# Navigation Control and Signal Processing Methods for Multiple Autonomous Unmanned Systems
## 2 An Overview of the Published Articles  
This section presents a comprehensive overview of the Special Issue title \"Navigation Control and Signal Processing Methods for Multiple Autonomous Unmanned Systems\", summarizing the key contributions of the published articles. Multi-robot task assignment is one of the main processes in an intelligent warehouse. One of the papers models multi-robot task assignment in an intelligent warehouse as an open-path multi-depot asymmetric traveling salesman problem (OP-MATSP). A two-objective integer linear programming (ILP) model for solving OP-MDTSP is proposed. The theoretical bound on the computational time complexity of this model is O(n!). We can solve the small multi-robot task assignment problem by solving the two-objective ILP model using the Gurobi solver. The multi-chromosome coding-based genetic algorithm has a smaller search space, so we use it to solve large-scale problems. The experimental results reveal that the two-objective ILP model is very good at solving small-scale problems. For large-scale problems, both the EGA and NSGA3 genetic algorithms can efficiently obtain suboptimal solutions. This demonstrates that the paper's multi-robot work assignment methods are helpful in an intelligent warehouse [61]. Additionally, a localization system is one of the basic requirements for coal mines. Ultra-wideband (UWB), as a technology with broad application prospects, is considered to have great potential in the absence of satellite signals, especially in an underground mine environment, as it can provide rescue assistance. However, state-of-the-art UWB position systems in coal mines cannot efficiently differ the line of sight from all communication links, which results in the deterioration of the localization accuracy. In another paper in this Special Issue, the authors propose an LOS/NLOS classification method based on a deep learning algorithm. Specifically, they utilize the Generative Adversarial Network (GAN) to generate diagnostic data for frame transmission under non-line-of-sight (NLOS) conditions. Then, a Convolutional Neural Network (CNN) is adopted to identify NLOS communication. Finally, the trilateral centroid positioning algorithm (TCPA), based on ranging data, is used to verify the effect of our method for a localization system in coal mines [62]. Another recent study, included in this Special Issue, proposes an improved in-motion coarse alignment method for a strapdown inertial navigation system (SINS) using position loci obtained from the Global Positioning System (GPS). The difference from the popular coarse alignment methods is that the proposed algorithm uses GPS position loci information to form the vector observation and does not need velocity information, which expands the application range of in-motion coarse alignment. In addition, this paper utilizes the Optimal-REQUEST algorithm to reduce the influence of random errors contained in the vector observation. The Optimal-REQUEST algorithm is an adaptive iterative updating algorithm, which can adaptively adjust the gain of the filter according to the loss function. The simulation results confirmed that the proposed algorithm can suppress the impact of random errors effectively. The pitch, roll, and yaw angles calculated by the proposed algorithm were improved by 51.95%, 53.80%, and 63.03% compared with the comparison algorithms [63]. There are various types of autonomous unmanned systems, covering different areas of sea, land, and air and exhibiting significance in multiple fields of national security and social life. Due to the development of technology, the scale of unmanned systems is becoming increasingly large, the number of components in the system is increasing, and the operating environment of the system is becoming increasingly complex. Therefore, the probability of failure of the components of the system will also be significantly increased. In order to eliminate the impact of a fault in time, the fault diagnosis method is significant.