# Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network
## 1 Introduction  
With the rapid advancement of unmanned aerial vehicle (UAV) technology [1], the application domains of UAVs continue to expand, posing higher demands on the accuracy and stability of UAV navigation systems. Traditional UAV navigation algorithms often rely on single-sensor data sources such as GPS and Inertial Measurement Units (IMUs) [2]. However, in complex environments, these sensor data are susceptible to noise, interference, and errors, leading to a decrease in navigation accuracy. To ensure that UAVs can efficiently and accurately execute tasks in complex environments, the performance of their navigation systems becomes crucial. Therefore, research on UAV navigation algorithms based on the fusion of multisensor data holds significant practical significance and application value. During the execution of tasks, UAV navigation systems typically adopt a fusion navigation approach integrating the Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) [3]. While GNSS offers long-term high-precision capabilities and cost-effectiveness, its inherent drawback lies in susceptibility to severe electromagnetic interference in battlefield environments, leading to disturbances in the GNSS receiver signals. Conversely, INS provides a higher sampling rate, enabling continuous signal output for recursive estimation. However, when used alone, the INS system's navigation computation results may suffer from increased errors due to noise introduction through integration operations, leading to divergence over time. The advantages and disadvantages of INS and GNSS are complementary. Integrating the strengths of both technologies provides a continuous, high-bandwidth, comprehensive, and high-precision navigation solution. This integration not only overcomes performance issues of individual sensors but also yields a system performance surpassing that of a single sensor. Therefore, inpractical navigation computations, employing Kalman-filter-based theory to integrate INS computed data with GNSS information offers a more continuous and reliable navigation solution, enhancing navigation parameter computation results. Kalman filtering [4], as a classical estimation theory method, is widely employed in UAV navigation systems. It estimates and corrects the state of the UAV through prediction and update steps, thereby enhancing navigation accuracy. [PERSON] [5] and others proposed that under the assumption of process and measurement noises following zero-mean Gaussian distributions with known covariance matrices, the Kalman filter can achieve optimal estimation solutions. However, this assumption does not always hold in practical systems. Therefore, some scholars have proposed alternative methods from the perspectives of noise modeling and adaptive estimation. The first method involves a thorough analysis of the composition mechanism of noise and utilizes mathematically interpretable models to accurately describe the noise characteristics. Kalman filtering often adopts such mechanistic models as the core models of the system during state estimation. For example, [PERSON] et al. [6] ingeniously utilized Allan variance to analyze noise in IMUs in their 2016 study and successfully identified different noise components within the IMU. It is noteworthy that the dynamic Allan variance [7] was originally designed to assess the stability of atomic clocks but was later extended to capture nonstationary components in signals. Although this noise analysis method can provide targeted explanations for each system, its generalizability is relatively weak and is significantly affected by sensor differences, thus requiring further refinement. Recently, scholars have proposed an innovative nonlinear optimization method [8; 9; 10], which combines gradient search and Newton search strategies, aiming to more accurately identify noise using Gaussian activation functions. Another strategy focuses on the adaptive estimation of noise based on data characteristics.