Adaptive Clutter Research Articles

Real-time adaptive clutter rejection filtering in color flow imaging using power method iterations

We propose a new algorithm for real-time, adaptive-clutter-rejection filtering in ultrasound color flow imaging (CFI) and related techniques. The algorithm is based on regression filtering using eigenvectors of the signal correlation matrix as a basis for representing clutter, a method that previously has been considered too computationally demanding for real-time processing in general CFI applications. The data acquisition and processing scheme introduced allows for a more localized sampling of the clutter statistics and, therefore, an improved clutter attenuation for lower filter orders. By using the iterative power method technique, the dominant eigenvalues and corresponding eigenvectors of the correlation matrix can be estimated efficiently, rendering real-time operation feasible on desktop computers. A new adaptive filter order algorithm is proposed that successfully estimates the proper dimension of the clutter basis, previously one of the major drawbacks of this clutter-rejection technique. The filter algorithm performance and computational demands has been compared to that of conventional clutter filters. Examples have been included which confirms that, by adapting the clutter-rejection filter to estimates of the clutter-signal statistics, improved attenuation of the clutter signal can be achieved in normal as well as more excessive cases of tissue movement and acceleration.

Adaptive Ground Clutter Removal Algorithm for Ground Penetrating Radar Applications in Harsh Environments

In this work we present a novel adaptive ground clutter removal (AGCR) algorithm for ground penetrating radar (GPR) applications. The reduction or even removal of the disturbing ground clutter, which mostly obscures the wanted reflections of buried targets, is very important especially for the detection of shallowly buried anti-personnel mines (APMs). Most of the ground clutter removal algorithms used nowadays, show weak performances in the case of a rough ground surface or suffer from high computation complexity. This algorithm is not designed to detect flush buried APMs. Further work is needed to ensure that the algorithm can detect all APM mines from flush buried to the maximum depth of cover of 13 cm as required by the UN SOPs. The algorithm presented in this contribution is capable to remove the ground clutter resulting from undulated surfaces by estimating not only the position but also the varying signal shape. This technique enables a near optimal ground clutter reduction with a moderate computation complexity. Furthermore, the proposed AGCR algorithm preserves the characteristic impulse responses of the buried objects, which affords a following target classification based on these significant reflections. The performance improvements of the proposed AGCR algorithm compared to standard algorithms are shown on measured data. The GPR data were collected with a laboratory test set-up which is also described briefly in this work.

An adaptive clutter rejection method based on AR model in color flow imaging

The ultrasound Doppler signal scattered from blood is heavily corrupted by the clutter signal reflected from slowly moving muscular tissue. The Doppler frequency shift of blood flow and clutter in different parts of human body greatly changes. Therefore, if a fixed wall filter is selected, the optimal filtering effect can not be attained. An adaptive clutter rejection method is proposed in this paper, which consists of a weak clutter rejector and a 2-order AR estimator. The clutter and blood power thresholds were preliminary defined in the weak clutter rejector. The echo power is compared with the two pre-defined thresholds, and the result was used to select an appropriate wall filter. The output of the weak clutter rejector is estimated by a 2-order AR estimator and two poles are acquired. The low frequency pole denotes the clutter signal and the high frequency pole denotes the blood signal. Before the AR estimation, a static signal is added to avoid producing split spectral peaks. It is illustrated in the simulation that the proposed method can detect the slower blood flow with smaller variance compared with the traditional wall filtering method.

Performance evaluation of eigendecomposition-based adaptive clutter filter for color flow imaging

In this paper, a newly developed eigendecomposition-based adaptive filter is applied in flow velocity estimation and its performance is evaluated and compared with the static polynomial regression filter. Results demonstrate that the eigendecomposition-based filter is the fully-adaptive and can not only suppress the clutter signals with minimal loss of low-flow signal, but also give less influence on the flow velocity estimation especially under the circumstance of high clutter velocity.

Multi-PRI Signal Processing for the Terminal Doppler Weather Radar. Part II: Range–Velocity Ambiguity Mitigation

Abstract Multiple pulse-repetition interval (multi-PRI) transmission is part of an adaptive signal transmission and processing algorithm being developed to combat range–velocity (RV) ambiguity for the Terminal Doppler Weather Radar (TDWR). In Part I of this two-part paper, an adaptive clutter filtering procedure that yields low biases in the moments estimates was presented. In this part, algorithms for simultaneously providing range-overlay protection and velocity dealiasing using multi-PRI signal transmission and processing are presented. The effectiveness of the multi-PRI RV ambiguity mitigation scheme is demonstrated using simulated and real weather radar data, with excellent results. Combined with the adaptive clutter filter, this technique will be used within the larger context of an adaptive signal transmission and processing scheme in which phase-code processing will be a complementary alternative.

An adaptive sidelobe cancellation algorithm for high‐gain antenna arrays

AbstractIn order to more accurately extract desired signals, it is necessary to suppress interference such as reflection and clutter more strictly. This paper discusses a method of suppressing sidelobes adaptively by placing a receive‐only array around a high‐gain array. The output power minimization method (DCMP) has been proposed as an adaptive clutter suppression method. When this method is applied to a high‐gain array, there is a danger of destroying the directivity of the high‐gain array. In this paper, a constraint on the weight not destroying the receiving pattern of the high‐gain array is added to the DCMP, resulting in a norm‐constrained DCMP (DCMP‐CN). By using this method, adaptive sidelobe suppression is possible regardless of the intensity of the input signals. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 1, 87(3): 11–18, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecja.10178

Adaptive clutter filtering for ultrasound color flow imaging

In this article, we present an adaptive clutter rejection method for selecting different clutter filters in ultrasound color flow imaging. A single clutter filter is typically used to reject the clutter. Because the clutter characteristics vary in both space and time, the single clutter filter approach has difficulty in providing optimum clutter rejection in ultrasound images. To achieve more accurate velocity estimation, we have developed a method to select a clutter filter adaptively at each location in an image from a set of predefined filters. Selection criteria have been developed based on the underlying clutter characteristics and the properties of various filters ( e.g., minimum-phase finite impulse response, projection-initialized infinite impulse response and polynomial regression). We have incorporated our adaptive clutter rejection method in an ultrasound system. We have found that our adaptive method can reduce the mean absolute error between the estimated and true flow velocities significantly compared with the conventional methods, in which a single clutter filter is used throughout the entire image. With in vivo abdominal data, we obtained an average gain of 5.0 dB in signal-to-clutter ratio (SCR), compared with the conventional method. These preliminary results indicate that the proposed adaptive method could improve the accuracy of flow velocity estimation in ultrasound color flow imaging through the improvement in SCR and the reduction in bias. (E-mail: ykim@u.washington.edu)

Manoeuvring target detection in over-the-horizon radar using adaptive clutter rejection and adaptive chirplet transform

In over-the-horizon radar (OTHR) systems, the signal-to-clutter ratio (SCR) used for moving target detection is very low. For slowly moving targets such as ships, the SCR is typically from −50 dB to −60 dB and their Doppler frequencies are close to that of the clutter. For manoeuvring targets, such as aircraft and missiles, the Doppler frequencies are time-varying when a long integration time is considered. When a target does not move uniformly, the Fourier transform based target detection techniques, including super-resolution spectrum techniques, may fail to work appropriately. In such situations, the Doppler signatures are time-varying and, therefore, time–frequency analysis techniques can be used for manoeuvring target detection. In addition, clutter rejection is also required for target detection due to the low SCR. The existing adaptive clutter rejection algorithms combine clutter rejection with spectrum analysis methods, which usually assume uniformly moving target (i.e. sinusoidal Doppler signature) models. An adaptive clutter reject algorithm is proposed together with the adaptive chirplet transform technique for manoeuvring target detection in a multipath environment. Simulation results using a simulated manoeuvring target signal with received raw OTHR clutter data show that targets with SCR below −50 dB can be detected by using the proposed algorithm.

Comparison of Doppler clutter cancellation techniques for naval multi-function radars

The authors describe a comparison of fixed and adaptive clutter cancellation processes applied to measured multi-function radar (MFR) data in a littoral environment. The adaptive filters require estimates of the clutter covariance, and comparisons of different strategies for obtaining this are made. The results for the adaptive filters generally show substantially improved target detectabilities over non-adaptive filters.

Adaptive clutter filtering via blind source separation for two-dimensional ultrasonic blood velocity measurement.

A method for adaptive clutter rejection via blind source separation (BSS) using principal and independent component analyses is presented in application to blood velocity measurement in the carotid artery. In particular, the filtering method's efficacy for eliminating clutter and preserving lateral blood flow signal components is presented. The performance of IIR filters is compromised by shorth data ensembles (10 to 20 temporal samples) as implemented for color-flow and high frame-rate imaging due to initialization requirements. Further, the ultrasonic imaging system's transfer function maps axial wall and lateral blood motion to overlapping spectra. As such, frequency domain-based approaches to wall filtering are ineffective for distinguishing wall from blood motion signals. Rather than operating in the frequency domain. BSS performs clutter rejection by decomposing the input data ensemble into N constitutive source signals in time, where N is the ensemble length. Source signal energy coupled with respective signal depth and time course profiles reveal which source signals correspond to blood, noise and clutter components. Clutter components may then be removed without disruption of lateral blood flow information needed for two-dimensional blood velocity measurement. A simplistic data simulation is employed to offer an intuitive understanding of BSS methods for signal separation. The adaptive BSS filter is further demonstrated using a Field II simulation of blood flow through the carotid artery including tissue motion. BSS clutter filter performance is compared to the performance of FIR, IIR and polynomial regression clutter filters. Finally, the filter is employed for clinical application using a Siemens Elegra scanner, carotid artery data with lateral blood flow collected from healthy volunteers, and Speckle Tracking; velocity magnitude and angle profiles are shown. Once again, the BSS clutter filter is contrasted to FIR, IIR and polynomial regression clutter filters using clinical examples. Velocities computed with Speckle Tracking after BSS wall filtering are highest in the center of the artery and diminish to low velocities near the vessel walls, with velocity magnitudes consistent with physiological expectations. These results demonstrate that the BSS adaptive filter sufficiently suppresses wall motion signal for clinical lateral blood velocity measurement using data ensembles suitable for color-flow and high frame-rate imaging.

Clutter filters adapted to tissue motion in ultrasound color flow imaging

The quality of ultrasound color flow images is highly dependent on sufficient attenuation of the clutter signals originating from stationary and slowly moving tissue. Without sufficient clutter rejection, the detection of low velocity blood flow will be poor, and the velocity estimates will have a large bias. In some situations, e.g., when imaging the coronary arteries or when the operator moves the probe in search for small vessels, there is considerable movement of tissue. It has been suggested that clutter rejection can be improved by mixing down the signal with an estimate of the mean frequency prior to high pass filtering. In this paper, we compare this algorithm with several other adaptive clutter filtering algorithms using both experimental data and simulations. We found that realistic accelerations of the tissue have a large effect on the clutter rejection. The best results were obtained by mixing down the signal with non-constant phase increments estimated from the signal. This adapted the filter to a possibly accelerated tissue motion and produced a significant improvement in clutter rejection.

Improved adaptive clutter cancellation through data-adaptive training

Adaptive array algorithms based on sample matrix inversion (SMI) require the availability of a secondary data set to train the adaptive filter. Numerous data-independent rules have been proposed for selecting this training data. However, such rules often perform poorly in inhomogeneous environments. We present data-adaptive methodologies for selecting the training data. The techniques, called Power Selected Training and Power Selected Deemphasis, use measurements of the interference environment to select training data. This work describes these algorithms and their performance on recorded radar data.

A Calibration Algorithm for Circular Polarimetric Radars

Since circular polarimetric radars can be used to assess the relative contribution of odd-bounce and even-bounce scattering to the response of targets and to implement novel schemes for adaptive rain clutter suppression and radar pulse compression, they are increasingly used in applications such as radar meteorology, biophysical and geophysical remote sensing, and radar navigation. Although methods for calibrating linear polarimetric radars are widely known, they cannot be used to calibrate circular polarimetric radars until account is taken for the different form of the response of calibration targets when measured with respect to a circularly polarized basis. The response of three standard targets used to calibrate linear polarimetric radars (an odd-bounce-reflector and two even-bounce reflectors with different angles of rotation about the radar line-of-sight) does not yield a unique solution to the calibration equations. If, however, the odd-bounce reflector is replaced by either a circularly polarizing or linear polarization selective target, the calibration equations can be solved.

On clutter dof for phased array airborne early warning radars

In recent years, considerable attention has been paid to adaptive clutter suppression for airborne early warning (AEW) radars. A necessary condition for efficient clutter suppression is that the number of adaptive degrees of freedom (DOF) is no less than that of clutter DOF. This paper analyzes the DOF of clutter for phased array AEW radars. The new results obtained here are helpful to design and adaptive system for clutter suppression.

Suboptimum approach for adaptive clutter suppression in airborne phased array radar

A new approach for clutter suppression in airborne phased array radar is presented. It is implemented by adaptive processing for the data in the detected Doppler channel, with the clutter remaining outside this channel. Simulations show that the new approach approximates the theoretical optimum. However, the computational expense is greatly reduced compared with the optimum processor.

Radar clutter suppression schemes

In the letter the spatial correlation between radar clutter returns from adjacent range rings is exploited to construct two new adaptive clutter suppression schemes. The schemes are tested by using actual radar data, including weather clutter as well as ground clutter. Experimental results show that these schemes are superior to the adaptive lattice prediction error filter.

Adaptive clutter suppression for airborne phased array radars

The problem of clutter suppression is more serious for airborne than for ground-based radars because the ground returns become Doppler coloured owing to platform motion. Therefore the clutter power depends on both the azimuth and the Doppler frequency. Such two-dimensional spectra can only be suppressed by two-dimensional filters, which require sampling of the echo field in both space and time. This is verified by a phased array radar using coherent pulse trains. The platform motion causes the clutter background to change with time, and any change of the platform velocity results in a change of the Doppler characteristics of the clutter returns. Therefore two-dimensional filter has to be adpative. The paper presents some new results on optimum adaptive clutter supression. For comparison, a suboptimum approach (beamformer plus adaptive temporal filter) is presented. It turns out that the favourable properties of the optimum space-time clutter filter can hardly be achieved by any suboptimum substitute.

Adaptive Clutter Suppression in Step Scan Radars

Clutter echoes with unknown power spectra (from weather, sea, chaff disturbances) can be suppressed only adaptively. The use of the discrete Fourier transform (DFT) for clutter suppression in step scan radars is investigated by use of a clutter model that is derived in analogy to measured clutter data of a radar with a rotating antenna.