Solution System Of Simulated Ballistic Trajectory Based On Machine Vision

The electromagnetic vector sensor array signal processing is a new emerging subdiscipline in array signal processing domain. In recent years, DOA and polarization parameters estimation of signals using array of electromagnetic (EM) vector sensors(VS's) has become a hotspot, which is widely used in radar, sonar and communication etc.The research on DOA and polarization parameters simultaneous estimation using an array of electromagnetic vector sensor is of great significance in practical application and academy. People have used traditional scalar sensor array on DOA estimation for a long time, and have massive academic achievements. Through this system we cannot obtain the integrated electric and magnetic fields information but only the information on one field component. The array element output only reflected the receive signal intensity and the absolute phase. The lack of information will eventually affects the algorithm performance. However, EMVS can provide complete electric and magnetic fields, which is composed of three orthogonal electric dipoles and three corresponding orthogonal magnetic dipoles. Not only it can use its array geometry structure to acquire the spatial information of the signal, but also can acquire all polarization information. So it has higher information acquire ability than the common arrays due to the additional polarization information. Compared with traditional scalar sensors , electromagnetic vector sensor array has a better performance: steady detective ability, stronger anti-jamming ability as well as higher space resolution.At present the mass of method used in electromagnetic vector sensor array signal processing domain is based on supposition of the white Gaussian noise and the steady signal. But in practical environment, the white Gaussian noise supposition may not always be true, and many signals exited are not steady. For example, many man-made signals ,such as BPSK, FSK, AM signals, exhibitin the cyclostationarity, and LFM signals used in radar applications. As a result, the performance of subspace-based DOA and polarization estimation techniques may degrade when dealing with non-stationary signals.The electromagnetic vector sensor array has rich redundant information. Using this characteristic and with the concrete characteristic of the signal, we research the estimations of DOA and polarization in colored noise.All of the existing PSA signal processing algorithms including the methods proposed in this paper assumes that each antenna (dipole or loop) of the electromagnetic vector sensor orients the referenced Cartesian coordinate strictly, i.e., the vector sensor is free of orientation errors. But in real application, the aboveapproximation is not always accurate. Affected by all kinds of factors, the unknown orientation errors are inevitably exist, which makes the manifold of the vector sensor deviate from the ideal value, and depresses the estimation performance of the existing methods greatly. So it is necessary to estimate and calibrate the orientation errors of the vector sensor. In the sixth chapter, we discuss the estimation and calibration problem of the PSA orientation errors, and derive corresponding expressions for the stochastic Cramér-Rao bound.