84 results about "Linearization" patented technology

A recursive RISE control method and system for maglev train levitation systems is disclosed. The control method includes: constructing the dynamic equations and voltage balance equations of the electromagnets based on a single-point levitation system, and then constructing a dynamic model of the single-point levitation system accordingly; designing a virtual control law, and then designing a recursive RISE controller based on the backstepping method by combining the dynamic model of the single-point levitation system and the virtual control law; performing stability analysis on the RISE controller to obtain its stability conditions; and, based on satisfying the stability conditions, using the RISE controller to perform real-time control of the levitation gap of the maglev train levitation system. This invention has excellent anti-interference capabilities, reducing the impact of external interference or model uncertainty on levitation accuracy. Furthermore, by handling the nonlinear relationship between voltage and current through the backstepping method, this invention avoids the model mismatch problem caused by linearization.

The application discloses a multi-power unit high-speed train control method, and relates to the technical field of train control.The method comprises the following steps: performing stress analysis on a multi-power unit high-speed train to determine a high-speed train dynamics model; determining a discrete-time nonlinear model based on the high-speed train dynamics model; constructing control force and control force change rate constraints; converting the discrete-time nonlinear model to obtain a dynamic linearization data model; designing a parameter estimation algorithm and an adaptive extended state observer; designing a discrete-time terminal sliding mode function and a hyperbolic curve type reaching law; determining a data-driven terminal sliding mode control scheme based on the dynamic linearization data model, the parameter estimation algorithm, the adaptive extended state observer, the discrete-time terminal sliding mode function and the hyperbolic curve type reaching law; and performing tracking control operation on the multi-power unit high-speed train by using the data-driven terminal sliding mode control scheme.The application improves the control precision of high-speed train operation while reducing the sliding mode chattering.

This invention belongs to the field of linear periodic time-varying system modeling and analysis technology, and discloses a small-signal modeling method and application for LCC-HVDC systems. The method includes: constructing mathematical models of the segmented and continuous parts of the LCC-HVDC system based on its circuit topology within each operating interval, and integrating these models as the mathematical model of the LCC-HVDC system; linearizing the trajectory of this mathematical model to obtain its small-signal model within each operating interval; designing a small-signal model of the LCC-HVDC system during switching between adjacent operating intervals using the Filippov method; and combining the small-signal models of the LCC-HVDC system within each operating interval and the small-signal models at the switching time to constitute the small-signal model of the LCC-HVDC system, thus completing the small-signal modeling. This invention can accurately characterize the switching dynamics of the LCC-HVDC system, achieving accurate and low-complexity small-signal modeling of the LCC-HVDC system.

The present application relates to the field of power system stability analysis and control, in particular to a kind of thermal mass energy storage converter broadband oscillation suppression parameter stable domain identification method, comprising the following steps: establish general electric-thermal coupling mechanism model, construct the dynamic differential equation describing the evolution of input electric power and energy storage medium temperature state of converter;Mechanical dynamics equation is constructed;Establish the small signal state space model of whole system, linearization is carried out at steady state operating point and the state space matrix of whole system is derived;Define practical small disturbance stability domain, set the real part decay rate threshold and minimum damping ratio threshold of whole system eigenvalue;Algebraic singularity constraint equation in high-dimensional parameter space is constructed;Solve algebraic singularity constraint equation, establish parameter stable domain.This method avoids the repeated iteration of traditional eigenvalue analysis, can accurately depict parameter boundary, effectively improve the frequency support capability of high proportion of new energy power grid and system robustness.

This invention discloses an AI-enhanced extended Kalman filter method and system, belonging to the field of Kalman filter technology. The method constructs an extended state including velocity and measurement bias, and performs prediction and linearization based on a dynamic model. The forward innovation and its variance are calculated using the minimum process noise of the fixed bias channel, and used for feature normalization. A sliding window feature sequence is constructed using the normalized innovation and velocity increment, and input into a one-dimensional convolutional neural network for multiple random deactivation inferences to obtain anomaly intensity scores and cognitive uncertainty. Based on this result, the process noise of the bias channel is adaptively adjusted to enhance bias tracking capability when anomalies are significant, and maintain estimation smoothness when normal conditions are normal. Finally, the extended Kalman filter update is completed, achieving robust joint estimation of velocity and bias. This invention improves the estimation stability and accuracy under strong noise and transonic conditions, and is applicable to velocity measurement of aircraft and ultra-high-speed motors.

The present application belongs to the field of integrated watershed water and landscape base planning, and relates to a watershed water and landscape base storage allocation gain evaluation method. First, considering the investment and operation cost of energy storage and new energy, the annual power generation yield and the influence of discount rate, the base storage allocation gain index is proposed. Second, a watershed water and landscape storage integrated planning model embedded with 8760h complementary operation simulation is established. The general storage constraint is designed, the technical characteristics of different types of storage are represented by parameter differentiation, and the high-efficiency solution of the planning model is realized by convex linearization technology. The watershed new energy access capacity and 8760h operation simulation process before and after the configuration of different types of storage are calculated. The gain effect of different storage allocation schemes is quantitatively compared by the proposed storage allocation gain evaluation index to determine the optimal storage allocation scheme. The present application can effectively reduce the solution scale of the model while ensuring the accuracy and improving the solution speed through the nested dimension reduction and convex linearization modeling technology.

The application discloses a Pick iterative random characteristic flow field deduction and reconstruction method and system for bridge wind engineering and belongs to the technical field of bridge wind engineering and structural health monitoring. The application divides a global space-time calculation domain, constructs local affine mapping and a space-time random characteristic base function, adopts Pick iterative linearization incompressible Navier-Stokes equations, avoids Jacobian matrix calculation, assembles a global over-determined linear equation group containing momentum, mass conservation, boundary and interface constraints, forms a double-enhanced linear system through algebraic row balance and physical enhancement layered weighting, realizes high-fidelity reconstruction of a flow field through least square solution, supports unknown fluid parameter alternating inversion under sparse data, and can automatically generate bridge wind-induced vibration early warning. The application eliminates edge device memory bottleneck, improves solution robustness and precision, strictly guarantees physical conservation, and is suitable for real-time deduction of bridge complex flow fields, sparse observation reconstruction and safety monitoring.

This invention provides a control method for a lower limb exoskeleton rehabilitation robot based on the sliding mode optimization method, belonging to the field of medical rehabilitation robot control technology. The method includes: establishing a two-degree-of-freedom dynamic model of the lower limb exoskeleton rehabilitation robot, and transforming it into a linear state-space model using feedback linearization technology; designing a linear quadratic regulator (LQR) for the linearized model to obtain the optimal feedback gain matrix; constructing an integral sliding surface containing the optimal feedback gain matrix, so that the equivalent control law of the system in the sliding mode possesses the globally optimal dynamic performance of the LQR; and based on adaptive preset time theory, constructing a sliding mode reaching law and an adaptive gain update law containing novel nonlinear functions to generate the final control torque.

The present application relates to a kind of well gas invasion and loss parameter inversion methods based on unscented Kalman filter, belong to petroleum engineering technical field, comprising: step 1: establish overflow wellbore multiphase flow model;Step 2: establish inversion model;Step 3: solve inversion model;Calculate the inversion result of downhole gas invasion and loss parameter.The application method combines wellbore multiphase flow equation with state space model, uses the nonlinear estimation characteristics of UKF algorithm, carries out dynamic filtering and state updating to wellhead measurable data, realizes the real-time inversion of downhole gas volume fraction, gas invasion flow and loss amount.UKF method can accurately capture the nonlinear characteristics of system without linearization, thereby improving the inversion accuracy and calculation stability under complex conditions.Through example verification, this method has good applicability under single reservoir, multiple reservoir and overflow and leakage coexistence conditions, provides reliable boundary conditions and intelligent control basis for well control system.

The application discloses a method for intercepting a maneuvering target based on linear quadratic differential game proportional guidance, which comprises the following steps: obtaining a meeting state quantity and constructing a locally linearized relative lateral dynamic equation rate based on the two-dimensional relative motion relationship between a pursuit side and a target; describing the locally linearized relative lateral dynamic equation as a finite time domain zero-sum linear quadratic differential game, constructing a lateral deviation and lateral velocity approximate state vector according to the meeting state quantity, and solving an optimal lateral control quantity of the pursuit side; corresponding the optimal lateral control quantity of the pursuit side with a proportional guidance law, calculating an instruction angular velocity of the pursuit side, and realizing online interception of the target. The application can realize real-time adjustment of the heading angular velocity of a fixed-wing unmanned aerial vehicle, and improve the success rate of online interception of a target rotor-wing unmanned aerial vehicle. The application can be widely applied to the technical field of maneuvering target interception.

This application belongs to the field of unmanned surface vessel (USV) control technology, specifically relating to a method and apparatus for predictive control of USV trajectory tracking using dynamic reference linearization. The method includes: establishing and discretizing a nonlinear dynamic model of the USV to construct a predictive model; constructing an online nonlinear optimization problem including terminal stability components based on this model; generating a dynamic reference trajectory in each control cycle based on the optimal solution of the previous moment, and linearizing the nonlinear model point-by-point along this trajectory to obtain a high-fidelity linear time-varying predictive model; further transforming the original nonlinear programming problem into an efficiently solvable quadratic programming problem; and outputting control quantities in real time by solving this problem and integrating reference commands. This application ensures the theoretical stability of the closed-loop system, achieves a synergy of high precision, high real-time performance, and high reliability, and effectively improves the trajectory tracking performance of USVs in complex marine missions.

A source-grid-load-storage flexibility multi-time scale optimization scheduling method considering conditional value at risk, comprising: constructing a power system supply-demand flexibility quantification model, including: a power system flexibility demand quantification model, a power system flexibility supply quantification model, and a power system flexibility supply-demand balance model; constructing a multi-time scale optimization scheduling model, including: a day-ahead optimization scheduling model, an intra-day optimization scheduling model, and a real-time optimization scheduling model; linearizing the CVaR nonlinear model; solving the linearized model, and performing simulation calculation under different strategies and different scenarios. The scheduling method has important theoretical value and engineering practical significance for accurately quantifying and controlling system operation risk, fully tapping the potential of system flexibility, and improving the economy, safety and operation efficiency of high-proportion new energy power systems.

This specification relates to the field of oil and gas exploration and development technology, and in particular to a method, apparatus, and equipment for optimizing crude oil production scheduling in a proportional manner. It includes: acquiring sample data of crude oil production scheduling ratios and constructing a production scheduling optimization equation based on a pre-set refinery production scheduling optimization plan; setting linear expansion points for nonlinear constraint terms in the production scheduling optimization equations and constructing a linearized residual equation; then obtaining the iterative values ​​corresponding to the linear expansion points; transforming and linearizing the nonlinear constraint terms in the production scheduling optimization equations to obtain a production scheduling optimization linear equation and constructing a linear programming model; based on the iterative values ​​corresponding to the linear expansion points and the sample data, iteratively training and solving the linear programming model to obtain the target production output; updating the iterative values ​​of the linear expansion points; and iteratively training the linear programming model using a backpropagation algorithm to obtain the production scheduling optimization method. This can improve the efficiency and accuracy of solving the crude oil production scheduling optimization method in a proportional manner.

The invention relates to a method for determining a volume flow (ΔV / Δt) of a filling material (2) in a container (3) by means of a filling level measuring device (1), comprising the following method steps: emitting high-frequency signals (SHF) towards the filling material surface and receiving corresponding received signals (RHF); determining filling level values (Li- n , Li) and / or a filling level change over time (ΔL / Δt) on the basis of the received signals (RHF); ascertaining the volume flow (ΔV / Δt) on the basis of a known linearisation model and the filling level change over time (ΔL / Δt), or on the basis of a volume difference (ΔV) which results from the linearisation model and the filling level values (Li-n, Li) and a corresponding time difference (Δt). The invention is therefore based on the knowledge that a volume flow (ΔV / Δt) or flow rate can also be determined on the basis of filling level measurement, provided that the linearisation model is available for the corresponding container. The method according to the invention advantageously makes it possible to dispense with separate flow rate measuring devices at the inlet (31) and at the outlet (32) of the container (2) in processes which require a filling level measurement per se.

The application relates to a micro-grid energy storage optimal scheduling method based on absolute value linearization, and belongs to the technical field of energy management and automatic control, and comprises the following steps: constructing a micro-grid system mixed integer nonlinear programming model containing renewable energy, load, energy storage and grid interaction; analyzing how a multi-objective cooperative optimization problem such as battery life protection and power smoothing is converted into a weighted total cost minimization problem with a penalty term; adopting an absolute value linearization modeling method to convert the nonlinear programming problem into a mixed integer linear programming problem; and adopting a branch and bound algorithm to solve the converted linear programming model, so that a globally optimal energy storage charging and discharging and grid interaction scheduling plan is obtained.

The application discloses a spacecraft magnetic moment attitude control method based on a non-inverse iteration algorithm, and the method establishes a linear periodic model for a spacecraft attitude control system which only relies on a magnetic moment; on the basis, according to a linear quadratic optimal control theory, an optimal controller design problem is converted into a solution problem of a discrete periodic Riccati equation associated with the optimal controller design problem; on the basis of a gradient descent principle, an error function is constructed, and a non-inverse iteration algorithm based on the gradient descent principle is proposed to solve the discrete periodic Riccati equation, so that a matrix inversion operation in a traditional solution method is avoided, and the calculation efficiency and the numerical stability are effectively improved; the obtained Riccati equation solution is used to obtain an optimal state feedback control law, so that the spacecraft attitude optimal control only using the magnetic moment is realized, and a new solution scheme is provided for an attitude control problem of a limited actuator in a space task.

This invention discloses a packet loss detection method for unmanned surface vessel (USV) formations based on adaptive fuzzy membership filtering. The method includes: establishing state equations describing the motion of both the leader and follower USVs; linearizing these nonlinear equations to simplify the complex curvilinear motion relationships into linear mathematical expressions, thus obtaining simplified state equations for the entire formation system; introducing an adjustable measurement noise figure based on the simplified formation model, dynamically adjusting the filtering intensity according to the current measurement noise level, thereby more accurately estimating the real-time state of the USVs; and simultaneously detecting packet loss during state updates, combining preset communication quality indicators and real-time calculated noise levels to determine the reliability of received data. This invention achieves good detection results for packet loss in noisy channels and can be widely applied in the field of data detection.

This invention provides a method, device, electronic device, and storage medium for error compensation in a dual-drive feed machine tool. The method includes: designing orthogonal experiments based on the three cutting elements to generate multiple sets of machining conditions; constructing a grating ruler calibration model using a laser tracker, synchronously acquiring multi-axis command trajectories and actual trajectories, calculating the actual positions of the dual-drive axes, and calculating three types of errors; constructing a multivariate nonlinear regression model of the three cutting elements and the three types of errors, solving the model coefficients using the least squares method after logarithmic linearization, and optimizing to a preset accuracy by introducing interaction terms between the three cutting elements; substituting the new three cutting elements into the optimized model to calculate the predicted values ​​of the three types of errors; performing independent position compensation for each axis based on the predicted independent following error values, and then correcting the synchronization deviation based on the predicted dual-drive synchronization error values ​​to generate a collaborative compensation trajectory. This invention improves the accuracy of error calculation and the adaptability to working conditions.

This invention discloses an advanced positioning algorithm that, by constructing a statically indeterminate linear equation system, can efficiently integrate data from various stations in a large-scale distributed positioning system and accurately determine the target location in a single calculation. Its innovation lies in utilizing the transformation relationship between polar and rectangular coordinate systems to directly construct a boundary value equation containing mixed variables based on the path difference equation. Through appropriate equation transformation and variable substitution, the algorithm achieves linearization of the boundary value equation, significantly simplifying the calculation process. Furthermore, addressing the decisive influence of the path difference construction method or matrix construction strategy on positioning accuracy, this invention proposes unique path difference construction rules or coefficient matrix construction methods. These rules and methods ensure highly accurate positioning results in practical applications. The new algorithm is not only simple but also significantly improves positioning efficiency, bringing a revolutionary change to large-scale distributed positioning.

This invention proposes a trajectory planning method for deformable aircraft in discrete deformable mode, belonging to the field of aircraft guidance and control technology. Based on dynamic equations, this method establishes a trajectory planning model for the deformable aircraft in discrete deformable mode, which is a mixed-integer optimal control problem. This mixed-integer optimal control problem is transformed into multiple continuous optimal control problems within the continuous range [0,1]. These problems are then linearized and discretized near a reference trajectory starting from the current position of the deformable aircraft, resulting in a convex discretized trajectory planning model. The discretized trajectory planning model is solved; if the solution converges, the solution is converted into a solution to the mixed-integer optimal control problem to obtain the trajectory planning result of the deformable aircraft. This invention can improve the efficiency and accuracy of trajectory planning for deformable aircraft in discrete deformable mode and has high application value.

The application discloses a hyperbolic linearization identification method based on a lower opening scanning. The application converts a hyperbola into an equidistant model, converts whether a target point cluster is a hyperbola into whether the point cluster presents an equidistant characteristic, reduces a to-be-estimated parameter of the hyperbola to one, and meanwhile, retains all information of the original hyperbola shape, and has better accuracy compared with a traditional least square method.

This invention belongs to the field of power transmission and distribution systems, and discloses a method and system for generating nodal baseline load response under transmission and distribution coordination. It integrates the nodal marginal electricity price into a pre-constructed two-layer Steinberg game model, iteratively updates the incentive unit price to obtain the optimal value, and linearizes the exponential and product nonlinear terms in the demand response incentive service quantity of the distribution system operator. Finally, based on the linearization result and the optimal unit price, it solves the game equilibrium to generate the nodal baseline load response. The nodal marginal electricity price can characterize the differences in the geographical distribution of nodes, thus transforming a single incentive signal into a differentiated and precise incentive signal. Simultaneously, the linearization process eliminates the limitations of nonlinear terms on commonly used optimizers through mathematical transformation. This enables the demand response mechanism to adapt to the actual operating needs of the power grid under high renewable energy penetration, improves the accuracy of incentives and the feasibility of optimization, effectively enhances the overall effect of demand response, and promotes its practical application.

The application belongs to the field of orbit estimation, and relates to a method for robust orbit estimation of space targets under complex observation environment. The application reconfigures the traditional nonlinear observation equation into the cross product form of the line-of-sight vector, realizes the linearization of the measurement model, and makes the orbit estimation problem effectively solved by minimizing the cross product residual. An optimization strategy of minimizing the sum of absolute values of residuals is adopted, a relaxation variable is introduced to convert the orbit estimation problem into a typical linear programming problem. By solving the linear programming problem, the bias solution of the initial orbit state of the space target is obtained, the abnormal data is automatically rejected, and the influence of the abnormal observation data on the orbit estimation result is greatly inhibited. Compared with the traditional least square (LS) method, the application can provide more accurate, stable and robust orbit estimation results when dealing with large initial errors and abnormal data, has stronger adaptability, and is especially suitable for orbit estimation under complex space environment.

The application discloses a kind of shared energy storage optimization scheduling method considering default penalty, and specifically relates to energy storage scheduling and energy management field, first, the multi-service transaction framework between energy storage operator and new energy station, power grid is built;Then the capacity lease default, energy transaction default and over-selling default behavior that energy storage operator can occur are quantitatively modeled, and are included in optimization target;Further, the scheduling model with the maximization of operator's income as the goal is established, the opportunity constraint of energy storage state of charge is introduced to balance economy and safety;Finally, the model is converted into mixed integer linear programming problem by Monte Carlo sampling and linearization technique to solve;The application can effectively guide energy storage operator to make optimal scheduling decision between default risk and economic benefit, improve the overall operation efficiency and economy of shared energy storage system.

The application relates to the technical field of engineering surveying, in particular to a prism rod inclination measuring method, which comprises the following steps: S1, fixing the bottom of a prism rod at a point to be measured, tilting the prism rod multiple times, and obtaining the coordinates of the observation points after each tilting; S2, constructing an original observation equation set and calculating an average equation according to the coordinates of the observation points obtained each time, taking the average equation as a reference, linearly processing each original observation equation, and obtaining a linear observation equation set; S3, solving the initial coordinate estimation value of the point to be measured based on the linear observation equation set through an equal-weight least square method; S4, calculating the deviation absolute values of the elevation values of the observation points and the initial elevation estimation value of the point to be measured, and completing the weight distribution of the coordinates of the observation points according to the deviation absolute values; and S5, solving the coordinates of the point to be measured through a weighted least square method, so that the measurement of the coordinates of the point to be measured is more reasonable and accurate.

This application relates to a flexible collaborative method and system for heavy-haul train platoons to resist malicious information degradation, belonging to the field of heavy-haul railway transportation technology. The method includes: modeling the communication topology of the heavy-haul train platoon using graph theory, treating each heavy-haul train as an independent intelligent agent that interacts only with its neighboring trains; establishing a dynamic model for each train and performing equivalent linearization based on pseudo-partial derivatives to obtain a data-dependent linearized model; constructing a channel fading model and a transmission state model to describe the impact of random fading and malicious information degradation on communication performance, respectively; and designing a distributed data-driven control algorithm based on the communication topology, linearized model, channel fading model, and transmission state model, including pseudo-partial derivative iterative estimation and parameter reset mechanisms, to generate control force commands for each train. This method can effectively resist malicious information degradation during communication, enabling bounded collaborative trajectory tracking of the platoon under information degradation environments, ensuring operational safety and transportation efficiency.

This application belongs to the field of microgrid frequency control, specifically disclosing a model-free adaptive microgrid frequency control method to resist network attacks. The method includes the following steps: considering the mixed effects of denial-of-service attacks and spoofing attacks, a system output model incorporating attack coefficients is established, modeling the microgrid frequency control system as a discrete-time nonlinear system; dynamic linearization technology is used to transform the nonlinear system into an equivalent linear data model; for the linear data model, a partial pseudo-derivative estimation algorithm is designed, and a model-free adaptive control law incorporating a decay function is designed; the time-varying partial pseudo-derivative matrix is ​​iteratively updated using the partial pseudo-derivative estimation algorithm, and based on the updated partial pseudo-derivative matrix, the control input at the current moment is calculated using the model-free adaptive control law to control the microgrid frequency. This application can achieve effective frequency control even when the microgrid frequency control system model is unknown and faces mixed network attack threats.

This application provides a data-driven multivariable adaptive predictive control method for an adaptive cyclic engine, belonging to the field of aero-engine technology. The method includes: constructing an equivalent data model of the adaptive cyclic engine based on full-format dynamic linearization of the engine's input and output data to obtain multi-step forward output prediction equations; performing real-time estimation of the pseudo-gradient matrix in the prediction equations using autoregressive and projection algorithms to form a predictive control rolling optimization framework; embedding a model-free adaptive control algorithm based on single-step optimization into this framework to form a composite control strategy; designing control input performance indicators with the goal of minimizing tracking error to obtain optimal control parameters; and generating real-time optimal control parameters using the engine's real-time input and output data based on the composite control strategy to achieve precise multivariable coordinated control. This application improves the long-term prediction, constraint handling, and global optimization capabilities of the control system.

The invention provides an aircraft bearing end face parallelism joint evaluation method with a common mode rejection function, and belongs to the technical field of aircraft engine bearing end face parallelism measurement. The method comprises the following steps: step 1, acquiring end face data of an inner ring and an outer ring of a bearing through a parallelism measurement system to obtain an original observation vector; 2, by introducing a weighing parameterization method, the absolute plane parameters of the inner ring and the outer ring are decoupled and recombined into common-mode tilt parameters and differential-mode shape parameters; step 3, error correlation modeling: capturing statistical characteristics of the system by constructing an equivalent covariance matrix containing cross correlation coefficients; step 4, performing robust judgment based on mahalanobis distance; and step 5, performing adjustment calculation in combination with a Gauss-Helmert model to obtain a final parallelism evaluation result. According to the method, the high-order residual problem of a traditional linearization model is solved, while common-mode vibration signals are reserved, real outliers are accurately recognized, and the numerical stability and the result reliability of parallelism evaluation are remarkably improved.

This invention provides a dual-antenna GNSS high-precision attitude and orientation method and system suitable for compact platforms, belonging to the field of GNSS navigation technology. The method includes: constructing a priori baseline vector using the carrier attitude information provided by the inertial navigation system and the baseline length of the dual antennas; establishing a linear constraint equation between the priori baseline vector and the baseline vector to be estimated; simultaneously solving the linear constraint equation with the GNSS linearized observation equation to construct a joint observation equation; solving the joint observation equation using the least squares method to obtain an ambiguity floating-point solution, and fixing the ambiguity of the ambiguity floating-point solution to obtain a solution for the baseline vector to be estimated; and calculating the pitch and heading angles of the carrier based on the solution for the baseline vector to be estimated. This invention achieves high-precision attitude and orientation using dual-antenna GNSS by establishing a linear constraint equation based on the prior baseline vector and simultaneously solving it with the GNSS linearized observation equation, improving the accuracy and continuity of attitude and orientation in ultra-short baseline scenarios.