High-precision initialization and acceleration of particle filter convergence to improve the accuracy and stability of terrain aided navigation.

Initial positioning errors and the low adaptability of a priori digital elevation maps result in large positioning uncertainty intervals in the initial stage of terrain-aided navigation (TAN). This produces pseudo-peaks and mismatches in the initial position likelihood function and renders the convergence of the particle filter (PF) slow and unstable, while even causing divergence. Thus, the occurrence of the "kidnapped robot problem" is highly probable during the initial stage of TAN and is a scenario frequently faced by deep-sea and ultra-long-range underwater vehicles. In this study, a PF initialization method based on non-linear multi-terrain aided fusion position (NLMTP) is proposed to improve the stability and accuracy of TAN. NLMTP uses the terrain-aided position (TAP) information during the initial stage of TAN to estimate the high-precision probability distribution of the starting position via backward smoothing. Accordingly, a PF initialization method for non-Gaussian prior distribution probability is proposed to improve the convergence speed of the PF during the initial stage of underwater TAN. Finally, a performance comparison of PF initialized via the NLMTP, TAP confidence interval, and TERCOM methods was performed using the survey data obtained via onboard sensors. The experimental results show that NLMTP initialization improves the convergence speed and positioning accuracy of PF in the initial TAN phase; this improvement is clear in the low terrain-adaptability area.

An AUV localization and path planning algorithm for terrain-aided navigation.

Terrain-aided navigation (TAN) holds high potential for long-term accurate navigation of autonomous underwater vehicles (AUVs), and path planning algorithms are essential in TAN to decrease positioning errors by avoiding flat areas. This study proposed an AUV localization and path planning algorithm for TAN, which consists of a value function calculation and online path planning. In the value function calculation, the topographic complexity is treated as a factor that influences AUV state transition probabilities to calculate the optimal policy; meanwhile, the online path planning applies a particle filter to localize and command AUVs, and particle weights are calculated according to topographic complexity. Simulation experimental results demonstrate that this algorithm could provide paths with accurate TAN location results and good maneuvering performance.

A Robust Terrain Aided Navigation Using the Rao-Blackwellized Particle Filter Trained by Long Short-Term Memory Networks.

Terrain-aided navigation (TAN) is a technology that estimates the position of the vehicle by comparing the altitude measured by an altimeter and height from the digital elevation model (DEM). The particle filter (PF)-based TAN has been commonly used to obtain stable real-time navigation solutions in cases where the unmanned aerial vehicle (UAV) operates at a high altitude. Even though TAN performs well on rough and unique terrains, its performance degrades in flat and repetitive terrains. In particular, in the case of PF-based TAN, there has been no verified technique for deciding its terrain validity. Therefore, this study designed a Rao-Blackwellized PF (RBPF)-based TAN, used long short-term memory (LSTM) networks to endure flat and repetitive terrains, and trained the noise covariances and measurement model of RBPF. LSTM is a modified recurrent neural network (RNN), which is an artificial neural network that recognizes patterns from time series data. Using this, this study tuned the noise covariances and measurement model of RBPF to minimize the navigation errors in various flight trajectories. This paper designed a TAN algorithm based on combining RBPF and LSTM and confirmed that it can enable a more precise navigation performance than conventional RBPF based TAN through simulations.

Underwater terrain-aided navigation system based on combination matching algorithm.

Considering that the terrain-aided navigation (TAN) system based on iterated closest contour point (ICCP) algorithm diverges easily when the indicative track of strapdown inertial navigation system (SINS) is large, Kalman filter is adopted in the traditional ICCP algorithm, difference between matching result and SINS output is used as the measurement of Kalman filter, then the cumulative error of the SINS is corrected in time by filter feedback correction, and the indicative track used in ICCP is improved. The mathematic model of the autonomous underwater vehicle (AUV) integrated into the navigation system and the observation model of TAN is built. Proper matching point number is designated by comparing the simulation results of matching time and matching precision. Simulation experiments are carried out according to the ICCP algorithm and the mathematic model. It can be concluded from the simulation experiments that the navigation accuracy and stability are improved with the proposed combinational algorithm in case that proper matching point number is engaged. It will be shown that the integrated navigation system is effective in prohibiting the divergence of the indicative track and can meet the requirements of underwater, long-term and high precision of the navigation system for autonomous underwater vehicles.

An Effective Terrain Aided Navigation for Low-Cost Autonomous Underwater Vehicles.

Terrain-aided navigation is a potentially powerful solution for obtaining submerged position fixes for autonomous underwater vehicles. The application of terrain-aided navigation with high-accuracy inertial navigation systems has demonstrated meter-level navigation accuracy in sea trials. However, available sensors may be limited depending on the type of the mission. Such limitations, especially for low-grade navigation sensors, not only degrade the accuracy of traditional navigation systems, but further impact the ability to successfully employ terrain-aided navigation. To address this problem, a tightly-coupled navigation is presented to successfully estimate the critical sensor errors by incorporating raw sensor data directly into an augmented navigation system. Furthermore, three-dimensional distance errors are calculated, providing measurement updates through the particle filter for absolute and bounded position error. The development of the terrain aided navigation system is elaborated for a vehicle equipped with a non-inertial-grade strapdown inertial navigation system, a 4-beam Doppler Velocity Log range sensor and a sonar altimeter. Using experimental data for navigation performance evaluation in areas with different terrain characteristics, the experiment results further show that the proposed method can be successfully applied to the low-cost AUVs and significantly improves navigation performance.