LPI radar target detection performance optimization based on joint cognitive frequency transmission and power allocation

Song, Yuxiao; Wei, Song; Tian, Biao; Xu, Shiyou; Zhang, Lei

doi:10.1016/j.sigpro.2022.108736

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…In this case, there must be some groups that do not contain effective signal components, and these groups do not contribute to SNRAA. According to this feature, AAE is introduced to represent the contribution of each group of data to the improvement in the accumulated SNR, which is defined as (15). The adoption of AAE aids in preventing the PSO algorithm from being entangled in the PRF harmonic component local optimization.…”

Section: =mentioning

confidence: 99%

“…Based on the information obtained by ESM, effective electronic countermeasures (ECM) [10][11][12] can be easily generated to counter specific radar sources. However, with the iteration and development of radar technology, low probability interception (LPI) [13][14][15][16][17] radar is widely used in practice, which makes the signal power lower than before. Moreover, it is difficult to guarantee that the ESM receiver is arranged in hostile radar's main lobe irradiation areas.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

High-Quality Radar Pulse Signal Acquisition and Deinterleaving under a Low Signal-to-Noise Ratio with Multi-Layer Particle Swarm Optimization

Wei,

Fang,

et al. 2024

Remote Sensing

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Acquiring pulse signals of radar source is an essential component in implementing electronic support measures (ESM). The conventional signal detection or deinterleaving method are mainly applied in relatively simple environments. Currently, radar electronic reconnaissance signal processing capability is severely constrained by poor signal-to-noise ratio (SNR) and the interleaving of signals from various radar sources. This research develops a multi-layer particle swarm optimization (PSO) pulse extraction and deinterleaving technique to improve ESM’s efficacy further. First, coherent accumulation of the received signals is performed using PSO to obtain higher SNR pulse. Second, the signals from this radar source are deinterleaved using the obtained pulse. Ultimately, the aforementioned procedures are combined into a multi-layer PSO architecture to capture radar source signals and deinterleaving them at low SNRs. The suggested algorithm’s efficacy and robustness are confirmed through simulation experiments.

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Section: =mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

High-Quality Radar Pulse Signal Acquisition and Deinterleaving under a Low Signal-to-Noise Ratio with Multi-Layer Particle Swarm Optimization

Wei,

Fang,

et al. 2024

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, this approach cannot sufficiently reflect the characteristics of non-stationary signals. In fact, most stochastic processes, including radar navigation signals [1], satellite signals [2], sonar signals [3] and so on are non-stationary in nature [4]. It is therefore vital to extract the characteristics of non-stationary signals with the objective of developing signal processing methods that are more appropriate for the representation, analysis, and processing of said signals.…”

Section: Introductionmentioning

confidence: 99%

Enhancing time‐frequency resolution via deep‐learning framework

Wang

Chen

Xiao

et al. 2023

IET Signal Processing

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The fixed window function used in the short‐time Fourier transform (STFT) does not guarantee both time and frequency resolution, exerting a negative impact on the subsequent study of time‐frequency analysis (TFA). To avoid these limitations, a post‐processing method that enhances the time‐frequency resolution using a deep‐learning (DL) framework is proposed. Initially, the deconvolution theoretical formula is derived and a post‐processing operation is performed on the time‐frequency representation (TFR) of the STFT via deconvolution, a theoretical calculation to obtain the ideal time‐frequency representation (ITFR). Then, aiming at the adverse influence of the window function, a novel fully‐convolutional encoder‐decoder network is trained to preserve effective features and acquire the optimal time‐frequency kernel. In essence, the generation of the optimal time‐frequency kernel can be regarded as a deconvolution process. The authors conducted the qualitative and quantitative analyses of numerical simulations, with experimental results demonstrate that the proposed method achieves satisfactory TFR, possesses strong anti‐noise capabilities, and exhibits high steady‐state generalisation capability. Furthermore, results of a comparative experiment with several TFA methods indicate that the proposed method yields significantly improved performance in terms of time‐frequency resolution, energy concentration, and computational load.

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Joint waveform design and resource allocation strategy for cognitive radar target situation awareness

Song,

Tian,

Wang

et al. 2024

IET Radar Sonar & Navi

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Traditional radar systems use fixed patterns and constant electromagnetic wave transmission to illuminate targets, but they often do not effectively use prior information about targets and consume significant radar resources. Cognitive radar has emerged as a way to improve resource efficiency and address these shortcomings. A joint waveform design and resource allocation strategy for cognitive radar that incorporates target situational awareness is proposed. This method integrates the interacting multiple model algorithm and the Unscented Kalman Particle Filter to achieve target situation awareness as prior knowledge. By combining the target attitude and the frequency response function of the target radar cross section at different time points in the prior knowledge, a joint beam control and power allocation strategy is formulated and transformed into an optimization problem. In addition, a cognitive pulse‐to‐pulse frequency agile waveform design method is proposed to support multiple target tracking under complex motion models. Simulation experiments demonstrate the effectiveness of this approach in obtaining accurate target situation information, achieving beam control, and optimizing power allocation. The designed waveforms can enhance radar target detection performance and improve low probability of intercept characteristics by adjusting the pulse repetition interval. This method has significant technical value.

show abstract

LPI radar target detection performance optimization based on joint cognitive frequency transmission and power allocation

Cited by 4 publications

References 16 publications

High-Quality Radar Pulse Signal Acquisition and Deinterleaving under a Low Signal-to-Noise Ratio with Multi-Layer Particle Swarm Optimization

High-Quality Radar Pulse Signal Acquisition and Deinterleaving under a Low Signal-to-Noise Ratio with Multi-Layer Particle Swarm Optimization

Enhancing time‐frequency resolution via deep‐learning framework

Joint waveform design and resource allocation strategy for cognitive radar target situation awareness

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