Research On Key Technologies Of Shipborne Satellite Antenna Micro Attitude Measurement System

MEMS inertial sensors are characterized by low cost,small size,low powerand fine shockproof capability.With the continual advancements of the elementperformance,the application domains of MEMS inertial sensors keep enlarging,however,up to now,MEMS gyros couldn't meet the practical needs of shipborneinertial navigation equipment due to low precision.The paper relies on the goingproject assumed by the laboratory,makes a study on shipborne satellite antennamicro attitude measurement system based on MEMS inertial sensors to get alow-cost shipborne antenna stabilized system,and form a basis simultaneously forfurther applications of MEMS inertial sensors in shipborne inertial equipment.In view of the performance characteristics of MEMS inertial sensors and theprecision requirements of attitude measurement system,the paper puts emphasison the study of several key techniques,i.e.,the whole design of the attitudemeasurement system,MEMS gyro signal processing,updating algorithm forsystemic strapdown matrix etc.Aiming at the disadvantage that errors of MEMS gyro attitude systemaccumulate quite fast,the measurement system comprising accelerometers andmagnetometers is combined with the gyro system to form an integrated attitudemeasurement system,thereby ensuring both a steady long-term precision and abetter dynamic performance.The installation scheme is presented that micromeasurement system is fixedly connected with the antenna base,to enhance thecalibration precision of magnetometers,and simultaneously ensure the systemuninterrupted when the antenna tracking signal is lost.The formula of angletransformation from attitude system to stabilized platform is derived for thepresented scheme which is demonstrated feasible.Since the large MEMS gyro random errors have a great effect on thesystemic dynamic precision,the identification technique for MEMS gyro randomerrors is studied based on the characteristic analysis of MEMS gyro error components.When identifying error coefficients by the use of Allan varianceanalysis results of gyro signal,due to the weakness lying in the common usedillustration method and least square fitting method in practical applications,theexperimental means of frequency-division collection of gyro data and segmentedfitting of Allan variance results are put forward to implement a betteridentification of the gyro random error parameters.Taking working requirements of attitude measurement system into account,the means to denoise MEMS gyro output signal are studied based on waveletthreshold shrinking,and the key factors for denoising is further analyzed.For theshortages of universal threshold criterion,conventional soft threshold and hardthreshold,an adaptive hyperbola threshold based wavelet denoising approach ispresented to denoise the MEMS gyro signal according to the characteristics ofMEMS gyro output noise.Simulation experiment has been performed to provethe effectiveness of the presented approach in gyro signal denoising incomparison with the conventional wavelet threshold denoising means.The Kalman filter function is constructed with MEMS gyro measurementinformation as the state vector and measurements from accelerometers andmagnetometers as the observation vector.Since both of the fundamental systemequations derived based on two distinct quaternion error models are nonlinearequations,the design scheme is brought forward by adoptingpseudo-measurement vector to model the antenna attitude measurement system.The system equation with double pseudo-measurement vectors,as well as thesystem state covariance matrix and the observation covariance matrix under thecircumstance of state dependency are firstly derived.Then the rationalimplementation of the quaternion normalization is analized,and the Kalman filterequation is finally derived in detail.The simulation shows that the algorithmmakes a pretty good information integration of gyros,accelerometers andmagnetometers,and is immune to large initial alignment error,thereby applicableto the designed system in this paper.The study of adaptive attitude algorithm for the varying systemic state and observation noise characters is developed.According to the small change inamplitude and strong randomness for the MEMS gyro error model,the systemicadaptive Kalman filter equation is derived by taking the minimization of theresidual covariance model error of Kalman filter as the adaptive target function,and the efficiency of the adaptive algorithm to overcome the bad effect of thevarying MEMS gyro error models on the system is demonstrated via simulatedexperiments.For the large amplitude of the accelerometer measurement errorcaused by ship maneuvering motion,a Radial Basis Function neural network isdesigned to learn the attitude algorithm,and an integrated attitude measurementsystem based on neural network and Kalman filter is put forward according to theforeseeable disturbance acceleration,thereby overcoming the bad effect of theship maneuvering motion on the system.