An initialization technique for improved MTI performance in phased array radars

Fletcher, Robert H.; Burlage, D. W.

doi:10.1109/proc.1972.8953

Cited by 36 publications

(14 citation statements)

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“…The frequency response of the filter can be calculated by (6) where B k (e jω ) is the discrete-time Fourier transform of the basis vector b k [22]. In order to design HP filters, K must be small.…”

Section: Polynomial Regression Filtersmentioning

confidence: 99%

“…Unfortunately, the number of samples per line-of-sight (LOS), or packet size N, must be kept small (2 ≤ N ≤ 20) to achieve real-time CF frame rates. The IIR filter initialization techniques are known to reduce transient filter responses [6], [7]. Another approach is to apply regression filters that treat input samples as polynomial functions in the time domain and perform least-square regression analysis [8].…”

Section: Introductionmentioning

confidence: 99%

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Adaptive clutter rejection filtering in ultrasonic strain-flow imaging

Kargel

Hobenreich

Trummer

et al. 2003

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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This paper introduces strain-flow imaging as a potential new technique for investigating vascular dynamics and tumor biology. The deformation of tissues surrounding pulsatile vessels and the velocity of fluid in the vessel are estimated from the same data set. The success of the approach depends on the performance of a digital filter that must separate echo signal components caused by flow from tissue motion components that vary spatially and temporally. Eigenfilters, which are an important tool for naturally separating signal components adaptively throughout the image, perform very well for this task. The method is examined using two tissue-mimicking flow phantoms that provide stationary and moving clutter associated with pulsatile flow.

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Section: Polynomial Regression Filtersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive clutter rejection filtering in ultrasonic strain-flow imaging

Kargel

Hobenreich

Trummer

et al. 2003

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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“…Step-initialization [11] was used to provide the unavailable input and output values for which are required by (1) and (2). Briefly, step-initialization assumes that all input values for are equal to the first available input values respectively.…”

Section: Experimental and Processing Detailsmentioning

confidence: 99%

Experimental evaluation of velocity and power estimation for ultrasound blood flow imaging, by means of a two-dimensional autocorrelation approach

Loupas

Peterson²,

Gill

1995

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

170

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This paper evaluates experimentally the performance of a novel axial velocity estimator, originally introduced in [4] and referred to as the 2D autocorrelator, and its Doppler power estimation counterpart, the 2D zero-lag autocorrelator, in the context of ultrasound color flow mapping. The evaluation also encompasses the well-established 1D autocorrelation technique for velocity estimation and its corresponding power estimator (1D zero-lag autocorrelator), to allow performance comparisons under identical conditions. Clutter-suppressed in vitro data sets from a steady-flow system are used to document the effect of the range gate and ensemble length, noise level and angle of insonation on the precision of the velocity estimates. The same data sets are used to examine issues related to the estimation of the Doppler signal's power. The first-order statistics of power estimates from regions corresponding to flow and noise are determined experimentally and the ability of power-based thresholding to separate flow signals from noise is characterized by means of ROC analysis. In summary, the results of the in vitro evaluation show that the proposed 2D-autocorrelation form of processing is consistently better than the corresponding 1D-autocorrelation techniques, in terms of both velocity and power estimation. Therefore, given their relatively modest implementation requirements, the 2D-autocorrelation algorithms for velocity and power estimation appear to represent a superior, yet realistic, alternative to conventional Doppler processing for color flow mapping.

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“…This requires a special processor, called the initialization processor, to compute the required steady state values of the filter for a given input signal. As the input signal may be approximated by a step function, this is called step initialization [2]. The initialization processor will improve the transient step response (zero frequency response) of the filter irrespective of the number of processed samples provided that the filter has one steady state zero at zero frequency.…”

Section: Introductionmentioning

confidence: 99%

Improved Transient Frequency Response of IIR Filters with Multiple Frequency Initialization

Jeedella¹,

Al-Ahmad²,

Al-Mualla³

et al. 2006

2006 13th IEEE International Conference on Electronics, Circuits and Systems

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This paper presents a new design method for IIR notch filters with improved performance while operating with a limited number of samples. The design criterion is to minimize the error between the steady state and transient frequency responses while utilizing the initialization technique. The new filters which use multiple frequency initialization offer better transient frequency responses than conventional steady state filters with initialization.

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An initialization technique for improved MTI performance in phased array radars

Cited by 36 publications

References 0 publications

Adaptive clutter rejection filtering in ultrasonic strain-flow imaging

Adaptive clutter rejection filtering in ultrasonic strain-flow imaging

Experimental evaluation of velocity and power estimation for ultrasound blood flow imaging, by means of a two-dimensional autocorrelation approach

Improved Transient Frequency Response of IIR Filters with Multiple Frequency Initialization

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