Magnitude-based pulse width estimation via efficient edge detection

Abstract

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.

An Image Processing Algorithm for Facile and Reproducible Quantification of Vomocytosis

“…For simplicity, we employed a moving rectangular pulse with a width of 7 units (Figure C and D). In effect, this convolution produces a 7-frame moving average, which removes the frame-to-frame fluctuations that are primarily caused by changes in microscope focus or alterations in Z-stack selections . To determine whether convolution affected the quantitative metrics used to screen for vomocytosis events, the fluorescence range and minimum derivative value were calculated for both the raw and convolved data.…”

Accurate Estimation of LPI Radar Pulse Train Parameters via Change Point Detection

“…However, the performance deteriorates when an overlap occurs between the Costas sequences in the time axis. A PW estimation method based on edge detection, which utilizes a Haar filter and a threshold, was presented in [14]. However, a weak-signal environment was not considered.…”

“…The main contribution of this study is that it provides an accurate estimation method for the TOA, PW, and PRI in a weak-signal environment. In addition, most studies have not considered various modulation schemes [9], [10], [11], [12], [13], [14], [15], [16], [17], whereas the proposed method shows robust estimation performance for the eight modulation schemes, such as LFM, Costas, Barker, Frank, P1, P2, P3, and P4 codes. Furthermore, most threshold-based estimation methods use a fixed value or a variable value (e.g., constant false alarm rate) as a threshold [9], [10], [11], [14], [15].…”

“…In addition, most studies have not considered various modulation schemes [9], [10], [11], [12], [13], [14], [15], [16], [17], whereas the proposed method shows robust estimation performance for the eight modulation schemes, such as LFM, Costas, Barker, Frank, P1, P2, P3, and P4 codes. Furthermore, most threshold-based estimation methods use a fixed value or a variable value (e.g., constant false alarm rate) as a threshold [9], [10], [11], [14], [15]. However, fixed-threshold-based methods are vulnerable in coping with a weak-signal environment because the noise is dominant as SNR decreases.…”

“…To minimize these potential troubles, we take a different approach of separating the pulse train into pulse and noise in advance and then using a fixed threshold. Lastly, most studies consider single pulses [12], [13], [14], [15], [16]. Although some studies concentrate on parameter estimation for the pulse train, they are fragile to weak signals [9], [10] or consider only single modulation [11].…”