A new method of pulse edge detection in low SNR

Fan, Xiaolei; Li, Tao; Su, Shaoying; Chen, Zengping

doi:10.1117/12.2205763

Cited by 3 publications

(4 citation statements)

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“…Similarly, the difference between the Haar and beta tests is between 1.5% and 3.7%. As noted previously, 1,2,7,8 the PW estimator improves as the size of the moving averages (used to calculate the ratio and Haar filter outputs) increases, and the best performance is achieved when the Haar filter length is equal to twice the PW.…”

Section: Experimental Evaluation Of Haar and Ratio-based Methodsmentioning

confidence: 58%

“…The first steps in this process include a pulse width (PW) estimator, and many effective component systems have been proposed. [1][2][3][4][5][6][7][8] Unfortunately, these approaches often maximize performance at the expense of operational efficiency, where we refer to an implementation as "efficient" if (1) it attempts to minimize the number of required arithmetic operations, and (2) it prioritizes addition over multiplication and multiplication over division. This designation arises due to the relative ease of implementing addition operations as part of a hardware design.…”

Section: Introductionmentioning

confidence: 99%

“…One approach of particular interest is based on a variant of Haar wavelet filtering and can be efficiently implemented using moving average windows. 1,2 To improve performance, however, the algorithm first processes the signal in the frequency domain before calculating the Haar filter output in the time domain. Hence, this approach accepts additional latency in the output data sequence to achieve a higher fidelity signal.…”

Section: Introductionmentioning

confidence: 99%

“…While many authors present effective implementations of the proposed algorithms, some do not take full advantage of the special form of convolution with a rectangular window (i.e., the moving sum). 1,2 The normalization at each step (to determine the average in some implementations) also introduces an expensive division, and the calculation of the square root introduces additional computational overhead. Finally, the mean and standard deviations of both the Rayleigh and exponential distributions include a single, common parameter; hence, a threshold defined by T ¼ μ þ k 1 σ (for mean μ, standard deviation σ, and scale factor k 1 ) can also be expressed as T ¼ k 2 μ (for a different scale factor k 2 ).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Magnitude-based pulse width estimation via efficient edge detection

Ranney

Tom

Tadas

et al. 2022

J. Appl. Rem. Sens.

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In recent years, researchers have addressed the problem of using noncoherent approaches to estimate pulse width and pulse repetition interval. Since the measured transmitter is noncooperative, and noncoherent integration gain can be realized, the input signal-to-noise ratio (SNR) for these estimators becomes critical. We examine multiple edge detectors that exploit moving sums calculated as part of a Haar filtering of the received signal magnitudes. Two different ratio tests are considered in addition to the Haar filtering (or "difference of boxes") approach, and a binary hypothesis test is designed based on a "smallest of" constant false alarm rate formulation. Probability arguments are then invoked to derive readily evaluated expressions for the detection thresholds. Tests are conducted, indicating that performance of the ratio-based approaches is comparable in terms of processed peak-to-background ratio. However, comparisons of root mean-squared (RMS) error indicate that the difference-based (Haar) approach produces lower error than both ratio-based approaches. The Haar filter approach is further demonstrated to remain effective (100% detection, 0% false alarm, RMS estimation errors of <3%) at low SNRs of ∼0 dB.

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Section: Experimental Evaluation Of Haar and Ratio-based Methodsmentioning

confidence: 58%