Search radar detection and track with the Hough transform. III. Detection performance with binary integration

Carlson, B.D.; Evans, E.D.; Wilson, S.L.

doi:10.1109/7.250412

Cited by 148 publications

(56 citation statements)

References 4 publications

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“…One way of solving the problem is to use "CLEAN" technique, but it requires more computation. In the paper, the binary Hough Transform [14] is proposed to alleviate the problem. The fundamental principle of binary Hough Transform based on WVD is: the WVD of multiple signals is computed first, then a primary threshold η is set in the time-frequency plane, the value of point whose amplitude is higher than η is defined as 1, the value of point whose amplitude is lower than −η is defined as −1, the value of point whose amplitude is between −η and η is defined as 0.…”

Section: Binary Hough Transformmentioning

confidence: 99%

Multiple Targets Detection Method Based on Binary Hough Transform and Adaptive Time-Frequency Filtering

Xue¹,

Sun²

2007

PIER

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Abstract-When the echo energy of multiple targets of vehicle volume detecting radar diverge greatly, small targets are easily masked by the platform of large targets, it is difficult to detect the all the targets by the Wigner-Hough Transform simultaneously. In this paper, a method based on binary Hough Transform and adaptive time-frequency filtering is proposed, which can avoid the problems of detecting the platform of large targets as well as platform masking of small targets and detect all the targets with different energy at the same time. The experimental results show the method has good performance and high practical value.

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Section: Binary Hough Transformmentioning

confidence: 99%

Multiple Targets Detection Method Based on Binary Hough Transform and Adaptive Time-Frequency Filtering

Xue¹,

Sun²

2007

PIER

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“…Elazar (1995) shows that the binomial distribution can be used to determine the expected HT false-alarm rate applied to such data sets. For an event to be detected, it must pass the S/N threshold and the HT threshold, which is based on known optimal parameters (Carlson et al, 1994;Elazar, 1995).…”

Section: Introductionmentioning

confidence: 99%

Extracting low signal-to-noise ratio events with the Hough transform from sparse array data

et al. 2018

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Low-frequency acoustic, i.e., infrasound, waves are measured by sparse arrays of microbarometers. Recorded data are processed by automatic detection algorithms based on array-processing techniques such as time-domain beam forming and f-k analysis. These algorithms use a signal-to-noise ratio (S/N) value as a detection criterion. In the case of high background noise or in the presence of multiple coinciding signals, the event's S/N decreases and can be missed by automatic processing. In seismology, detecting low-S/N events with geophone arrays is a well-known problem. Whether it is in global earthquake monitoring or reservoir microseismic activity characterization, detecting low-S/N events is needed to better understand the sources or the medium of propagation. We use an image-processing technique as a postprocessing step in the automatic detection of low S/N events. In particular, we consider the use of the Hough transform (HT) technique to detect straight lines in beam-forming results, i.e., a back azimuth (BA) time series. The presence of such lines, due to similar BA values, can be indicative of a low-S/N event. A statistical framework is developed for the HT parameterization, which includes defining a threshold value for detection as well as evaluating the false alarm rate. The method is tested on synthetic data and five years of recorded infrasound from glaciers. It is shown that the automatic detection capability is increased by detecting low-S/N events while keeping a low false-alarm rate.

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“…TBD procedures allow simultaneous detection and tracking and show superior detection performance over the conventional methods. Many TBD algorithms have been proposed, such as the Hough Transform (HT)-based TBD [12][13][14], the Dynamic Programming (DP)-based TBD [15][16][17][18] and the Particle Filter (PF)-based TBD [19,20].…”

Section: Introductionmentioning

confidence: 99%

TBD Algorithm Based on Improved Randomized Hough Transform for Dim Target Detection

Li¹,

Wei²

2012

PIER C

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Abstract-The track-before-detect (TBD) methodologies jointly process more consecutive scans and show superior detection performance for the low signal-to-noise ratio (SNR) targets over the conventional methods. A TBD algorithm based on improved Randomized Hough Transform for dim target detection is proposed in this paper. This algorithm uses the sequence numbers of scans to make sure that the point pairs are selected from different scans, avoiding the unreasonable situation that the point pairs may be selected from the same scan in the traditional Randomized Hough Transform (RHT). Second, it introduces a new voting method. Based on the minimum Euclidean distance criterion, this voting method finds the optimal parameter cell to vote, making the voting result better than the traditional RHT. In addition, we not only increase score of the optimal parameter cell but also update the corresponding parameter, thus suppressing the deviation between the recovered track and the target's track. Simulation results demonstrate the proposed algorithm can detect the dim target more rapidly and accurately than traditional RHT, especially under the background of low SNR.

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Search radar detection and track with the Hough transform. III. Detection performance with binary integration

Cited by 148 publications

References 4 publications

Multiple Targets Detection Method Based on Binary Hough Transform and Adaptive Time-Frequency Filtering

Multiple Targets Detection Method Based on Binary Hough Transform and Adaptive Time-Frequency Filtering

Extracting low signal-to-noise ratio events with the Hough transform from sparse array data

TBD Algorithm Based on Improved Randomized Hough Transform for Dim Target Detection

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