Strapdown navigation technology - A literature survey

Garg, Subhash; Morrow, L. D.; Mamen, R.

doi:10.2514/6.1977-1105

Cited by 3 publications

(6 citation statements)

References 85 publications

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“…With these performance data, which are two orders of magnitude better than in the present experimental model, the limiting factor, at present, is the variation of the leakage current in the integrator switches. This variation is in the order of 50 pA, and with typical values of the integrator capacitors the equivalent null point uncertainty at u 5 ,u 6 is expected to be about 0.5 /*V. This is almost two orders of magnitude better than the 20 /*V uncertainty of the experimental processor. Consequently, the angular drift rate Q id is expected to be in the order of 0.01 deg/h even without special provisions for temperature stabilization.…”

Section: Performance Considerations and Experimental Resultsmentioning

confidence: 92%

“…The amplifiers have voltage drift coefficients of about 1 />tK/°C and input leakage currents of about 50 nA. Without special provisions of temperature control, the total null point stability at room temperature was about 20 /*V. For a maximum output of 6 V at u 5 ,u 6 , and for a maximum angular rate Q,=200 deg/s, this corresponds to an angular drift rate of -0.2 deg/h. This already is in the medium class IMU quality.…”

Section: Performance Considerations and Experimental Resultsmentioning

confidence: 98%

“…The pulse sequences V 4 ,V 4 \V 5 ,V 5 are such that immediately after the sampling pulse V 4 , V 4 the corresponding integrator is reset, and the next sampling pulse operates on the alternative integrator. u s ,u 6 are consequently proportional to Ff and Of, respectively.…”

Section: Realization Of the Signal Separation Processormentioning

confidence: 98%

“…Alternative approaches have either involved departures from classical sensor realizations based on mechanical gyroscopes, 1 ' 3 or novel concepts of inertial measurement unit (IMU) mechanizations. 4 ' 5 The principal trend that has emerged from these efforts is the strapdown concept, 6 with its greatly simplified mechanization but higher demands on the gyroscopes. 7 In consequence, the challenge for substantial cost reduction still persists.…”

Section: Introductionmentioning

confidence: 99%

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A nongyroscopic inertial measurement unit

Merhav

1982

Journal of Guidance, Control, and Dynamics

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This paper describes a concept of a low-cost gyroless inertial measurement unit. By means of an orthogonal triad of rotating or vibrating accelerometers, angular rates are measured along with specific forces. The precise separation of the two signals from the accelerometer readings is described. Apart from its simplicity, the special features of the separation method are that signal bandwidth is not sacrified, inertial angular rate measurements are essentially drift-free, and dynamical errors due to angular rate and specific force derivatives are reduced to negligible values. A detailed statistical analysis of the error sources, due to dynamical effects, sampling, nonlinearities, and vibration is given. The residual angular rate offset is due to the electronic processor which, with modern CMOS components, is shown to provide a performance level in the 1 n.mi./h class. The possibility of dispensing with the costly gyroscopes, which are essential in conventional designs, indicates a substantial saving in cost. Nomenclaturea = vector of accelerometer outputs a = bandwidth of random process b = index of body axes c = index of coherent terms F = vector of specific forces applied to vehicle g = gravity vector h (t) = impulse response / = index of coordinates x,y, z I = distance vector m = ratio of frequencies n = vector of measurement noise o = index of orthogonal terms p, q, r -components of angular rate R E -radius of Earth R = position error / = time T = period of cycle u = normalized distance function V = vibration amplitude X, Y, Z = rectangular coordinate system a = bandwidth normalized to Schuler frequency co 5 f = normalized time f=co 5 / \L = beat frequency v = vibration frequency T = time difference p = radius of rotation or amplitude of vibration a = standard deviation (•) = autocorrelation function $ (•)= power density spectrum $* (*) = power density spectrum of sampled signal co = frequency of revolution co 5 = Schuler frequency ft = angular rate vector of body axis system Q,.= component of angular rate error

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Section: Performance Considerations and Experimental Resultsmentioning

confidence: 92%

Section: Performance Considerations and Experimental Resultsmentioning

confidence: 98%

Section: Realization Of the Signal Separation Processormentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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A nongyroscopic inertial measurement unit

Merhav

1982

Journal of Guidance, Control, and Dynamics

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“…Inertia measurement systems (IMS), in strapdown platforms [2], are substantially based on acceleration and angular velocity measurements. In contrast with GPS, the IMS are characterized by fine accuracy only within a short measurement time.…”

Section: Introductionmentioning

confidence: 99%

Direct tuning of inertia sensors of a navigation system using the neural network approach

Salvini

2009

Inverse Problems in Science and Engineering

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In the present article, the inverse problem concerning the identification of suitable sensitivities for inertia measurement sensors, part of an inertial navigation system, is discussed. For a precise tuning of the navigation system, it is assumed that the sensitivities could modify their effective values as the consequence of instantaneous movements and environmental conditions. The goal is to set up a tool that is able to foresee any possible sensitivity scattering. The key point is to find a strategy to teach a neural network (NN) to be able to correct measurements when auxiliary global positioning systems (GPS) are not properly working. In this article the strategy is presented and discussed and several tests show some examples in which the procedure was successful or not. However, for all unsuccessful cases, it is possible to recognize the malfunctioning prior to complete the identification; therefore, data to discard in the NN training are easily identified. The final NN application is not discussed within this article because it is non-essential for the present aim that is the definition of optimal NN output parameters.

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Optimal Methods of RTK-GPS/Accelerometer Integration to Monitor the Displacement of Structures

Hwang

Yun

Park

et al. 2012

Sensors

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The accurate measurement of diverse displacements of structures is an important index for the evaluation of a structure’s safety. In this study, a comparative analysis was conducted to determine the integrated RTK-GPS/accelerometer method that can provide the most precise structure displacement measurements. For this purpose, three methods of calculating the dynamic displacements from the acceleration data were comparatively analyzed. In addition, two methods of determining dynamic, static, and quasi-static displacements by integrating the displacements measured from the RTK-GPS system and the accelerometer were also comparatively analyzed. To ensure precise comparison results, a cantilever beam was manufactured onto which diverse types of displacements were generated to evaluate the measurement accuracy by method. Linear variable differential transformer (LVDT) measurements were used as references for the evaluation to ensure accuracy. The study results showed that the most suitable method of measuring the dynamic displacement with the accelerometer was to calculate the displacement by filtering and double-integrating the acceleration data using the FIR band-pass filter. The integration method that uses frequency-based displacement extraction was most appropriate for the integrated RTK-GPS/accelerometer method of comprehensively measuring the dynamic, static, and quasi-static displacements.

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Strapdown navigation technology - A literature survey

Cited by 3 publications

References 85 publications

A nongyroscopic inertial measurement unit

A nongyroscopic inertial measurement unit

Direct tuning of inertia sensors of a navigation system using the neural network approach

Optimal Methods of RTK-GPS/Accelerometer Integration to Monitor the Displacement of Structures

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