Huang Jianjun, Hu Haoqiang, Kang Li
School of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China.
Sensors (Basel). 2024 Jan 2;24(1):263. doi: 10.3390/s24010263.
In the field of maneuvering target tracking, the combined observations of azimuth and Doppler may cause weak observation or non-observation in the application of traditional target-tracking algorithms. Additionally, traditional target tracking algorithms require pre-defined multiple mathematical models to accurately capture the complex motion states of targets, while model mismatch and unavoidable measurement noise lead to significant errors in target state prediction. To address those above challenges, in recent years, the target tracking algorithms based on neural networks, such as recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and transformer architectures, have been widely used for their unique advantages to achieve accurate predictions. To better model the nonlinear relationship between the observation time series and the target state time series, as well as the contextual relationship among time series points, we present a deep learning algorithm called recursive downsample-convolve-interact neural network (RDCINN) based on convolutional neural network (CNN) that downsamples time series into subsequences and extracts multi-resolution features to enable the modeling of complex relationships between time series, which overcomes the shortcomings of traditional target tracking algorithms in using observation information inefficiently due to weak observation or non-observation. The experimental results show that our algorithm outperforms other existing algorithms in the scenario of strong maneuvering target tracking with the combined observations of azimuth and Doppler.
在机动目标跟踪领域，方位角和多普勒的联合观测可能会导致传统目标跟踪算法在应用中出现弱观测或无观测的情况。此外，传统目标跟踪算法需要预先定义多个数学模型来准确捕捉目标的复杂运动状态，而模型不匹配和不可避免的测量噪声会导致目标状态预测出现显著误差。为应对上述挑战，近年来，基于神经网络的目标跟踪算法，如递归神经网络（RNN）、长短期记忆（LSTM）网络和Transformer架构，因其独特优势被广泛用于实现准确预测。为了更好地对观测时间序列与目标状态时间序列之间的非线性关系以及时间序列点之间的上下文关系进行建模，我们提出了一种基于卷积神经网络（CNN）的深度学习算法——递归下采样-卷积-交互神经网络（RDCINN），该算法将时间序列下采样为子序列并提取多分辨率特征，以实现对时间序列之间复杂关系的建模，克服了传统目标跟踪算法因弱观测或无观测而低效利用观测信息的缺点。实验结果表明，在方位角和多普勒联合观测的强机动目标跟踪场景中，我们的算法优于其他现有算法。
