A distributed atmospheric turbulence model is developed for helicopter flight simulation. The impacts of different aerodynamic surfaces on handling qualities with helicopter in turbulence are analyzed. Both helicopter flight dynamic model and turbulence model are validated against the flight test data, which shows that the proposed model improves the accuracy of the simulation of rotor blade element turbulence model on the vertical axis within the interested frequency range for handling-quality analysis. A quantitative index is developed to study the impact of turbulence on helicopter handling qualities by analyzing the effects of different aerodynamic surfaces on helicopter response to turbulence. The results show that the main rotor has the dominant effects on both the heave and roll handling qualities, the airframe has the primary effect on the pitch handling quality, and the tail rotor has an important effect on the yaw handling quality. Furthermore, the impacts of the main rotor in turbulence on the helicopter handling qualities of roll, pitch, and yaw increase with the forward speed at lower speeds but decrease at higher speeds. The increment trend with the forward speed of the airframe’s impacts on the pitch and yaw handling qualities reduces at higher speeds.

Existing path planning schemes designed for wireless sensor networks generally account for abstract payload sensors, rendering them inapplicable to concrete sensor-array-based localization systems due to differences in measurement models. In this paper, we establish a general framework for path planning of a practical sensor array and factor in an oft-neglected degree of freedom regarding optimality, i.e., the array’s orientation/attitude. The optimization problem is formulated based on the A-optimality criterion under constraints arising from the maximum distance between consecutive waypoints, maximal heading change, and forbidden regions. To facilitate semidefinite relaxation (SDR), we recast the optimization function into a fractional nonhomogeneous quadratic structure and transform the constraints into a bilinear form. By applying SDR and replacing the bilinear terms with a matrix variable, the problem is relaxed into a single-ratio fractional program. By leveraging the Charnes-Cooper variable transformation, we transform the single-ratio fractional program into a mixed semidefinite/second-order cone program (SD/SOCP) that can be solved in polynomial time. Finally, we apply the results to angle-of-arrival (AOA) and direct localization. Simulation results demonstrate that the proposed path planning scheme attains near-optimal performance.

Direct position determination (DPD) (a.k.a. direct localization) offers enhanced precision over traditional two-step approaches. This technique, however, involves considerable communication overhead for transmitting raw data. Low-bit direct localization methods have recently been introduced to address this issue. In this letter, we present a gridless, one-bit maximum likelihood (ML) approach for the direct localization of an orthogonal frequency division multiplexing (OFDM) signal source. A recent majorization-minimization (MM) algorithm introduced a surrogate function for the log-likelihood function, which lacks a closed-form optimal solution and requires exhaustive searches at each iteration. Our method improves upon this algorithm by developing a refined surrogate function that yields a closed-form optimal solution, thereby eliminating the need for exhaustive searches. Accordingly, the proposed MM approach can eliminate grid quantization errors (GQE) by eliminating the search process. Simulation results validate the proposed method's efficacy in mitigating GQE and its efficiency in scenarios with densely populated search grids.

This paper focuses on the problem of using one-dimensional (1-D) angle-of-arrival (AOA) measurements, also referred to as space angles (SA), from linear arrays to achieve three-dimensional (3-D) rigid body localization (RBL). We address the constrained weighted least squares (CWLS) and maximum likelihood estimation (MLE) problems for SA RBL. We first establish a boundary condition for SA RBL, which determines the minimum number of sensors and anchors for SA RBL. Subsequently, we employ the semidefinite relaxation (SDR) technique to relax the feasible set of the CWLS problem, converting it into a convex semidefinite program (SDP) to achieve a coarse suboptimal solution. Since the SDR technique is limited to bilinear structures, it cannot tackle the MLE problem for SA RBL (given the inverse cosine term in the MLE objective). To handle the MLE problem for SA RBL and to obtain an exact solution for the CWLS problem, we propose a novel, generic majorization-minimization (MM) framework capable of finding a stationary point solution under the special orthogonal group constraint. We establish surrogate functions for the MLE and CWLS objectives and, as a by-product, derive upper bounds for the inverse cosine, squared inverse cosine, and nonhomogeneous Rayleigh quotient functions. Simulation results demonstrate that using the coarse solution obtained from SDR as the initial point, the proposed MM-based CWLS and MLE algorithms can achieve Cramér–Rao lower bound (CRLB) performance under low noise conditions, with the MLE algorithm exhibiting the lowest bias.

Arrays deployed in antenna-array-based direct position determination (DPD) systems are conventionally placed parallel to each other with invariant orientations, which may be inappropriate. This paper proposes a novel array orientation adjustment method to achieve optimal DPD performance, in which we seek optimal array orientations that maximize the trace of the Fisher information matrix (FIM) and then minimize localization uncertainty. The optimization problem is unconstrained, multivariable, and can be converted into multiple tractable single-variable optimization problems. An exact solution can be obtained by a straightforward but computationally tedious one-dimensional exhaustive search. To reduce the computational load, we then derive an asymptotic solution in cases where the arrays consist of massive antennas. Numerical examples demonstrate the effectiveness and feasibility of the proposed method in resolution and precision enhancement, especially in a low signal-to-noise ratio (SNR).

Millimeter-wave (mmWave) fifth-generation (5G) and beyond 5G localization enables the provisioning of extremely accurate positioning information, a feature that has attracted substantial research efforts. In this paper, we contribute to this effort by exploring optimal array geometric structures of direct position determination (DPD) systems, inspired by sensor placement problems that predominantly focus on two-step localization and have not yet been extended to DPD. Specifically, we research an optimal array placement and orientation strategy for two-dimensional (2-D) DPD systems that use sensors equipped with uniform linear arrays (ULA) to localize an agent. The A-optimality criterion in Bayesian optimal (experimental) design theory is invoked to formulate this problem. We use an optimization subproblem that optimizes array orientations when array locations are arbitrary but fixed to tackle this high-dimensional optimization problem. Then the optimization problem is converted into a typical optimal angular separation problem in two-step localization. Experimental results show that judiciously designed array geometric structures can lead to significant performance improvements.