A Coherent Integration Method for Moving Target Detection Using Frequency Agile Radar

Huang, Penghui; Dong, Shuoshuo; Liu, Xingzhao; Jiang, Xue; Liao, Guisheng; Xu, Haisong; Sun, Siyue

doi:10.1109/lgrs.2018.2870869

Cited by 29 publications

(12 citation statements)

References 14 publications

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“…The frequency of the nth pulse can be denoted by f n = f 0 + k n ∆ f , where f 0 is the initial carrier frequency, ∆ f is the minimal frequency agility interval, and k n controls the rules of how carrier frequency varies. Based on the signal model given in [15], the received signal of the nth pulse at the mth array element after matched filtering can be expressed as…”

Section: Signal Modelmentioning

confidence: 99%

“…In [7], Wang et al proposed an improved frequency agile coherent radon transform (FA-CRT) scheme to cope with the long-time coherent integration in frequency agile radar. In [15], P. Huang et al proposed a method for removing the residual coupling between the agile carrier frequency and slow-time by using Keystone Transform (KT) between different bursts to achieve coherent accumulation. In [16], Li et al proposed a combined range-velocity estimation method based on compressive sensing (CS) in the range-velocity plane, where the CS is employed to reduce the number of channels, therefore, the system complexity and the computational burden are effectively reduced [17].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fast Implementation of Approximated Maximum Likelihood Parameter Estimation for Frequency Agile Radar under Jamming Environment

Zhao

Suo

et al. 2020

Sensors

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A computationally efficient target parameter estimation algorithm for frequency agile radar (FAR) under jamming environment is developed. First, the barrage noise jamming and the deceptive jamming are suppressed by using adaptive beamforming and frequency agility. Second, the analytical solution of the parameter estimation is obtained by a low-order approximation to the multi-dimensional maximum likelihood (ML) function. Due to that, fine grid-search (FGS) is avoided and the computational complexity is greatly reduced.

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Section: Signal Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fast Implementation of Approximated Maximum Likelihood Parameter Estimation for Frequency Agile Radar under Jamming Environment

Zhao

Suo

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…It should be noted that the phase errors of the FAR system will inevitably remain in the transceiver system, even after the phase error is corrected. In the literature mentioned above, we only consider coherent signal processing method without system phase errors [16,17]. However, in the practical application, especially when the agile frequency range reaches GHz, we must consider how to accomplish phase error correction and range and velocity information extraction simultaneously [18,19].…”

Section: Introductionmentioning

confidence: 99%

Joint phase error estimation and sparse scene restoration algorithms for frequency agile radar

Fei

Zhang

Zhu

et al. 2021

IET Radar Sonar & Navi

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Frequency agile radar (FAR) systems have been demonstrated to have outstanding performance in electronic counter-countermeasures (ECCM). With increased bandwidth in which FAR can switch carrier frequency, the phase errorswill appear at the corresponding frequency point, resulting in the decrease of coherence of the system. To accomplish phase error correction and range and velocity information extraction of the target simultaneously, a joint phase error estimation and sparse scene restoration algorithm based on sparse recovery (JPSA-SR) is proposed here. To further reduce the computational complexity of the algorithm, a joint phase error estimation and sparse scene restoration algorithm based on alternating direction method of multipliers (JPSA-ADMM) is also proposed. Numerical examples show the effectiveness of the proposed algorithms.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…In recent years, along with intense confrontation degree of Modern electronic warfare (MEW) unceasingly escalating, electronic signals have become more and more complex [1][2][3][4] , Frequency-agility (FA) and Pulse repetition frequency jittering (PRFJ) signal (FA-PRFJ signal) is one of them [5][6][7] . Because of its excellent anti-interference performance, lowinterception probability and inherent security features [5] , FA-PRFJ signal has been widely used in the emitters of many high-value civilian and military targets, such as the radar equipped in hypersonic aircraft [8] .…”

Section: Introductionmentioning

confidence: 99%

Coherent Integration Algorithm for Frequency‐Agile and PRF‐Jittering Signals in Passive Localization

Jiang

Zhao

2021

Chinese J of Electronics

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Increasing pulses Coherent processing interval (CPI) can effectively improve the location parameters estimation performance in passive localization. However, for a moving emitter transmitting pulses with Frequency agile and Pulse repetition frequency jittering (FA-PRFJ) in a CPI, there will exist random phase, uneven sampling and Range migration (RM), which deteriorates the estimation performance of location parameters. Aiming at long-time coherent localization parameters estimation for the above emitter, this paper proposes a joint Range difference (RD) and Range rate difference (RRD) estimation algorithm. Firstly, the signal model of a moving emitter transmitting FA-PRFJ signal is constructed, and the influence of the FA and PRFJ on coherent integration is analyzed. Secondly, the random phase induced by FA is eliminated by frequency symmetric autocorrelation function operation. Then, RD and RRD can be coherently estimated after RM correction via the modified scaled non-uniform fast Fourier transform. This method can be efficiently implemented by Fast Fourier transform (FFT), inverse FFT and FFT-based chirp-z transform without any searching operation. Simulation results demonstrate that the proposed method has a better antinoise capability with a much lower computational complexity compared with several representative methods.

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A Coherent Integration Method for Moving Target Detection Using Frequency Agile Radar

Cited by 29 publications

References 14 publications

Fast Implementation of Approximated Maximum Likelihood Parameter Estimation for Frequency Agile Radar under Jamming Environment

Fast Implementation of Approximated Maximum Likelihood Parameter Estimation for Frequency Agile Radar under Jamming Environment

Joint phase error estimation and sparse scene restoration algorithms for frequency agile radar

Coherent Integration Algorithm for Frequency‐Agile and PRF‐Jittering Signals in Passive Localization

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