Noise radar range doppler imaging via 2D generalized smoothed‐
            <i>l</i>
            0

Lu, Xingyu; Gu, Hong; Su, Weimin

doi:10.1049/ell2.12158

Cited by 4 publications

(4 citation statements)

References 10 publications

(16 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Considering that a small

α

would lead to a lot of local minimum, we solve the problem () by iterations with a decreasing sequence of

α

to avoid the local minima and to achieve a sparse solution. We refer the readers to [9] for more details of the algorithm (i.e. 2D generalised smoothed‐

L_{0}

) to solve the problem ().…”

Section: Proposed Sparse Recovery–based Methodsmentioning

confidence: 99%

“…For a traditional pulse‐Doppler radar system that transmits repetitive waveforms, the maximum unambiguous range

R_{\max} = c T_{s} / 2

, where

normalc

denotes the velocity of light. If there are

K

targets and all these targets are with unambiguous ranges, the digital received echo of a noise radar system can be written as [9]

y false(n, m false) = \sum_{k = 1}^{K} α_{k} s_{m} (n T_{f} - τ_{k}) e^{false(j ω_{k} m T_{s} false)},

where

T_{s}

denotes the slow‐time sampling interval (i.e. PRI).…”

Section: Signal Modelmentioning

confidence: 99%

“…The imposition of sparse recovery theory to radar target detection has been proved to be beneficial for sidelobe reduction [7,8]. In our previous work, we proposed a sparse recovery method to suppress the range sidelobes and alleviate the RSM effect in a noise radar system [9]. However, the range-ambiguity of the targets and clutters was not considered in the sparse recovery model in [9].…”

mentioning

confidence: 99%

“…In our previous work, we proposed a sparse recovery method to suppress the range sidelobes and alleviate the RSM effect in a noise radar system [9]. However, the range-ambiguity of the targets and clutters was not considered in the sparse recovery model in [9]. As far as we know, the imposition of sparse recovery to the range ambiguity suppression for radar has not been sufficiently studied thus far.…”

mentioning

confidence: 99%

See 3 more Smart Citations

Range‐ambiguity suppression for noise radar in cluttered environment via sparse recovery

Xia

et al. 2021

Electronics Letters

Self Cite

View full text Add to dashboard Cite

Range ambiguity suppression is a significant problem in traditional pulse-Doppler radar systems, especially when the pulse repetition frequency is set high to avoid Doppler ambiguity. One possible way to deal with such problem is to apply the noise radar system, by transmitting pulse-agile waveforms and using a group of matched filters as the receiver to accumulate the power of unambiguous echoes while dispersing the range-ambiguous ones. However, when strong targets or heavy clutters exist, the dispersed ambiguous power is still high, which leads to high range-Doppler sidelobes and overwhelms the weak targets. This paper explores a new way to deal with the range-ambiguity suppression problem in noise radar. The range-ambiguity suppression as a sparse recovery problem is formulated, and then both the unambiguous and ambiguous targets (and/or clutters) can be simultaneously reconstructed. The proposed method can enhance the range-ambiguity suppression performance and synthetic an ambiguity-free range-Doppler map with a low sidelobe level, which ensures the weak target recovery performance even in a cluttered environment.

show abstract

“…Considering that a small

α

would lead to a lot of local minimum, we solve the problem () by iterations with a decreasing sequence of

α

to avoid the local minima and to achieve a sparse solution. We refer the readers to [9] for more details of the algorithm (i.e. 2D generalised smoothed‐