these, no constraint is imposed on the probability distribution function of the input signal. Nevertheless, cyclostationary methods suffer from some drawbacks, such as the fact that some channels are unidentifiable when they exhibit a number of zeros equally spaced around the unit circle. In this thesis the performance of a cyclostationary blind channel identification algorithm combined with a maximumlikelihood sequence estimation receiver is analysed. The simulations were conducted in the panEuropean mobile communication system GSM environment and the performance of the blind technique was compared with conventional channel estimation methods using training. It is shown that although blind equalisation techniques can converge in a few hundred symbols in a timeinvariant channel environment, the degradation with respect to methods with training is still considerable. Yet, the fact that a dedicated training sequence is not needed makes blind techniques attractive, because the data used for training purposes can be reallocated as information data. In the concluding part of this thesis a new blind channel identification algorithm which combines methods that exploit cyclostationarity implicitly and explicitly is presented. It is shown that the properties of cyclostationary statistics are exploited in the new algorithm, and enhance the performance of the technique that solely exploits fractionallyspaced sampling. The algorithm is robust in the presence of correlated noise and interference from adjacent users
1 edition published in 1998 in English and held by 1 WorldCat member library worldwide
Blind channel identification and equalisation are the processes by which a channel impulse response can be identified and proper equaliser filter coefficients can be obtained, without knowledge of the transmitted signal. Techniques that exploit cyclostationarity can reveal information about systems which are nonminimum phase; nonminimum phase channels cannot be identified using only secondorder statistics (SOS), because these do not contain the necessary phase information. Cyclostationary blind equalisation methods exploit the fact that, sampling the received signal at a rate higher than the transmitted signal symbol rate, the received signal becomes cyclostationary. In general, cyclostationary blind equalisers can identify a channel with less data than higherorder statistics (HOS) methods, and unlike these, no constraint is imposed on the probability distribution function of the input signal. Nevertheless, cyclostationary methods suffer from some drawbacks, such as the fact that some channels are unidentifiable when they exhibit a number of zeros equally spaced around the unit circle. In this thesis the performance of a cyclostationary blind channel identification algorithm combined with a maximumlikelihood sequence estimation receiver is analysed. The simulations were conducted in the panEuropean mobile communication system GSM environment and the performance of the blind technique was compared with conventional channel estimation methods using training. It is shown that although blind equalisation techniques can converge in a few hundred symbols in a timeinvariant channel environment, the degradation with respect to methods with training is still considerable. Yet, the fact that a dedicated training sequence is not needed makes blind techniques attractive, because the data used for training purposes can be reallocated as information data. In the concluding part of this thesis a new blind channel identification algorithm which combines methods that exploit cyclostationarity implicitly and explicitly is presented. It is shown that the properties of cyclostationary statistics are exploited in the new algorithm, and enhance the performance of the technique that solely exploits fractionallyspaced sampling. The algorithm is robust in the presence of correlated noise and interference from adjacent users
Analogue and digital signal processing and coding(
Visual
)
1 edition published in 1989 in English and held by 1 WorldCat member library worldwide
1 edition published in 1989 in English and held by 1 WorldCat member library worldwide
On adaptive filter structure and performance by
Bernard Mulgrew(
)
1 edition published in 1987 in English and held by 1 WorldCat member library worldwide
1 edition published in 1987 in English and held by 1 WorldCat member library worldwide
Signal processing for airborne bistatic radar by Kian P Ong(
)
1 edition published in 2003 in English and held by 1 WorldCat member library worldwide
The major problem encountered by an airborne bistatic radar is the suppression of bistatic clutter. Unlike clutter echoes for a sidelooking airborne monostatic radar, bistatic clutter echoes are range dependent. Using training data from nearby range gates will result in widening of the clutter notch of STAP (spacetime adaptive processing) processor. This will cause target returns from slow relative velocity aircraft to be suppressed or even go undetected. Some means of Doppler compensation for mitigating the clutter range dependency must be carried out. This thesis investigates the nature of the clutter echoes with different radar configurations. A novel Doppler compensation method using Doppler interpolation in the angleDoppler domain and power correction for a JDL (joint domain localized) processor is proposed. Performing Doppler compensation in the Doppler domain, allows several different Doppler compensations to be carried out at the same time, using separate Doppler bins compensation. When using a JDL processor, a 2D Fourier transformation is required to transform spacetime domain training data into angularDoppler domain. Performing Doppler compensation in the spacetime domain requires Fourier transformations of the Doppler compensated training data to be carried out for every training range gate. The whole process is then repeated for every range gate under test. On the other hand, Fourier transformations of the training data are required only once for all range gates under test, when using Doppler interpolation. Before carrying out any Doppler compensation, the peak clutter Doppler frequency difference between the training range gate and the range gate under test, needs to be determined. A novel way of calculating the Doppler frequency difference that is robust to error in preknown parameters is also proposed. Reducing the computational cost of the STAP processor has always been the desire of any reduced dimension processors such as the JDL processor. Two methods of further reducing the computational cost of the JDL processor are proposed. A tuned DFT algorithm allow the size of the clutter sample covariance matrix of the JDL processor to be reduced by a factor proportional to the number of array elements, without losses in processor performance. Using alternate Doppler bins selection allows computational cost reduction, but with performance loss outside the clutter notch region. Different systems parameters are also used to evaluate the performance of the Doppler interpolation process and the JDL processor. Both clutter range and Doppler ambiguity exist in radar systems operating in medium pulse repetitive frequency mode. When suppressing range ambiguous clutter echoes, performing Doppler compensation for the clutter echoes arriving from the nearest ambiguous range alone, appear to be sufficient. Clutter sample covariance matrix is estimated using training data from the range or time or both dimension. Investigations on the number of range and time training data required for the estimation process in both spacetime and angularDoppler domain are carried out. Due to error in the Doppler compensation process, a method of using the minimum amount of range training data is proposed. The number of training data required for different clutter sample covariance matrix sizes is also evaluated. For Doppler interpolation and power correction JDL processor, the number of Doppler bins used can be increased, to reduce the amount of training data required, while maintaining certain desirable processor performance characteristics
1 edition published in 2003 in English and held by 1 WorldCat member library worldwide
The major problem encountered by an airborne bistatic radar is the suppression of bistatic clutter. Unlike clutter echoes for a sidelooking airborne monostatic radar, bistatic clutter echoes are range dependent. Using training data from nearby range gates will result in widening of the clutter notch of STAP (spacetime adaptive processing) processor. This will cause target returns from slow relative velocity aircraft to be suppressed or even go undetected. Some means of Doppler compensation for mitigating the clutter range dependency must be carried out. This thesis investigates the nature of the clutter echoes with different radar configurations. A novel Doppler compensation method using Doppler interpolation in the angleDoppler domain and power correction for a JDL (joint domain localized) processor is proposed. Performing Doppler compensation in the Doppler domain, allows several different Doppler compensations to be carried out at the same time, using separate Doppler bins compensation. When using a JDL processor, a 2D Fourier transformation is required to transform spacetime domain training data into angularDoppler domain. Performing Doppler compensation in the spacetime domain requires Fourier transformations of the Doppler compensated training data to be carried out for every training range gate. The whole process is then repeated for every range gate under test. On the other hand, Fourier transformations of the training data are required only once for all range gates under test, when using Doppler interpolation. Before carrying out any Doppler compensation, the peak clutter Doppler frequency difference between the training range gate and the range gate under test, needs to be determined. A novel way of calculating the Doppler frequency difference that is robust to error in preknown parameters is also proposed. Reducing the computational cost of the STAP processor has always been the desire of any reduced dimension processors such as the JDL processor. Two methods of further reducing the computational cost of the JDL processor are proposed. A tuned DFT algorithm allow the size of the clutter sample covariance matrix of the JDL processor to be reduced by a factor proportional to the number of array elements, without losses in processor performance. Using alternate Doppler bins selection allows computational cost reduction, but with performance loss outside the clutter notch region. Different systems parameters are also used to evaluate the performance of the Doppler interpolation process and the JDL processor. Both clutter range and Doppler ambiguity exist in radar systems operating in medium pulse repetitive frequency mode. When suppressing range ambiguous clutter echoes, performing Doppler compensation for the clutter echoes arriving from the nearest ambiguous range alone, appear to be sufficient. Clutter sample covariance matrix is estimated using training data from the range or time or both dimension. Investigations on the number of range and time training data required for the estimation process in both spacetime and angularDoppler domain are carried out. Due to error in the Doppler compensation process, a method of using the minimum amount of range training data is proposed. The number of training data required for different clutter sample covariance matrix sizes is also evaluated. For Doppler interpolation and power correction JDL processor, the number of Doppler bins used can be increased, to reduce the amount of training data required, while maintaining certain desirable processor performance characteristics
Adaptive Bayesian decision feedback equalizer for dispersive mobile radio channels by Sheng Chen(
)
1 edition published in 1995 in English and held by 1 WorldCat member library worldwide
1 edition published in 1995 in English and held by 1 WorldCat member library worldwide
Adaptive equalisation for impulsive noise environments by Apostolos T Georgiadis(
)
1 edition published in 2001 in English and held by 1 WorldCat member library worldwide
1 edition published in 2001 in English and held by 1 WorldCat member library worldwide
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