Time-frequency transform used in radar Doppler tomography

Swiercz, Ewa

doi:10.1109/irs.2014.6869256

Cited by 5 publications

(5 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The frequency information related to time cannot be obtained by Fourier method, so it is not the mainstream method of micro-Doppler features extraction. For timefrequency analysis, the main methods include short-time Fourier transform [38], generalized S-transform [39], Gabor transform [40]- [42] and so on. Gabor transform is a short-time Fourier transform with Gaussian window.…”

Section: Radar Decision Processingmentioning

confidence: 99%

UAV Target Identification Based on Information fusion of Computer Vision and Radar Signals

Wei

Hong

Fang

2022

Preprint

View full text Add to dashboard Cite

With the increasing number of unmanned aerial vehicles (UAVs), the increasing pressure of air traffic management and airspace security, it is a challenge to achieve effective UAV target detection. Computer vision and radar signal detection are two commonly used technologies in this field. Computer vision is limited by meteorological conditions and the radar signal is affected by ground clutter at low altitudes. In response to these problems, this paper describes the information fusion method from two different levels of data-level fusion and decision-level fusion. In this method, Computer vision and radar signals complement each other to improve the detection accuracy. For each level, the method of information fusion is introduced in detail. Besides, the effectiveness of the method is proved by comprehensive experiment. The results show that the accuracy of the fusion method is improved, with a maximum of 9.0%.

show abstract

Section: Radar Decision Processingmentioning

confidence: 99%

UAV Target Identification Based on Information fusion of Computer Vision and Radar Signals

Wei

Hong

Fang

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…e frequency information related to the time cannot be obtained by the Fourier method, so it is not the mainstream method of micro-Doppler feature extraction. For time-frequency analysis, the main methods include shorttime Fourier transform [39], generalized S-transform [40], and Gabor transform [41][42][43]. Gabor transform is a shorttime Fourier transform with a Gaussian window.…”

Section: Radar Decision Processing Chenmentioning

confidence: 99%

Research on Information Fusion of Computer Vision and Radar Signals in UAV Target Identification

Wei

Hong

Fang

2022

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

As one of the crucial sensing methods, multisensor fusion recognition aids the Internet of Things (IoT) in connecting things through ubiquitous perceptual terminals. The small size, sluggish flying speed, low flight altitude, and low electromagnetic intensity of unmanned aerial vehicles (UAVs) have put enormous strain on air traffic management and airspace security. It is urgent to achieve effective UAV target detection. The radio monitoring method, acoustic detection scheme, computer vision, and radar signal detection are commonly used technologies in this field. The radio monitoring approach has low accuracy, the acoustic detection strategy has a limited detection range, computer vision is limited by weather conditions, and the radar signals at low altitudes are influenced by ground clutter. To address these issues, this paper proposes an information fusion strategy based on two levels of fusion: data-level fusion and decision-level fusion. In this strategy, Computer vision and radar signals complement each other to improve the detection accuracy. For each level, the method of information fusion is introduced in detail. Furthermore, the effectiveness of the method has been demonstrated by a series of comprehensive experiments. The results show that the accuracy of the fusion method is improved, and the proposed method can still work even when the single method loses function.

show abstract

“…1 to describe line integrals and projections. A one‐dimensional (1D) projection P θ ( r ) at angle θ is defined as the Radon transform of a two‐dimensional (2D) target function f ( x , y ) [20, 21]

P_{θ} false(r false) = \int_{- normal∞}^{\infty} \int_{- \infty}^{normal∞} f false(x, y false) δ false(r - x \cos θ - y \sin θ false) normald x normald y,

where r is the distance from origin to the integral line.…”

Section: Foundations Of M–d Imaging and Simulations Studymentioning

confidence: 99%

“…The core idea of tomographic reconstruction is the Fourier slice theorem [20, 21]. The theorem states that the 1D Fourier transform of a parallel projection at a specific angle θ is equal to a slice of the 2D Fourier transform of the original target at the same angle, that is

S_{θ} ()(, k_{r}) = \int_{- normal∞}^{+ normal∞} P_{θ} (r) \exp ()(, - j k_{r} r) normald r = F_{θ} ()(, k_{x}, k_{y}) .

Where k x = k r cos θ , k y = k r sin θ are the spatial frequency components; F θ ( k x , k y ) is a slice of the 2D Fourier transform of the function f ( x , y ) along the direction of θ .…”

Section: Foundations Of M–d Imaging and Simulations Studymentioning

confidence: 99%

“…The above results indicated that if an infinite number of projections are taken, then F ( k x , k y ) would be known at all points in 2D Fourier plane. Given F ( k x , k y ), the target function f ( x , y ) can be recovered by using 2D inverse Fourier transform [20, 21]

\begin{matrix} right leftthickmathspace.5em \\ f (x, y) = \int_{- \infty}^{normal∞} \int_{- normal∞}^{\infty} F (k_{x}, k_{y}) \exp \{j 2 π (k_{x} x + k_{y} y)\} d k_{x} d k_{y} . \end{matrix}

Exchanging the rectangular coordinate system to a polar coordinate system and using (2), the target function can be computed as

\begin{matrix} right leftthickmathspace.5em \\ f (x, y) = \int_{0}^{π} \int_{- \infty}^{normal∞} S_{θ} (k_{r}) |k_{r}| \cdot \exp \{j 2 π k_{r} (x \cos θ + y \sin θ)\} \\ d k_{r} d θ . \end{matrix}

The product | k r | S θ ( k r ) is called a filtered projection [20, 21]. The resulting projections for different angles θ are then added to form the estimate of f ( x , y ).…”

Section: Foundations Of M–d Imaging and Simulations Studymentioning

confidence: 99%

See 1 more Smart Citation

Micro‐Doppler imaging for fast rotating targets using illuminators of opportunity

Peng

Wan

et al. 2016

IET Radar, Sonar & Navigation

View full text Add to dashboard Cite

Time-frequency transform used in radar Doppler tomography

Cited by 5 publications

References 3 publications

UAV Target Identification Based on Information fusion of Computer Vision and Radar Signals

UAV Target Identification Based on Information fusion of Computer Vision and Radar Signals

Research on Information Fusion of Computer Vision and Radar Signals in UAV Target Identification

Micro‐Doppler imaging for fast rotating targets using illuminators of opportunity

Contact Info

Product

Resources

About