In order to solve the problems of low robustness and high complexity of power allocation algorithm in cooperative communication, under the constraints of limited channel strength, number of nodes and total power, this paper presents a power allocation algorithm based on channel capacity in cooperative communication. Based on the traditional channel capacity model, the harmonic mean and average weight of channel strength are introduced to simplify the solution process of the objective function in order to reduce the complexity of the algorithm. The crossover and mutation rules in the genetic algorithm are introduced to avoid local optima, and intelligent velocity constraints of particles are established to reduce the algorithm complexity. The optimal function is established by calculating the channel strength weight coefficients. Simulation results show that the proposed algorithm has improved in channel capacity and convergence compared to the mainstream and original algorithms. The improved algorithm has better practicality and advancement in cooperative communication.
KEYWORDS: Data modeling, Education and training, Machine learning, Covariance matrices, Signal processing, Tunable filters, Signal filtering, Signal attenuation, Performance modeling, Detection and tracking algorithms
High precision indoor positioning technology is the key research content in the field of communication and control. In order to reduce the influence of environmental factors on indoor positioning accuracy, a fingerprint location model fusion algorithm based on channel state information (CSI) was proposed. In the off-line stage, the pauta criterion is used to remove abnormal data, and then the CSI amplitude is filtered by Kalman to make the CSI amplitude have certain periodic characteristics. Then the phase of CSI is corrected by linear transformation, and the processed amplitude and phase are taken as the joint CSI fingerprint. Finally, CatBoost-KNN model fusion localization algorithm was used to train fingerprint data. In the online stage, the coordinates of the pre-processed points to be measured are predicted by the trained CatBoost- KNN model. Simulation results show that the average positioning error of this method is 0.96m. Compared with some existing positioning methods, the positioning accuracy of the improved method is improved, and the algorithm has good practicability in large-scale indoor positioning applications.
In order to solve the problem of low network coverage due to random deployment of nodes in wireless sensor networks (WSN), this paper proposes an Improved Fruit Fly Optimization Algorithm (IFOA) and uses the network coverage as the algorithm optimization objective function. First, a Tent chaotic mapping is used to initialize the population so that the initial population individuals are diverse. Second, a linear decay step strategy is used to balance the global search in the first stage of the algorithm and the local search in the second stage. Finally, the optimal fruit fly individuals are perturbed by the Cauchy mutation to prevent the algorithm from falling into a local optimum. And the algorithm is applied to the wireless sensor network coverage optimization problem. Simulation results show that the algorithm effectively improves the network coverage, reduces node redundancy, and makes the network have higher coverage performance.
Combined navigation system is a navigation and positioning system composed of inertial navigation system and BeiDou satellite navigation system. Most of the navigation system models in combined navigation are nonlinear, but the traditional Kalman filtering algorithm is not well applied to nonlinear equations, and the Unscented Kalman filtering algorithm and Extended Kalman filtering algorithm which can be applied to nonlinear equations are constant in the fusion process of noise, so it will cause filtering divergence. In this paper, on the basis of Unscented Kalman filtering algorithm proposed will introduce the square root traceless Kalman filter algorithm, the algorithm through QR decomposition and Cholesk decomposition, the Sage-Husa algorithm combined with Square Root Unscented Kalman Filter algorithm, directly calculate the state error covariance matrix prediction and estimation of the square root factor, maintain the stability of the filtering, through practice proved that compared to Kalman filtering .The Nonlinear adaptive regression square root Kalman filter filter has a good navigation and positioning function, as the filtering is more convergent and the position accuracy can be within 5m, the speed error can be between 0.5m/s-1m/s. Compared with KF algorithm, the position error is increased by about 75%, and the speed error is increased by about 50%.
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