The high complexity and long computing time of Scale-Invariant Feature Transform(SIFT) algorithm cannot meet the real-time requirements of stereo matching. And the mismatching rate is high when an image has many similar regions. To solve the problems, an improved stereo matching algorithm was proposed. The proposed algorithm was improved in two aspects. Firstly, because the circular has natural rotation invariance, the feature point was acted as the center and the rectangle region of the original algorithm was replaced by two approximate-size concentric circle regions in the improved algorithm. Meanwhile, the gradient accumulated values of 12 directions were calculated within the areas of the inner circle and the outer circle ring respectively, and the dimension of the local feature descriptor was reduced from 128 to 24. Then, a 12-dimensional global vector was added, so that the generated feature descriptor contained the SIFT vector based on local information and the global vector based on global information, which improved the resolving power of the algorithm when the images had similar areas. The simulation results show that, compared with the original algorithm, the real-time performance of the proposed algorithm was improved by 59.5% and the mismatching rate was decreased by 9 percentage points when the image had many similar regions. The proposed algorithm is suitable for in the case of high real-time image processing.

For the nonlinear, non-Gaussian and high dynamic model in Strapdown Inertial Navigation System/Global Navigation Satellite System (SINS/GNSS) tightly integrated navigation system, the general K-means Particle Swarm Optimization (PSO) algorithm was ineffective, and the particle impoverishses and diverges greatly. A novel K-means PSO algorithm was proposed. According to the Geometric Dilution Of Precision (GDOP) of the SINS/GNSS tightly integrated navigation system, the weight of particle was updated, and the weight of each K-means was updated. The novel algorithm was applied in SNS/GNSS tightly integrated navigation system. The simulation result shows that the novel algorithm can restrain the divergence effectively and it improves precision.

The conventional Receiver Autonomous Integrity Monitoring (RAIM) algorithm is limited when detecting weak pseudo-range bias under gradual change because of its longer detection delay and higher miss detection rate. A weight modification accumulated epochs parity vector RAIM algorithm based on self-adaptive strategy was presented to solve this problem. In this algorithm, the weight factor was obtained according to the single epoch fault degree to adjust the proportion of each epoch in the selected window to structure more effective detection statistics, and the size of the window was determined according to the repeated simulation experiments. The simulation results show that the proposed method can better detect weak pseudo-range bias under gradual change, compared to accumulated epoch and the conventional RAIM algorithm, the detection delay time declines by 16.67% and 56.52% respectively.