To solve the problem that the antenna resources in heterogeneous network are limited which leads to the unrealizable Interference Alignment (IA), a partial IA scheme for maximizing the utilization of antenna resources was proposed based on the characteristics of heterogeneous network. Firstly, a system model based on partial connectivity in heterogeneous network was built and the feasibility conditions for entire system to achieve IA were analyzed. Then, based on the heterogeneity of network (the difference between transmitted power and user stability), the users were assigned to different priorities and were distributed with different antenna resources according to their different priorities. Finally, with the goal of maximizing total rate of system and the utilization of antenna resources, a partial IA scheme was proposed, in which the high-priority users had full alignment and low-priority users had the maximum interference removed. In the Matlab simulation experiment where antenna resources are limited, the proposed scheme can increase total system rate by 10% compared with traditional IA algorithm, and the received rate of the high-priority users is 40% higher than that of the low-priority users. The experimental results show that the proposed algorithm can make full use of the limited antenna resources and achieve the maximum total system rate while satisfying the different requirements of users.

In practical application, Modified Gain Extended Kalman Filter (MGEKF) algorithm generally uses erroneous measured values instead of the real values for calculation, so the modified results also contain errors. To solve this problem, an improved MGEKF algorithm based on Back Propagation Neural Network (BPNN), termed BPNN-MGEKF algorithm, was proposed in this paper. At BPNN training time, measured values were used as the input, and modified results by true values as the output. BPNN-MGEKF was applied to single moving station bearing-only position experiment. The experimental results shows that, BPNN-MGEKF improves the positioning accuracy of more than 10% compared to extended Kalman filter, MGEKF and smoothing modified gain extended Kalman filter algorithm, and it is more stable.