Microwave imaging of aircraft

Steinberg, Bernhard

doi:10.1109/5.16351

Cited by 162 publications

(55 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The first three matrix blocks contain 128 range profiles while the forth one contains the rest 16 range profiles. The ARPs of the matrix blocks are calculated according to (11). The original and magnified ARPs are displayed in The Shannon entropy is used to denote the focal quality of the ISAR image [14].…”

Section: Range Alignment Among Matrix Blocksmentioning

confidence: 99%

“…Based on the adopted criterion, the already existed range alignment algorithms in open articles can be divided into four kinds, namely, cross-correlation [1,6,7], entropy minimization [8,9], contrast maximization [10] and frequency-domain method [1]. In terms of phase adjustment, the prominent scatterer algorithm [11], doppler centroid tracking [12], phase gradient autofocus [13], entropy minimization [14][15][16][17][18][19] and contrast maximization [18,20] are regarded as the outstanding representatives.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Robust Sub-Integer Range Alignment Algorithm Against MTRC for Isar Imaging

Hu¹,

Chen²

2017

PIER B

View full text Add to dashboard Cite

Abstract-Range alignment plays an important role in the inverse synthetic aperture radar (ISAR) imaging. The performance of the traditional range alignment algorithms decreases when the migration through resolution cells (MTRC) is much severe. In this paper, a measure of MTRC is defined, and the effect of MTRC on range alignment is analyzed. Taking MTRC into account, a robust sub-integer range alignment algorithm is proposed. Firstly, each range profile is interpolated to remove the precision limitation of integer range resolution cell. Subsequently, the matrix formed by all the range profiles is partitioned into several matrix blocks on the slow-time domain. For each matrix block, the range profiles are aligned by minimizing the entropy of the average range profile (ARP). Finally, the matrix blocks are coarsely aligned using the maximum correlation method, followed by a fine alignment based on the minimization of the ISAR image entropy. The effectiveness of the proposed algorithm is validated by simulations and real-world data. Results demonstrate that the proposed method is robust against MTRC and can reduce the alignment error. The resultant ISAR image is much better focused.

show abstract

Section: Range Alignment Among Matrix Blocksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Robust Sub-Integer Range Alignment Algorithm Against MTRC for Isar Imaging

Hu¹,

Chen²

2017

PIER B

View full text Add to dashboard Cite

show abstract

“…This is contrasted to the equivalent ISAR image generated through standard range-Doppler processing of the HRR profiles in Figure 8. Here the classical motion compensation by Haywood [42] and the dominant scatterer algorithm (DSA) [43] auto focusing methods were used as a baseline for comparison. The root mean-square-error (RMSE) determined from the converted state estimates over 50 simulation runs is given in Table III.…”

Section: Number Of Pulses Per Hrr Profile (N) 64mentioning

confidence: 99%

Joint inference of dominant scatterer locations and motion parameters of an extended target in high range‐resolution radar

Freitas

Villiers

Nel

2015

IET Radar, Sonar & Navigation

View full text Add to dashboard Cite

A target of interest measured by a high range resolution radar may be modelled by multiple dominant points of reflections referred to as dominant scatterers. In this paper a non-linear state space setting is used to model the states and measurements of a target moving in the down-and cross-range dimensions. A resample-move particle filter with simulated annealing is successfully used to jointly infer the locations of the dominant scatterers and the motion parameters of the target. A novel technique for the initialization of the particle filter for the given application is presented. The location estimates of scatterers using the particle filter method are compared to those obtained using standard range-Doppler inverse synthetic aperture radar (ISAR) imaging when using the same radar returns for both cases. The particle filter infers the location of scatterers more accurately than range-Doppler ISAR processing, and the processing can be performed online as opposed to ISAR processing, which requires batching. It is relatively straightforward to extend the method to perform localisation and tracking of scatterers in three dimensions, whereas such an extension is challenging in range-Doppler ISAR processing. However, several challenges need be addressed to make this algorithm suitable for practical implementation and these challenges are discussed. This method may be used to obtain very accurate estimates of target state, which may in turn be used for accurate ISAR motion compensation. Given enough computing resources this algorithm may in future become the basis of a new radar target imaging scheme.

show abstract

“…A simple approach to ISAR autofocus is to find a range cell containing a strong scatterer, which is the dominant scatterer algorithm (DSA) [3]. For a complex target that does not have a stable prominent scatterer, a phase gradient is estimated by averaging the phase differences over each range cell, and then an iterative phase correction is conducted, which is called phase gradient autofocus (PGA) [4].…”

mentioning

confidence: 99%

Autofocus for ISAR Imaging Using Higher Order Statistics

She

Liu

2008

IEEE Geosci. Remote Sensing Lett.

View full text Add to dashboard Cite

Autofocus is imperative for inverse synthetic aperture radar (ISAR) imaging. In this letter, a new approach for ISAR autofocus is developed by using fourth-order statistics properties of the radar's return signal. After the ISAR signal model is established, the approach is described. The results of processing real data confirm the effectiveness of the proposed approach and show its capability for suppressing noise. The developed approach has a numerical stability and a smaller computational load compared with the maximum image contrast and the minimum image entropy methods. Abstract-Autofocus is imperative for inverse synthetic aperture radar (ISAR) imaging. In this letter, a new approach for ISAR autofocus is developed by using fourth-order statistics properties of the radar's return signal. After the ISAR signal model is established, the approach is described. The results of processing real data confirm the effectiveness of the proposed approach and show its capability for suppressing noise. The developed approach has a numerical stability and a smaller computational load compared with the maximum image contrast and the minimum image entropy methods.

show abstract

Microwave imaging of aircraft

Cited by 162 publications

References 25 publications

A Robust Sub-Integer Range Alignment Algorithm Against MTRC for Isar Imaging

A Robust Sub-Integer Range Alignment Algorithm Against MTRC for Isar Imaging

Joint inference of dominant scatterer locations and motion parameters of an extended target in high range‐resolution radar

Autofocus for ISAR Imaging Using Higher Order Statistics

Contact Info

Product

Resources

About