Array filters for attenuating coherence interference in the presence of random noise (original) (raw)
Related papers
2003
Abstract A vector of digital filters is derived for the multichannel processing of the signals acquired by an array of sensors with the objective of extracting multiple desired signals by the attenuation of multiple interferences and random noise. The signals and interferences are assumed to have arbitrary waveforms with no a priori knowledge of these waveforms. The time duration of the recorded array data is assumed to be long enough to incorporate all time delayed propagated waveforms at the sensors of the array.
Absolutely optimum array filters for sensor arrays
Acoustics, Speech and Signal Processing, IEEE Transactions on, 1985
A family of array filters is derived for processing the recorded measurements of an array of sensors with the purpose of attenuating coherent noise interfering with a desired signal. One member of this family is the absolutely optimum array filter (AOAF), which minimizes the magnitude squared of the array rejection response subject to an all-pass condition for the desired signal. The AOAF is shown to have an array rejection response where both the width of the main lobes and the height of the sidelobes are reduced to zero.
2008
We are concerned with the detection and location of small seismic events, such as can be encountered in monitoring hydro-fracturing with surface sensors. Ambient seismic noise is the main problem in detection of weak seismic phases from these events, particularly as the sites of interest are often within or near producing fields. Bandpass filtering and stacking are the most widely used technique for enhancing the signal-to-noise ratio (SNR) in passive seismic experiments, but they are of limited value when both noise and signal share the same frequency band. Seismic arrays can be used to reduce the unwanted noise (e.g. traffic noise, pumping noise, scattering ground roll) by delay-and-sum techniques (also called beamforming) or by frequency-wavenumber filtering. Beamforming maximizes the array response for the assumed direction and slowness of the signal. Whereas in some situations it can be highly effective, and the azimuth and slowness of the signal can be determined by a grid search approach, it is vulnerable to contamination by side-lobe energy, particularly for broadband signals and noise .
Frequency-dependent phase coherence for noise suppression in seismic array data
Journal of Geophysical Research, 2007
We introduce a coherence measure for seismic signal enhancement through incoherent noise attenuation. Our processing tool is designed for densely spaced arrays and identifies signals by their coherent appearance. The approach is based on the determination of the lateral phase coherence as function of distance, time, frequency, and slowness. The coherence is derived from the local phases of neighboring stations which we obtain from analytic signals through the S-transform. The coherence is used to attenuate incoherent components in the time-frequency representations of the seismic records. No waveforms are averaged in our approach to maintain local amplitude information. This way we construct a data-adaptive filter which enhances coherent signals using the frequency-dependent and slowness-dependent phase coherence. We explain the method and show its abilities and limitations with theoretical test data. Furthermore, we have selected an ocean bottom seismometer (OBS) record section from NW-Spain and a teleseismic event registered at Spanish broadband stations to show the filter performance on real array data. Incoherent noise has been attenuated in all cases to enable a less ambiguous signal detection. In our last example, the filter also reveals weak conversions/reflections at the 410-km and 660-km discontinuities which are hardly visible in the unfiltered input data.
Suppression of Ambient Seismic Noise In 2D Arrays Using Multi-channel Wiener Filters
2007
Hydraulic fracture-induced microseismic events in producing oil and gas fields are usually small, and noise levels are high at the surface as a result of the heavy equipment in use. Similarly, in nonhydrocarbon settings, arrays for detecting local earthquakes will benefit from reduced noise levels and the ability to detect smaller events will be increased. We propose a frequency-dependent multichannel Wiener filtering technique with linear constraints that uses an adaptive least-squares method to remove coherent noise in seismic array data. The noise records on several reference channels are used to predict the noise on a primary channel and then can be subtracted from the observed data. On a test with an unconstrained version of this filter, maximal noise suppression leads to signal distortion. Two methods of im-posing constraints then achieve signal preservation. In one case study, synthetic signals are added to noise from a pilot deployment of a hexagonal array ͑nine three-component seismometers, approximately 150ϫ 150 m͒ above a gas field; noise levels are suppressed by up to 11 dB ͑at 2-10 Hz͒. In a second case study, natural seismicity recorded at a dense array ͑ϳ10 m spacing͒ in Italy is used, where the application of the filter improves the signal-to-noise ratio ͑S/N͒ more than 20 dB ͑at 2-15 Hz͒ using 35 stations. In both cases, the performance of the multichannel Wiener filters is significantly better than stacking, especially at lower frequencies where stacking does not help to suppress the coherent noise. The unconstrained version of the filter yields the best improvement in signal-to-noise ratio, but the constrained filter is useful when waveform distortion is unacceptable.
Digital filters for attenuating interference arriving from a wide range of angles
IEEE Transactions on Signal Processing, 1992
Digital filters for the off-line multichannel processing of the signals recorded by an array of sensors are derived with the objective of extracting a desired signal arriving from a known direction by attenuating coherent interference arriving from any direction within a wide range of angles and additive random sensor noise. Both the desired signal and coherent interference have arbitrary waveforms. Since the direction of arrival of the interference is unknown, the array response to it is treated as a random variable, and the expected value of its squared magnitude is taken as a minimization criterion in order to attenuate the coherent interference. The output power spectrum in response to the additive random sensor noise is taken as a second criterion. A convex combination of both criteria is minimized subject to an all-pass condition for the desired signal. Simulation results are presented showing the tradeoff between attenuating interference and random noise, the effect of the angular separation between the directions of arrival of the desired signal and interference and the effect of the number of sensors of the array on the output of the array filter.
Optimum Array Filters for Two-Dimensional Sensor Arrays
IEEE Transactions on Geoscience and Remote Sensing, 1987
This communication presents optimum and "absolutely optimum" filtering algorithms for two-dimensional sensor arrays. These d __/__/Y_/_ algorithms can extract the desired signal, while suppressing an unde-dx y sired coherent interference. They are derived using a frequency-domain constrained filtering technique, and are relevant to three types of arrays: rectangular, circular, and elliptical. Illustrative examples are also included.
Array signal Processing in the known waveform and steering vector case
IEEE Transactions on Signal Processing, 2004
The amplitude estimation of a signal that is known only up to an unknown scaling factor, with interference and noise present, is of interest in several applications, including using the emerging quadrupole resonance (QR) technology for explosive detection. In such applications, a sensor array is often deployed for interference suppression. This paper considers the complex amplitude estimation of a known waveform signal whose array response is also known a priori. Two approaches, viz., the Capon and the maximum likelihood (ML) methods, are considered for the signal amplitude estimation in the presence of temporally white but spatially colored interference and noise. We derive closed-form expressions for the expected values and mean-squared errors (MSEs) of the two estimators. A comparative study shows that the ML estimate is unbiased, whereas the Capon estimate is biased downwards for finite data sample lengths. We show that both methods are asymptotically statistically efficient when the number of data samples is large but not when the signal-to-noise ratio (SNR) is high. Furthermore, we consider a more general scenario where the interference and noise are both spatially and temporally correlated. We model the interference and noise vector as a multichannel autoregressive (AR) random process. An alternating least squares (ALS) method for parameter estimation is presented. We show that in most cases, the ALS method is superior to the modelmismatched ML (M 3 L) method, which ignores the temporal correlation of the interference and noise.
Instrumental variable approach to array processing in spatially correlated noise fields
1994
High-performance signal parameter estimation from sensor array data is a problem which has received much attention. A number of so-called eigenvector (EV) techniques such as MUSIC, ESPRIT, WSF, and MODE have been proposed in the literature. The EV techniques for array processing require knowledge of the spatial noise correlation matrix that constitutes a significant drawback. Herein, a novel instrumental 1,ariable (IV) approach to the sensor array problem i s proposed. The IV technique relies on the same basic geometric properties as the EV methods to obtain parameter estimates. However, by exploiting the temporal correlatedness of the source signals, no knowledge of the spatial noise covariance is required. The asymptotic properties of the IV estimator are examined and an optimal IV method is derived. Computer simulations are presented to study the properties of the IV estimators in samples of practical length. The proposed algorithm is also shown to perform better than MUSIC on a full-scale passive sonar experiment.