A safe and reliable task planning method is a prerequisite for the collaborative execution of ocean observation data collection tasks by multiple unmanned surface vessels (multi-USVs). Deep Reinforcement Learning (DRL) combines the powerful nonlinear function-fitting capabilities of deep neural networks with the decision-making and control abilities of reinforcement learning, providing a novel approach to solving the multi-USV task planning problem. However, when applied to the field of multi-USV task planning, it faces challenges such as a vast exploration space, extended training times, and unstable training process. To this end, this paper proposes a multi-USV task planning method based on improved deep reinforcement learning. The proposed method draws on the idea of a value decomposition network, breaking down the multi-USV task planning problem into two subproblems: task allocation and autonomous collision avoidance. Different state spaces, action spaces, and reward functions are designed for the various subproblems. Based on this, a deep neural network is used to map the state space of each subproblem to the action space of each USV, and the generated strategy of the deep neural network is assessed based on the corresponding reward function. This successfully integrates task allocation and path planning into a comprehensive task planning framework. Deep neural networks consist of the Actor networks and the Critic networks. During the training phase of the Critic network, different methods are used to train different Critic networks to improve the convergence speed of the algorithm. An improved temporal difference error method is specifically applied to train the Critic network for evaluating autonomous collision avoidance strategies, resulting in improving the autonomous collision avoidance ability of USVs. At the same time, to improve the efficiency of task allocation, hierarchical mechanisms, and regional division mechanisms are introduced to construct sub-system task planning models, which further decompose the task planning problem. A combination of successor features and an improved temporal difference error method is specifically applied to train another Critic network for evaluating the sub-systems task allocation schemes and collaborative motion trajectories, aiming to enhance the allocation efficiency of the sub-systems. Furthermore, transfer learning is employed to merge the sub-system task planning, using it as a constraint to direct the exploration and assessment of both the cluster task allocation schemes and the cluster collaborative motion trajectories. This enables rapid and accurate learning for task allocation within the multi-USV cluster. During the training phase of the Actor network, the introduction of the experience replay method and target network technique is employed to enhance the proximal policy optimization algorithm. This facilitates distributed joint training of the Actor network, thereby improving the accuracy of the algorithm. Simulation results validate the effectiveness and superiority of this method.

It is a serious problem that the performance loss is suffered by traditional Direction-of-Arrival (DOA) estimation methods in non-ideal environment, such as mutual coupling of array elements, coherent sources, colored noise and plethora targets. A data-driven robust DOA estimation framework is proposed for MIMO radar via deep neural networks (DNN), so as to overcome the problems mentioned before. The framework consists of an autoencoder, a feedforward network, a network parameters database and a series of parallel directed acyclic graph networks (DAGN). Assisted with feedforward network for target-number determination, matching parameters of networks will be loaded from database. The autoencoder acts like a noise filter, it reconstructs the noise-free covariance from the noisy signal and thus the generalization burden of the subsequent DOA estimation DAGN will be decreased. Each sub-network of the parallel DAGN consists of a convolutional neural network (CNN) and two bidirectional long short-term memory (BiLSTM) networks, from which the estimation of DOA will be obtained by regression. The simulation results show that the proposed method is superior to the traditional methods in a non-ideal environment, and can also perform well when the number of targets reaches the upper limitation of the degrees of freedom of MIMO radar.

With the advent of the Internet of Things (IoT) era, the multi-unmanned aerial vehicle (UAV) networks have attracted great attention in the fields of source detection and localization. However, as a real-time signal processing performance of the UAV is limited by the computing speed and accuracy of the embedded hardware, the effectiveness of source localization is greatly reduced. Aiming at improving the accuracy and computational efficiency of source localization, a Cramer-Rao bound (CRB) weighted multi-UAV network source localization method is proposed based on the deep neural networks (DNN) and spatial spectrum fitting (SSF). The proposed source localization system is composed of UAVs equipped with a radar array. The source location can be achieved using the DOA of the sources signals of UAVs, but the accuracy and real-time performance of the conventional DOA estimation algorithms are not satisfactory, and the data fusion strategy of the conventional cross-location framework needs further improvement. In the proposed method, a DNN-based SSF denoted as the deep SSF (DeepSSF), is designed to achieve accurate DOA estimation. In the DeepSSF, the DOA estimation performance is guaranteed by the DNN’s strong nonlinear fitting ability and highly parallel structure. In addition, based on the obtained DOA information, the source is located once by every two UAVs. Finally, the source localization is realized based on the weighted CRB according to the principle that the more the DOA distribution deviates from zero, the lower the estimation accuracy. The simulation results verify the efficiency of the proposed method.

Traditional vital sign monitoring devices typically involve direct contact with the skin using electrodes, making them unsuitable for daily vital sign monitoring due to the discomfort and skin damage. Remote detection technology provides an effective solution to these issues. This article introduces an efficient and robust algorithm for estimating vital signs using frequency-modulated continuous-wave (FMCW) radar. The integration of this method with emerging technologies such as the Internet of Things (IoT), enables long-term and contactless vital sign monitoring, which facilitates a new model of self-management for chronic diseases and their prevention. While the breathing estimation accuracy is typically constrained by noise, heart rate estimation is primarily hindered by strong interference from the breathing signal and its higher-order harmonics. A maximum likelihood estimatior based on the Newton’s method is derived and proposed in this paper to accurately assess the breathing and heartbeat frequencies by enhancing the precision of vital sign parameter estimation. The proposed algorithm is validated utilizing a 77 GHz FMCW radar and compared with a reliable reference sensor. Experimental results from eight subjects demonstrate that the proposed method enhances the estimation accuracy, outperforming both conventional spectral estimation and other methods. Specifically, the root mean square error between the reference sensor measurements and the estimations is lower than 1 bpm for breathing rates and 1.5 bpm for heart rates. Additionally, the Bland-Altman plots demonstrate a high level of agreement between these estimations and the reference measurements.

The traditional frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar two-dimensional (2D) super-resolution (SR) estimation algorithm for target localization has high computational complexity, which runs counter to the increasing demand for real-time radar imaging. In this paper, a fast joint direction-of-arrival (DOA) and range estimation framework for target localization is proposed; it utilizes a very deep super-resolution (VDSR) neural network (NN) framework to accelerate the imaging process while ensuring estimation accuracy. Firstly, we propose a fast low-resolution imaging algorithm based on the Nystrom method. The approximate signal subspace matrix is obtained from partial data, and low-resolution imaging is performed on a low-density grid. Then, the bicubic interpolation algorithm is used to expand the low-resolution image to the desired dimensions. Next, the deep SR network is used to obtain the high-resolution image, and the final joint DOA and range estimation is achieved based on the reconstructed image. Simulations and experiments were carried out to validate the computational efficiency and effectiveness of the proposed framework.

A generalized noise reconstruction approach is proposed for improved direction-of-arrival (DOA) estimation. First, coarse estimation of the noise component in the received data is obtained based on cumulative sum of an approximate first-order finite difference series, where the generalized pattern search (GPS) algorithm is used to find the best initial value. The objective function is constructed with the total noise power estimated by the least squares (LS) method. After that, a new cumulative sum constructed with the solution from the previous step is used to accurately reconstruct the noise component in the data. Finally, robust DOA estimation is achieved through the denoised data. In simulations, different cases of noise, such as non-uniform noise, colored noise, impulsive noise, and K-distribution sea clutter, are considered. TI frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar data is also used to demonstrate the effectiveness of the proposed approach.