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To address the issue that the existing multidomain fusion methods do not consider data correlation within mini-batch data, the first attempt is made in this letter to propose a method based on a sample intercorrelation learning multidomain fusion network (SIMFNet), which aims to accommodate multivariate domain data and further enhance the aquatic human activity recognition (AHAR) performance. To fully use the radar multidimensional information, the three-branch convolution neural network (CNN) feature extractor is first used to extract domain-specific features from the time–range map (TRM), time–Doppler map (TDM), and cadence velocity diagram (CVD). Then, the multidomain features are fused and fed into the graph construction layer (GCL) to generate instance graphs. Next, a graph aggregation layer (GAL) is applied to aggregate node information from various-hop neighborhood domains. Finally, node-level classification is used to achieve AHAR. The experimental results evaluated on the built AHAR dataset demonstrate that the proposed SIMFNet has better generalization performance than the state-of-the-art multidomain fusion methods. [ABSTRACT FROM AUTHOR]

Constant false alarm rate (CFAR) detection algorithms, which are widely used in frequency-modulated continuous wave (FMCW) radar systems, achieve target detection by employing a threshold determined on the basis of a predicted background level. However, in multitarget scenarios, the multitarget shadowing effect can lead to inaccurate prediction of the background level and improper setting of the threshold, which then results in severely degraded CFAR performance. To combat this multitarget shadowing effect, a novel CFAR algorithm based on sparsity adaptive correlation maximization (SACM-CFAR) is proposed in this work. The proposed SACM-CFAR algorithm realizes target detection by utilizing the correlation between linear measurements of the radar intermediate frequency (IF) signal and the sensing matrix. To achieve a desired false alarm rate, the proposed algorithm determines the threshold by estimating the distributed parameters of the reduced sample set obtained by removing the detected targets from the original sample set. Both simulation results and field test results verify that the proposed algorithm outperforms conventional algorithms in multitarget scenarios. [ABSTRACT FROM AUTHOR]