Search and rescue (SAR) is an important part of joint operations and a key support for combat effectiveness. Because of the complex composition of the SAR system of systems (SoS), sensitivity analysis method is usually used to carry out sensitivity analysis of SoS capability, so as to determine the main design indicators affecting SoS capability. Reliable sensitivity analysis results are often based on the analysis for sufficient data. However, the SAR SoS capability is affected by many factors and there are numerous design indicators. Even if a small number of design points are selected for each design indicator, tens of thousands of test schemes will be formed, and carrying out all simulation tests will bring huge workload and time cost. To solve this contradiction, this paper introduces a bidirectional deep neural network (BDNN), and takes advantage of its better self-learning and adaptive features and unique structure to train the existing test data, Through strong feature extraction ability of BDNN, the network model between the design indicator and capability indicator is formed, namely, the capability fitting and data reconstruction (CFDR) model, so that the implicit relationship between the two is fixed into the model. In the training process, the number of hidden layers and neurons in each hidden layer, and the amount of training data are explored according to the training effect, so as to obtain a better parameter combination. In order to avoid introducing large cumulative errors accumulated during BDNN pre-training into DNN, particle swarm optimization (PSO) was introduced to optimize weight parameters and avoid large training errors being transmitted to deep neural network (DNN). Meanwhile, three basic functions were used to verify the strong global optimization and convergence abilities of the BDNN after optimized by the PSO (PSO-BDNN). Finally, the new test scheme is applied to the CFDR model to obtain the SoS capability value. The reconstructed data obtained from the CFDR model based on BDNN and PSO-BDNN respectively were compared with the simulation test data.The results show that the reconstruction accuracy of the CFDR model based on the PSO-BDNN is greatly improved than that of the BDNN. And the feasibility of this model as a reconstruction data generation model and the effectiveness of this model as an analysis data extension method applied to the sensitivity analysis of insufficient data to obtain reliable analysis results are verified.INDEX TERMS Capability fitting and data reconstruction model, bidirectional deep neural network, particle swarm optimization, search and rescue, SoS capability sensitivity analysis.The associate editor coordinating the review of this manuscript and approving it for publication was Mitra Mirhassani .

Search and rescue (SAR) is an important part of joint operations, and also one of the key supports for ensuring combat effectiveness. Aviation equipment is a major component of SAR action. Therefore, the SAR capability of aviation equipment has become the key to affecting the overall SAR action. This paper proposes the concept of the system of systems influence degree (SoSID) and conducts a scientific quantitative evaluation to quantitatively measure the effect of aviation equipment used in SAR. First, according to the characteristics of SAR action in threat environments, the SAR capability of aviation equipment is analyzed, and an indicator decomposition hierarchy model based on this SAR capability is proposed. Second, based on the above model, the DECIDE (destroy, execute, cost, implement, defend, evade) SoSID evaluation model is proposed. Third, a comparative test is designed and a sensitivity analysis is conducted based on the sobol power sensitivity (SPS) analysis method to obtain the index sensitivity of the SAR capability. The sensitivity is then ranked to obtain key indicators. Finally, we build a simulation test environment to obtain multiple test plans for comparison and verify the rationality of the index decomposition hierarchy model and the SoSID evaluation model as well as the effectiveness of the SPS analysis method through analysis of the simulation results.