Efficient Velocity Filter Implementations for Dim Target Detection

Kennedy, Hugh L.

doi:10.1109/taes.2011.6034680

Cited by 36 publications

(8 citation statements)

References 16 publications

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“…При параллельном подходе анализ спектрального состава структурной текстуры выполняют одновременно в нескольких частотных полосах. В работе [6] с использованием такого подхода проводилась обработка объектов, рассеянных по изображению. В результате были снижены вычислительные затраты и повышена оперативность обработки изображений [7].…”

Section: анализ литературных источников и постановка проблемыunclassified

Elaboration of the transform with generalized comb scaling and wavelet functions for the image segmentation

Полякова

Krylov

Ищенко

2014

EEJET

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Section: анализ литературных источников и постановка проблемыunclassified

Elaboration of the transform with generalized comb scaling and wavelet functions for the image segmentation

Полякова

Krylov

Ищенко

2014

EEJET

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“…Since the exact target velocity is unknown, a velocity filter bank is used to cover the possible target velocities, and the space should be close enough to achieve a specified maximum SNR loss due to mismatch. is approach could result in a large number of filters required; hence, it will increase the computational complexity [20]. Besides, it cannot separate some targets with very close velocities; such scenario appears in the following experimental data.…”

Section: Introductionmentioning

confidence: 99%

“…is work mainly focuses on the tracking of the time-Doppler and time-azimuth traces for targets in sight; however, the propagation environment, including the sea, land, and ionospheric propagation [10][11][12], is extremely complicated, which leads to a challenging problem due to unknown and varying number of multitarget, model mismatch, lower signal-to-noise ratio (SNR), hybrid clutter, severe breaking points, intersecting traces, etc. Typical TBD strategies include Kalman filter (KF) [13,14], Hough transform (HT) [15][16][17], velocity filtering (VF) [18][19][20][21], particle filtering (PF) [22][23][24][25], dynamic programming (DP) [26][27][28][29][30], and Greedy algorithm [31]. eir basic concept, possible advantages, and limitation for application related to this work are summarized as follows:…”

Section: Introductionmentioning

confidence: 99%

Track-Before-Detect Procedures in AM Radio-Based Passive Radar

et al. 2021

International Journal of Antennas and Propagation

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In amplitude modulation radio-based passive radar, the track-before-detect (TBD) procedures are performed to process long time observation data. This work mainly focuses on the tracking of the time-Doppler and time-azimuth traces of multiple target under real scenarios such as low signal-to-noise ratio, hybrid clutter, severe breaking points, and intersecting traces. A new original approach to deal with the TBD problem of this work is developed. Two types of linear equations according to the simplified radio wave propagation model are formulated, which is the key point of the proposed method. We make the theoretical analysis about how the linear equations can be used to track multiple target, and how multiple target’s constant velocities and initial positions can be estimated, which is an additional parameter estimation capability of the proposed method. Both the simulated data and real experimental data are performed with the proposed method and some conventional TBD methods. Several comparisons of the results are given to verify the effectiveness of the proposed method.

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“…The tracking and isolation of moving objects with a specific range of speed are a challenging research topic in the field of automotive application [28][29][30][31]. To cope with this issue, velocity filters have been used in the past for localizing and monitoring moving objects in image sequences or otherwise 3D imagery [32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Velocity Filtering Using Quantum 3D FFT

Κούκιου

Anastassopoulos

2023

Photonics

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In this work, the quantum version of 3D FFT is proposed for constructing velocity filters. Velocity filters are desirable when we need to separate moving objects with a specific velocity range in amplitude and direction in a rapidly changing background. These filters are useful in many application fields, such as for monitoring regions for security reasons or inspecting processes in experimental physics. A faster and more attractive way to implement this filtering procedure is through 3D FFT instead of using 3D FIR filters. Additionally, 3D FFT provides the capability to create banks of ready-made filters with various characteristics. Thus, 3D filtering is carried out in the frequency domain by rejecting appropriate frequency bands according to the spectral content of the trajectory of the object to be isolated. The 3D FFT procedure and the corresponding inverse one are required in the beginning and end of the filtering process. Although 3D FFT is computationally effective, it becomes time-consuming when we need to process large data cubes. The implementation of velocity filters by means of the quantum version of 3D FFT is investigated in this work. All necessary quantum circuits and quantum procedures needed are presented in detail. This proposed quantum structure results in velocity filtering with a short execution time. For this purpose, a review of the necessary quantum computational units is presented for the implementation of quantum 3D FFT and representative examples of applications of velocity filtering are provided.

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Efficient Velocity Filter Implementations for Dim Target Detection

Cited by 36 publications

References 16 publications

Elaboration of the transform with generalized comb scaling and wavelet functions for the image segmentation

Elaboration of the transform with generalized comb scaling and wavelet functions for the image segmentation

Track-Before-Detect Procedures in AM Radio-Based Passive Radar

Velocity Filtering Using Quantum 3D FFT

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