Motivated by the problem of radar target recognition, we develop a label-aided factor analysis (LA-FA) model for statistical modeling of high-resolution range profile (HRRP) under the prerequisite that the HRRP data are Gaussian distributed. The LA-FA model is the extension of the multitask learning-based factor analysis (MTL-FA) model, which is mainly applied to the recognition problem with small training data size. Compared to the MTL-FA model, our LA-FA model introduces the discrete class labels via Sigmoid-Bernoulli hierarchy to restrict the learning of model parameters, which offers the potential to enhance the separability of statistical models from different classes, thus beneficial to the improvement of recognition capability. In addition, since the noise level of a test sample is usually different from those of the training samples in the real application, we introduce a noise-robust modification method for Gaussian-based models. The proposed modification method is implemented by updating the noise level parameter of the statistical model according to the estimated signal-to-noise ratio (SNR) of test HRRP. Experiments on measured HRRP data demonstrate the better recognition performance of the LA-FA model with limited training data and our noise-robust model modification method under low test SNR. Especially, when there are 20 training HRRP samples per frame, the recognition rate of our LA-FA model is 7% higher than that of the MTL-FA model, and moreover, the recognition accuracy of the noise-robust LA-FA model is 3% higher than that of the LA-FA model without modification under the condition of SNR = 15 dB.

中文翻译：

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带有标签约束的雷达目标识别统计建模

受雷达目标识别问题的启发，我们在 HRRP 数据呈高斯分布的前提下，开发了一种标签辅助因子分析 (LA-FA) 模型，用于高分辨率距离剖面 (HRRP) 的统计建模。LA-FA模型是基于多任务学习的因子分析（MTL-FA）模型的扩展，主要应用于训练数据量较小的识别问题。与 MTL-FA 模型相比，我们的 LA-FA 模型通过 Sigmoid-Bernoulli 层次结构引入离散类标签来限制模型参数的学习，这提供了增强来自不同类的统计模型的可分离性的潜力，从而有利于提高识别能力。此外，由于测试样本的噪声水平通常与实际应用中的训练样本的噪声水平不同，因此我们引入了一种基于高斯模型的噪声鲁棒修改方法。所提出的修改方法是通过根据测试 HRRP 的估计信噪比 (SNR) 更新统计模型的噪声水平参数来实现的。对测量的 HRRP 数据进行的实验表明，在训练数据有限的情况下，LA-FA 模型和我们的噪声鲁棒模型修改方法在低测试 SNR 下具有更好的识别性能。特别是当每帧有 20 个训练 HRRP 样本时，我们 LA-FA 模型的识别率比 MTL-FA 模型高 7%，而且，