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For uncertainty measurement problem in the assessment process of technology maturity level and integration maturity level, System maturity assessment model based on uncertainty measurement method of information systems is proposed. Firstly, assessment architecture of system maturity level of information systems, which consists of technology maturity level and integration maturity level, is constructed. Secondly, uncertainty measurement assessment model based on confidence identification criteria and short board principle is introduced. And finally, an example of system maturity level assessment of a command and control system is given. The example result shows that the proposed method is reasonable and valid.

Present combat effectiveness evaluation methods of weapon system, such as statistical testing method and analytic method, are characterized by small sample sizes. All parameters used in such methods are calculated as determinate numbers. Sometimes this makes the calculation results differ violently from actual outcomes. Different evaluating consequences often fall into the same grade, making it difficult to distinguish individual differences. This paper proposes a novel evaluation method, called grey relational evaluation method based on unascertained rational number, to solve the problems mentioned above. Firstly, the evaluating attributes are reduced according to rough set theory, and the corresponding weight computation algorithms are developed. Secondly, the uncertainty of the evaluation data is expressed by unascertained rational numbers, and the corresponding datum construction algorithms are represented. Then, the steps of grey relational evaluation model based on rough set and unascertained rational number are introduced. Finally, experiments are carried out to evaluate the electronic equipment's combat effectiveness by utilizing both methods, i.e. one is determinate parameter method and the other is unascertained rational number method, respectively. The results show that the unascertained rational number method is more considerate to the uncertainty hidden within the data and so its evaluation consequences are more reasonable and reliable. And it can effectively distinguish individual effectiveness differences, which makes it feasible to analyze uncertainty of evaluation consequences.

Considering that poor/limited data about technical risks is obtained in equipment tests, it's not a good idea to use existing methods to identify and deal with new risks. In this paper, a four-step method which combining case based reasoning and grey relational analysis is proposed. By utilizing prior information, a case library, in which every case is described in details representing for certain kind of technical risk, is built. With a new kind of risk symptom rising, the previous library is retrieved to find the case with largest conditional degree, and then a recommendation solution for the new risk symptom is given according to the solution which is taken to solve the risk with largest conditional degree mentioned above. The knowledge comes into being in the process of dealing with the new risk symptom, is combined into a new case, by creating or modifying the existing case. At last, the new case is stored in the case library. A practical example is provided. The results show that the CBR-based grey relational technical risk identification method is sensible and feasible.

Data processing of small samples is an important and valuable research problem in the electronic equipment test. Because it is dificult and complex to determine the probability distribution of small samples, it is dificult to use the traditional probability theory to process the samples and assess the degree of uncertainty. Using the grey relational theory and the norm theory, the grey distance information approach, which is based on the grey distance information quantity of a sample and the average grey distance information quantity of the samples, is proposed in this article. The deffnitions of the grey distance information quantity of a sample and the average grey distance information quantity of the samples, with their characteristics and algorithms, are introduced. The correlative problems, including the algorithm of estimated value, the standard deviation, and the acceptance and rejection criteria of the samples and estimated results, are also proposed. Moreover, the information whitening ratio is introduced to select the weight algorithm and to compare the difierent samples. Several examples are given to demonstrate the application of the proposed approach. The examples show that the proposed approach, which has no demand for the probability distribution of small samples, is feasible and effective.

Quantitative situation analysis and assessment of electronic countermeasure is one of the most important goals of researches on mission programming of electronic countermeasure equipment. But most references focused on total assessment of countermeasure situation, instead of specific analysis. A novel situation analysis method of electronic countermeasure that is based on unascertained rational number was proposed in the paper. The description model of situation data and the specific analysis model were introduced, which was presented by the simulation of the situation analysis of electronic jamming scene. The results show that the proposed method is rational and valid. It can analyze both countermeasure situation of electronic equipment concretely and quantitatively.

From the view of system, the grey characteristics, including structure grey, relation grey, model grey and cognition grey, of the weapon equipment Sos are proposed. So the related theories and techniques of the grey systems are introduced to solve the Sos problems, such as Sos data description, Sos organization optimum, combat efficiency evaluation, Sos contribution ratio and Sos utilization. Finally, the source and the grey model of uncertain information in weapon equipment tests are studied. The uncertainty measurement model of the test datum and the average uncertainty measurement model of the system are introduced. The proposed thoughts can offer theoretic supports for the increasing application of the grey system theory to the domain of the weapon equipment Sos.

The big data development problem of test and evaluation of weapon and equipment is studied. Firstly, the concept of big data and research status in the military domain are summarized. And the concept of big data of test and evaluation of weapon and equipment is given. Secondly, the development demands and status of big data of test and evaluation of weapon and equipment are analyzed. Then, the development contents of big data of test and evaluation of weapon and equipment, such as data collection, big data calculation, data mining, and et al, are introduced. Finally, the key development problems of big data of test and evaluation of weapon and equipment, such as theory system, technique system, operation system, and et al, are proposed emphasisly.

The method which combines objective weights model with subjective weights model is usually used to weigh the factors to overcome the impacts of decision-makers in the multi-attribute group decision making. Due to the incomplete decision-making information, the weights given by each expert are regarded as the uncertain grey value according to the grey system theory and a combined weights algorithm based on grey relational analysis is proposed. According to the experts' comparison judgment matrix, the weight decision matrix is gained, and the positive and the negative ideal reference sequences are constructed. Then the grey relational grade between the positive ideal reference sequence and the weight sequence of each expert and that between the negative ideal reference sequence and the weight sequence of each expert are calculated. Subsequently, the expert weights are acquired through an optimal function that maximizes the subjective decision preference of each expert. And the weights that have subjective and objective information are acquired by integrating the expert weights with the weight decision matrix. Finally, with the help of simulation example, the rationality and feasibility of the proposed method are proved.

Parameter estimation of small samples is a valuable research problem in various research domains. Traditional statistical parameter estimation approach needs to seek the distribution regularity of samples under some assumptions. But the assumptions usually bring new error to the parameter estimation value and make the reliability of the parameter estimation lower. Firstly, from the view of the topology of the sample space and the distances between samples, a new non-statistical parameter estimation approach and a grey parameter estimation approach based on grey theory and norm were proposed. Secondly, the correlative model and algorithms, including the definitions of the grey distance measure and grey relation entropy, were introduced. And the grey parameter estimation approach was compared with traditional statistical parameter estimation approach, too. Finally, the parameter estimation examples by the proposed approach were given. The simulations show that the approach is feasible and effective.