Transfer alignment

Abstract: Inertial Navigation Sysreni (INS) concepts offer attractive benefits when applied to modern air-to-gro~~nd weapons because of countermeasures invulnerability, rapidly reducing size and cost and the ability to reduce the performance demands of terminal g~~ida i ice s ekers. Traditional INS transfer alignment methods place both time and maneuver constraints on tactical l a~mch timelines which detract from INS ~ ~ t i l i t y in tactical scenarios. A rapid transfer alignment approach is presented uliich allows alignment in less than ten seconds to an accuracy of less than one nirad w i t h o ~ ~ t the req~iirement for aircraft lateral plane maneuvers. The alignment approach augments the traditional nlasterislave velocity concept with masterlslave a t t i t ~ ~ d e matching wliich allows alignment error observability with rotational maneuvers. The resulting 24-state, 6-measurement Kalman filter is shown to be implementable using commercially available microprocessors with achieved filter update rates of 36 Hz.

Abstract: Formulations of Kalman filters are presented which are capable of aligning one strapdown inertial sensor assembly with another by estimating the misalignment angle between them. One formulation treats the case of a fixed misalignment. Another treats the case of a dynamic misalignment, caused, say, by bending of the common supporting body. Measurements can be made by gyros only, or by gyros plus accelerometers. Filters which estimate inertial sensor error parameters are also discussed.

Abstract: This paper presents the results of an effort directed at developing and flight-testing an innovative rapid transfer alignment algorithm for inertially-guided air-launched munitions. The algorithm, referred to as RAP (Rapid Alignment Prototype), employs a 17-state Kalman filter designed to accurately align a weapon-grade Inertial Measurement Unit (IMU) relative to an aircraft-grade Inertial Navigation System (INS) within five seconds. The alignment procedure requires the pilot to execute only a brief wing-rock maneuver. No time-consuming heading changes or lengthy s-turns are required. The RAP Kalman filter achieves the rapid convergence time by recursively processing both velocity-match and attitude-match measurements at a 12.5 Hz rate to estimate and correct IMU velocity, attitude, and inertial sensor errors. Following laboratory and van testing at Eglin AFB, a series of F-16 flight tests were conducted. Flight test results demonstrated that the RAP filter achieved sub-milliradian alignment accuracy in less than 10 seconds. As further confirmation of alignment accuracy, IMU position error statistics were computed over a 100-sec post-alignment captive-carry trajectory. Test results indicated that the mean radial position error after 100-sec of unaided navigation was roughly 70 ft with an associated CEP of 61 ft. RAP's unprecedented alignment accuracy and reduced launch timeline provide a rapid-response capability for time-critical targets such as mobile launchers and troop emplacements.

Abstract: This paper presents an online approach for estimating the dynamic flexure model parameters in shipboard transfer alignment (TA). Traditionally, the application of Kalman filters (KFs) to the TA process is often restricted because of the lack of real-time information on dynamic flexure characteristics, and a KF designed on the basis of inaccurate parameters of the dynamic flexure model will result in a large alignment error. To overcome this difficulty, a parameter estimation algorithm is proposed in this paper, which utilizes the angular increment difference measured by the master inertial navigation system (MINS) and the slave inertial navigation system. Specifically, the Tufts-Kumaresan method is introduced to compute the unknown parameters of the dynamic flexure model from the angular increment correlation function. Our simulation results show that the proposed method can estimate the dynamic flexure parameters with a high degree of accuracy, even in low signal-to-noise ratio conditions. This parameter estimation method does not require a priori knowledge of dynamic flexure characteristics and, therefore, provides the shipboard sensors with an accurate and rapid-response capability for alignment with the MINS.