Showing papers on "Adaptive filter published in 1990"

Adaptive Algorithms and Stochastic Approximations

Abstract: Adaptive systems are widely encountered in many applications ranging through adaptive filtering and more generally adaptive signal processing, systems identification and adaptive control, to pattern recognition and machine intelligence: adaptation is now recognised as keystone of "intelligence" within computerised systems. These diverse areas echo the classes of models which conveniently describe each corresponding system. Thus although there can hardly be a "general theory of adaptive systems" encompassing both the modelling task and the design of the adaptation procedure, nevertheless, these diverse issues have a major common component: namely the use of adaptive algorithms, also known as stochastic approximations in the mathematical statistics literature, that is to say the adaptation procedure (once all modelling problems have been resolved). The juxtaposition of these two expressions in the title reflects the ambition of the authors to produce a reference work, both for engineers who use these adaptive algorithms and for probabilists or statisticians who would like to study stochastic approximations in terms of problems arising from real applications. Hence the book is organised in two parts, the first one user-oriented, and the second providing the mathematical foundations to support the practice described in the first part. The book covers the topcis of convergence, convergence rate, permanent adaptation and tracking, change detection, and is illustrated by various realistic applications originating from these areas of applications.

Adaptive speckle filters and scene heterogeneity

Abstract: The presence of speckle in radar images makes the radiometric and textural aspects less efficient for class discrimination. Many adaptive filters have been developed for speckle reduction, the most well known of which are analyzed. It is shown that they are based on a test related to the local coefficient of variation of the observed image, which describes the scene heterogeneity. Some practical criteria are introduced to modify the filters in order to make them more efficient. The filters are tested on a simulated synthetic aperture radar (SAR) image and an SAR-580 image. As was expected, the new filters perform better, i.e. they average the homogeneous areas better and preserve texture information, edges, linear features, and point target responses better at the same time. Moreover, they can be adapted to features other than the coefficient of variation to reduce the speckle while preserving the corresponding information. >

Federated square root filter for decentralized parallel processors

Abstract: An efficient, federated Kalman filter is developed for use in distributed multisensor systems. The design accommodates sensor-dedicated local filters, some of which use data from a common reference subsystem. The local filters run in parallel, and provide sensor data compression via prefiltering. The master filter runs at a selectable reduced rate, fusing local filter outputs via efficient square root algorithms. Common local process noise correlations are handled by use of a conservative matrix upper bound. The federated filter yields estimates that are globally optimal or conservatively suboptimal, depending upon the master filter processing rate. This design achieves a major improvement in throughput (speed), is well suited to real-time system implementation, and enhances fault detection, isolation, and recovery capability. >

Neural nets for adaptive filtering and adaptive pattern recognition

Abstract: The adaptive linear combiner (ALC) is described, and practical applications of the ALC in signal processing and pattern recognition are presented. Six signal processing examples are given, which are system modeling, statistical prediction, noise canceling, echo canceling, universe modeling, and channel equalization. Adaptive pattern recognition using neural nets is then discussed. The concept involves the use of an invariance net followed by a trainable classifier. It makes use of a multilayer adaptation algorithm that descrambles output and reproduces original patterns. >

Multidelay block frequency domain adaptive filter

Abstract: A flexible multidelay block frequency domain (MDF) adaptive filter is presented. The distinctive feature of the MDF adaptive filter is to allow one to choose the size of an FFT tailored to the efficient use of the hardware, rather than the requirements of a specific application. The MDF adaptive filter also requires less memory and thus reduces the hardware requirements and cost. In performance, the MDF adaptive filter introduces smaller block delay and is faster, making it ideal for a time-varying application such as modeling an acoustic path in a teleconference room. This is achieved by using a smaller block size, updating the weight vectors more often, and reducing the total execution time of the adaptive process. The MDF adaptive filter compares favorably to other frequency-domain adaptive filters when its adaptation speed and misadjustment are tested in computer simulations. >

Development of the filtered‐U algorithm for active noise control

Abstract: The filtered‐X algorithm developed by Widrow and Burgess is an alternate form of the least‐mean‐square (LMS) algorithm for use when there are transfer functions in the auxiliary path following the adaptive filter. To ensure convergence of the algorithm, the input to the error correlators is filtered by a copy of these auxiliary path transfer functions. More recently, the author has presented a new approach to active noise control in the presence of acoustic feedback that uses an infinite impulse response (IIR) filter structure with an alternate form of the recursive least‐mean‐square (RLMS) algorithm. This algorithm may be described as a filtered‐U algorithm since it uses a copy of the auxiliary path transfer functions to filter the generalized input vector U to the error correlators of both the direct and recursive elements of the filter to ensure convergence. The relationship of the filtered‐U to the filtered‐X algorithm and other earlier concepts is discussed.

Nonlinear techniques for interference suppression in spread-spectrum systems

Abstract: The binary nature of direct-sequence signals is exploited to obtain nonlinear filters that outperform the linear filters hitherto used for this purpose. The case of a Gaussian interferer with known autoregressive parameters is considered. Using simulations, it is shown that an approximate conditional mean (ACM) filter of the Masreliez type performs significantly better than the optimum linear (Kalman-Bucy) filter. For the case of interferers with unknown parameters, the nature of the nonlinearity in the ACM filter is used to obtain an adaptive filtering algorithm that is identical to the linear transversal filter except that the previous prediction errors are transformed nonlinearly before being incorporated into the linear prediction. Two versions of this filter are considered: one in which the filter coefficients are updated using the Widrow LMS algorithm, and another in which the coefficients are updated using an approximate gradient algorithm. Simulations indicate that the nonlinear filter with LMS updates performs substantially better than the linear filter for both narrowband Gaussian and single-tone interferers, whereas the gradient algorithm gives slightly better performance for Gaussian interferers but is rather ineffective in suppressing a sinusoidal interferer. >

Adaptive estimation of power system frequency deviation and its rate of change for calculating sudden power system overloads

Abstract: A novel Kalman filtering-based technique is presented for estimating power system frequency deviation and its average rate of change during emergency operating conditions that may require load shedding. This method obtains the optimal estimate of the power system frequency deviation from noisy voltage samples and the best estimate of the mean system frequency deviation and its rate of change while accounting for low-frequency synchronizing oscillations which occur during large disturbances. The proposed technique is a two-stage algorithm which uses an adaptive extended Kalman filter in series with an adaptive linear Kalman filter. The extended Kalman filter calculates the frequency deviation, magnitude, and phase angle of the voltage phasor, which may change during the time period covered by the estimation window. Both the measurement noise variance and the system noise covariance associated with the voltage samples are calculated online. The instantaneous frequency deviation is used as the input to a linear Kalman filter, which models the frequency deviation as a random walk plus a random ramp process. The estimated average rate of frequency decay is represented by the slope of the random ramp. Results for both single and multiple measurements are reported. >

Gradient-based adaptive IIR notch filtering for frequency estimation

Abstract: The use of gradient-based algorithms with infinite impulse response (IIR) notch filtering for estimating sinusoids imbedded in noise is investigated. Two notch filter model structures are presented. The first is for applications where two signal sources with correlated noise components can be assessed. The second can be used in situations where only one composite signal source is available. Error surface analysis indicates that second-order modules are unimodal and result in guaranteed convergence. Higher-order modules are multimodal and require judicious choice of initial parameter estimates. Simulation results are included to demonstrate the performance characteristics of both filter structures. >

Temporal backpropagation for FIR neural networks

Abstract: The traditional feedforward neural network is a static structure which simply maps input to output. To better reflect the dynamics in a biological system, a network structure which models each synapse by a finite-impulse response (FIR) linear filter is proposed. An efficient-gradient descent algorithm which is shown to be a temporal generalization of the familiar backpropagation algorithm is derived. By modeling each synapse as a linear filter, the neural network as a whole may be thought of as an adaptive system with its own internal dynamics. Equivalently, one may think of the network as a complex nonlinear filter. Applications should thus include areas of pattern recognition where there is an inherent temporal quality to the data, such as in speech recognition. The networks should also find a natural use in areas of nonlinear control, and other adaptive signal processing and filtering applications such as noise cancellation or equalization

Linear Prediction Theory: A Mathematical Basis for Adaptive Systems

Abstract: The theory presented in this work forms the basis of many algorithms for parameter estimation, adaptive system identification, and adaptive filtering. Linear prediction theory has applications in such fields as communications, control, radar and sonar systems, geophysics, estimation of economic processes, and training problems in synthetic neural nets. Emphasis is placed on three main areas. First, the mathematical tools required for the most important linear prediction algorithms are derived in a unified framework. Second, the relationships between different approaches are pointed out, thus allowing the selection of the optimal technique for a particular problem. Third, the material is presented in the context of results in algorithm research, with many references to publications in the field. The book is suitable for a graduate course on adaptive signal processing and should be useful for practising engineers faced with the problem of designing systems for operation in time-varying environments.

Switching adaptive filters for enhancing noisy and reverberant speech from microphone array recordings

Abstract: Switching adaptive filters, suitable for speech beamforming, with no prior knowledge about the speech source are presented. The filters have two sections, of which only one section at any given time is allowed to adapt its coefficients. The switch between both is controlled by a speech detection function. The first section implements an adaptive look direction and cues in on the desired speech. This section only adapts when speech is present. The second section acts as a multichannel adaptive noise canceller. The obtained noise references are typically very bad; hence, adaptation must be restricted to silence-only periods. Several ideas were explored for the first section. The most robust solution, and the one with the best sound quality, was given by the simplest solution, i.e. a delay and sum beamformer that cues in on the direct path only and neglects all multipath contributions. Tests were performed with a four-microphone array in a highly reverberant room with both music and fan type noise as jammers, SNR improvements of 10 dB were typical with no audible distortion. >

Adaptive stack filtering under the mean absolute error criterion

Abstract: An adaptive filter algorithm is developed for the class of stack filters, which is a class of nonlinear filters obeying a weak superposition property. The adaptation algorithm can be interpreted as a learning algorithm for a group of decision-making units, the decisions of which are subject to a set of constraints called the stacking constraints. Under a rather weak statistical assumption on the training inputs, the decision strategy adopted by the group, which evolves according to the proposed learning algorithm, is shown to converge to an optimal strategy in the sense that it corresponds to an optimal stack filter under the mean absolute error-criterion, this adaptive algorithm requires only increment, decrement, and comparison operations and only local interconnections between the learning units. Implementation of the algorithm in hardware is therefore very feasible. An example is provided to show how the adaptive stack filtering algorithm can be used in an application in image processing. >

Decision feedback equalization for digital cellular radio

Abstract: The authors study an adaptive decision feedback equalizer (DFE) for application in the USA digital cellular radio telephone system. A synchronous DFE and a fractionally spaced DFE are adaptive and use a fast recursive least squares algorithm to track rapid channel variations. Simulation results indicating the performance sensitivity to time delay spread, Doppler shift, and timing jitter are presented. A DFE using a complex fast-Kalman adaptation algorithm is presented, and its bit error rate performance evaluated. The fast Kalman equalizer is found to possess good tracking ability and can track channel variations at vehicle speeds of 50 mph (80 km/h). Sensitivity to sample timing jitter can be reduced by using a DFE with fractionally spaced feedforward taps. >

Performance analysis of LMS adaptive prediction filters

Abstract: The conditions required to implement real-time adaptive prediction filters that provide nearly optimal performance in realistic input conditions are delineated. The effects of signal bandwidth, input signal-to-noise ratio (SNR), noise correlation, and noise nonstationarity are explicitly considered. Analytical modeling, Monte Carlo simulations and experimental results obtained using a hardware implementation are utilized to provide performance bounds for specified input conditions. It is shown that there is a nonlinear degradation in the signal processing gain as a function of the input SNR that results from the statistical properties of the adaptive filter weights. The stochastic properties of the filter weights ensure that the performance of the adaptive filter is bounded by that of the optimal matched filter for known stationary input conditions. >

The fast adaptive ROTOR's RLS algorithm

Abstract: A fast algorithm for implementation of the QR-factorization-based recursive-least-squares (RLS) adaptive filter is discussed. This fast adaptive rotors (FAR) algorithm can be implemented with a pipelined array of processors called ROTORs and CISORs. The ROTORs compute 2*2 orthogonal (Givens) rotations, and the CISORs compute the cosines and sines of the angles used in the ROTORs. The algorithm requires 4N ROTORs and 2N CISORs at each iteration to compute the solution to the RLS problem. The algorithm is numerically stable. The FAR algorithm is derived using a single generic updating formula for orthogonal matrices, which is introduced and derived. Whereas the generic updating formula is reminiscent of previous fast transversal filters and fast lattice algorithms, the set of internally propogated adaptive filter quantities is entirely different and constitutes yet another complete characterization of the RLS covariance and the forward, backward, and pinning estimation problems. >

Optimized fast Hankel transform filters

Abstract: In the linear digital filter theory for calculation of Hankel transforms it is possible to find explicit series expansions for the filter coefficients. A method is presented for optimizing the Hankel filters calculated in this way. For a certain desired accuracy of computation, the sampling density and filter length are minimized by choosing the parameters determining the filter characteristics according to the analytical properties of the input function. A new approach to the calculation of the filter coefficients has been developed for these optimized filters. The length of the filters may be further reduced by introducing a shift in the sampling scheme.

Crosstalk analysis and filter optimization of single- and double-cavity Fabry-Perot filters

Abstract: Since Fabry-Perot (FP) filters are major candidates for use as demultiplexers in wavelength division multiple access (WDMA) networks, the authors have analyzed, in an exact and unified manner, the crosstalk degradation for several different variations of the basic FP filter, compared their performances, and optimized their design parameters. A description is given of the system model (a wavelength division multiple access system consisting of M connected transmitters and receivers with each transmitter consisting of an on-off modulated fixed-frequency laser, with the 'off' power level equal to zero) and the parameters used throughout, along with a brief discussion of relevant Fabry-Perot equations and terms and the four different filter structures analyzed. The crosstalk is examined for the cases of a single-cavity FP filter, a double-pass FP filter, a two-stage double-cavity FP filter, and a vernier double-cavity FP filter. The criteria for the performance comparison were the worst-case crosstalk, BER, and crosstalk power penalty. >

Adaptive box filters for removal of random noise from digital images

Abstract: Adaptive box-filtering algorithms to remove random bit errors and to smooth noisy data have been developed. For both procedures, the standard deviation of those pixels within a local box surrounding each pixel is used. A series of two or three filters with decreasing box sizes can be run to clean up extremely noisy images and to remove bit errors near sharp edges. The second filter, for noise smoothing, is similar to the 'sigma filter' of Lee (1983). The technique effectively reduces speckle in radar images without eliminating fine details.

Analysis and design of a signed regressor LMS algorithm for stationary and nonstationary adaptive filtering with correlated Gaussian data

Abstract: A least mean square (LMS) algorithm with clipped data is studied for use when updating the weights of an adaptive filter with correlated Gaussian input. Both stationary and nonstationary environments are considered. Three main contributions are presented. The first, corresponding to the stationary case, is a proof of the convergence of the algorithm in the case of a M-dependent sequence of correlated observation vectors. It is proven that the steady state mean square misalignment of the adaptive filter weights has an upper bound proportional to the algorithm step size mu . The second contribution, also belonging to the stationary case, is the derivation of the expressions of convergence time N/sub c/ and steady state mean square excess estimation error epsilon . It is shown that N/sub c/ is proportional to 1/( mu lambda ), with lambda being the minimum eigenvalue of the input covariance matrix. It is also shown that the product N/sub c/ epsilon is independent of mu . For a given epsilon , the convergence time increases with the eigenvalue spread of the input covariance matrix and the filter length, as well as its input noise power. The range of mu that achieves tolerable values of N/sub c/ and epsilon is determined. The third contribution is concerned with the nonstationary case. It is shown that the mean square excess estimation error is the sum of the two terms with opposite dependencies on mu . An optimum value of mu is derived. >

Radar signal categorization using a neural network

Abstract: Neural networks are used to analyze a complex simulated radar environment which contains noisy radar pulses generated by many different emitters. The neural network used is an energy-minimizing network. The limiting process contains the state vector within a set of limits, and this model is called the brain state in a box, or BSB model, which forms energy minima (attractors in the network dynamical system) based on learned input data. The system first determines how many emitters are present (the deinterleaving problem). Pulses from individual simulated emitters give rise to separate stable attractors in the network. Once individual emitters are characterized, it is possible to tentatively identify them based on their observed parameters. As a test of this idea, a neural network was used to form a small database that could potentially make emitter identification. There were three errors of classification. The number of iterations are required to reach an attractor state was very long, and some of the final states were not fully limited. These factors indicate the uncertainty of the neural network. >

Minimum mean absolute error estimation over the class of generalized stack filters

Abstract: A class of sliding window operators called generalized stack filters is developed. This class of filters, which includes all rank order filters, stack filters, and digital morphological filters, is the set of all filters possessing the threshold decomposition architecture and a consistency property called the stacking property. Conditions under which these filters possess the weak superposition property known as threshold decomposition are determined. An algorithm is provided for determining a generalized stack filter which minimizes the mean absolute error (MAE) between the output of the filter and a desired input signal, given noisy observations of that signal. The algorithm is a linear program whose complexity depends on the window width of the filter and the number of threshold levels observed by each of the filters in the superposition architecture. The results show that choosing the generalized stack filter which minimizes the MAE is equivalent to massively parallel threshold-crossing decisions making when the decision are consistent with each other. >

Communication system with active noise cancellation

Abstract: An active acoustic attenuation system (10) is provided with various adaptive filter models (40, 48, 56, 70, 84, 100) enabling communication between persons (26, 30) in spaced zones (12, 16) by selectively cancelling undesired noise and undesired speech, all on an on-line basis without dedicated off-line pretraining and also for both broadband and narrowband noise.

Multidimensional signal processing for electro-optical target detection

Abstract: This paper describes the evolutionary development of adaptive signal processing algorithms which utilize spatial and spectral information provided by a passive infrared sensor to enhance the detectability of targets in clutter. Key parameters affecting the performance of multi-spectral detection processors are identified and discussed. Adaptive filtering algorithms are presented which can achieve near-optimum detection performance with no prior knowledge of the target and background spectral properties.© (1990) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Performance of adaptive matched filter receivers over fading multipath channels

Abstract: A unified discrete channel model from the information source up to the sampler was developed for fading multipath channels. Different methods for adaptive channel measurement was studied. The performance of a discrete matched filter using different adaptation techniques and working over a troposcatter channel is predicted. It is shown that the effects of channel measurement noise are less damaging for the decision-directed adaptation technique as compared to any kind of reference-directed adaptation. >

Tracking targets using adaptive Kalman filtering

Abstract: A simple algorithm for estimating the unknown process noise variance of an otherwise known linear plant, using a Kalman filter is suggested. The process noise variance estimator is essentially dead beat, using the difference between the expected prediction error variance, computed in the Kalman filter, and the measured prediction error variance. The estimate is used to adapt the Kalman filter. The use of the adaptive filter is demonstrated in a simulated example in which a wildly maneuvering target is tracked. >

Tracking analysis of the sign algorithm in nonstationary environments

Abstract: A tracking analysis of the adaptive filters equipped with the sign algorithm and operating in nonstationary environments is presented. Under the assumption that the nonstationary can be modeled using a random disturbance, it is shown that the long-term time average of the mean-absolute error is bounded, and that there exists an optimal choice of the convergence constant mu which minimizes this quality. Applying the commonly used independence assumption, and under the assumption that the nonstationarity is solely due to the time-varying behavior of the optimal coefficients, it is shown that the distributions of the successive coefficient misalignment vectors converge to a limiting distribution when the adaptive filter is used in the system identification mode. Finally, under the additional assumption that the signals involved are zero mean and Gaussian, a set of nonlinear difference equations is derived that characterizes the mean and mean-squared behavior of the filter coefficients and the mean-squared estimation error during adaptation and tracking. Results of several experiments that show very good correlation with the theoretical analyses are presented. >

The performance of time-dependent adaptive filters for interference rejection

Abstract: Time-dependent adaptive filters (TDAFs) that allow for the cyclostationary nature of communication signals by periodically changing the filter and adaptation parameters are examined. The TDAF has an advantage over the conventional time-independent adaptive filter in achieving better performance, i.e. reduced mean square error (MSE), for signals with periodic statistics. The basic theory of the TDAF is presented. The TDAF is shown to be more effective than the time-independent adaptive filter for interference rejection. This is verified by theoretical analysis and computer simulation of specific cases of extracting a signal in noise or interference. The criteria for judging the performance of the TDAF for interference rejection are MSE, bit error rate measurements, and constellation diagrams. >

Testing a decentralized filter for GPS/INS integration

Abstract: A decentralized Kalman filter strategy is presented and applied to GPS/INS (Global Positioning System/inertial navigation system) integration. Two Kalman filters are used. One is a local filter, processing GPS data and providing locally best estimates of position and velocity. The second is an INS filter which uses the results from the GPS filter as updates to the estimates obtained from the inertial data. Because of the high short-term accuracy of the inertial system, the position results from INS can be used for cycle slip detection and correction. The major advantages of this method are the flexible combination of GPS and INS and the simplicity of the implementation. Compared to centralized filtering, the decentralized filter gives globally the same optimal estimation accuracy as the centralized Kalman filter. The accuracy does not deteriorate when a suboptimal cascaded filter is used, which has some advantages in terms of computational efficiency. Extension of this method to more sensors is straightforward. Numerical results are used to illustrate the salient features of the method. >