Search

Most of the solutions to existing test selection problems are based on single-objective optimization algorithms and multi-signal models, which maybe lead to some problems such as rough index calculation and large solution set limitations. To solve these problems, a test optimization selection method based on NSGA-3 algorithm and Bayesian network model is proposed. Firstly, the paper describes the improved Bayesian network model, expounds the method of model establishment, and introduces the model's learning ability and processing ability on uncertain information. According to the constraints and objective functions established by the design requirements, NSGA-3 is used to calculate the test optimization selection scheme based on the improved Bayesian network model. Taking a certain component of the missile airborne radar as an example, the fault detection rate and isolation rate are selected as constraints, and the false alarm rate, misdiagnosis rate, test cost, and test quantity are the optimization goals. The method of this paper is used for test optimization selection. It has been verified that this method can effectively solve the problem of multi-objective test selection, and has guiding significance for testability design.

Aiming at the problem that it is difficult to directly test the intercepted distance between radar and specific ESM reconnaissance equipment, the equivalent test method of radar intercepted distance by ESM reconnaissance equipment is studied. Firstly, the equivalent model of intercepted distance under the expected test condition and the substitute test condition is deduced. Then, according to different test scenarios, the equivalent model is subdivided into four different types. Finally, the equivalent model is verified by the measured data. The data is from the intercepted distance of a certain radar in the outfield flight test. The test results show that the error of the equivalent model is 7.26%, which meets the requirement that the error of the test outline is less than 10%.

In view of real-time demand for speed compensation on high speed moving platform, an implementation method of missile speed compensation is proposed. After the idea of range cell migration compensation applied to the missile speed compensation, the signal-to-noise ratio of coherent accumulation is improved, and hardware design is realized on FPGA. The simulation experiment shows that the missile speed compensation method with FPGA has advantages of high real-time, fast processing speed and high precision.

Due to the multipath effect, the direction of arrival (DOA) estimation performance for low-angle targets decreases greatly. To overcome this problem, this paper proposes a spatial differencing reconstruction based DOA estimation algorithm by using the received signal model of multiple input multiple output (MIMO) radar. Combining with the spatial diversity of MIMO radar, the proposed method can first use the multipath echo power to select the best signal. Then, a spatial differencing based iterative scheme is developed to reduce the effect of additive noise, resulting in a better estimation performance for low-angle targets. Simulation results show that the proposed method has better advantages in suppressing noise and improving estimation accuracy.