The large field optical theodolite is an important test equipment for the shooting range. Usually， the full-field angle measurement error of the large-field optical theodolite is corrected by using the partition modeling error correction method based on the true value of the light pipe. In order to improve the full-field angle measurement accuracy of large-field infrared theodolites， this paper proposes a divisional error correction method that relies on the theodolite's pointing value as the reference value for optical calibration. Two different correction methods based on the true value and the pointing value are used to construct two correction models respectively， solve the spatial angle of the same measured target， and carry out comparative experiments. The results show that the divisional error correction method based on the pointing value can obtain better angle measurement accuracy.

Aiming at the problem of low efficiency of multi-target detection and tracking of unmanned aerial vehicle （UAV） swarms under the restricted sensing range， a detection and tracking method of UAV swarms based on the clonal selection-infection immunity model is proposed. The virus infection process is introduced into the information transmission of the UAV swarm， and the UAV decision-making activation mechanism is constructed to ensure the effective scheduling of cluster resources； the UAV strategy decision-making mechanism is constructed by introducing the clone selection process to ensure the reasonable operation of the UAV. At the same time， the "overheating" strategy judgment mechanism is introduced to prevent the same strategy from being executed by multiple drones， and to reduce the probability of missing targets. The simulation results show that the proposed method can effectively improve the detection coverage area of the cluster area and realize the tracking of more targets.

To solve the problem of sensor optimal deployment in the constrained conditions, a multi-sensor optimal deployment method is proposed based on genetic algorithm particle swarm optimization (GA-PSO). The method firstly meshes the battlefield geography environment and establishes the constraint matrix of deployment according to the battlefield geography environment and tactical conditions. Then, the objective optimization function based on detection coverage is established. Finally, the GA-PSO is used to solve the optimal position of the sensor. The simulation verifies the effectiveness and rationality of the proposed method.