To improve the localization precision of unmanned aerial vehicle (UAV), a novel framework is established by jointly utilizing multiple measurements of received signal strength (RSS) from multiple base stations (BSs) and multiple points on trajectory. First, a joint maximum likelihood (ML) of exploiting both trajectory information and multi-BSs is proposed. To reduce its high complexity, two low-complexity localization methods are designed. The first method is from BS to trajectory (BST), called LCSL-BST. First, fixing the nth BS, by exploiting multiple measurements along trajectory, the position of UAV is computed by ML rule. Finally, all computed positions of UAV for different BSs are combined to form the resulting position. The second method reverses the order, called LCSL-TBS. We also derive the Cramer-Rao lower boundary (CRLB) of the joint ML method. From simulation results, we can see that the proposed joint ML and separate LCSL-BST methods have made a significant improvement over conventional ML method without use of trajectory knowledge in terms of location performance. The former achieves the joint CRLB and the latter is of low-complexity.

Passive geolocation by multiple unmanned aerial vehicles (UAVs) covers a wide range of military and civilian applications including rescue, wild life tracking and electronic warfare. The sensor-target geometry is known to significantly affect the localization precision. The existing optimal sensor placement strategies mainly work on the cases without any constraints on the sensor locations. However, UAVs cannot fly/hover simply in arbitrary region due to realistic constraints, such as the geographical limitations, the security issues, and the max flying speed. In this paper, optimal geometrical configurations of UAVs in received signal strength (RSS)-based localization under region constraints are investigated. Employing the D-optimal criteria, i.e., maximizing the determinant of Fisher information matrix (FIM), such optimal problem is formulated. Based on the rigorous algebra and geometrical derivations, optimal and also closed-form configurations of UAVs under different flying states are proposed. Finally, the effectiveness and practicality of the proposed configurations are demonstrated by simulation examples.

Massive multiple input multiple output (MIMO) antenna arrays eventuate a huge amount of circuit costs and computational complexity. To satisfy the needs of high precision and low cost in future green wireless communication, the conventional hybrid analog and digital MIMO receive structure emerges a natural choice. But it exists an issue of the phase ambiguity in direction of arrival (DOA) estimation and requires at least two time-slots to complete one-time DOA measurement with the first time-slot generating the set of candidate solutions and the second one to find a true direction by received beamforming over this set, which will lead to a low time-efficiency. To address this problem, a new heterogeneous sub-connected hybrid analog and digital (H ² AD) MIMO structure is proposed with an intrinsic ability of removing phase ambiguity, and then a corresponding new framework is developed to implement a rapid high-precision DOA estimation using only single time-slot. The proposed framework consists of two steps: 1) form a set of candidate solutions using existing methods like MUSIC; 2) find the class of the true solutions and compute the class mean. To infer the set of true solutions, we propose two new clustering methods: weight global minimum distance (WGMD) and weight local minimum distance (WLMD). Next, we also enhance two classic clustering methods: accelerating local weighted k-means (ALW-K-means) and improved density. Additionally, the corresponding closed-form expression of Cramer–Rao lower bound (CRLB) is derived. Simulation results show that the proposed frameworks using the above four clustering can approach the CRLB in almost all signal to noise ratio (SNR) regions except for extremely low SNR (SNR< −5 dB). Four clustering methods have an accuracy decreasing order as follows: WGMD, improved DBSCAN, ALW-K-means and WLMD.

For a sub-connected hybrid multiple-input multiple-output (MIMO) receiver with $K$ subarrays and $N$ antennas, there exists a challenging problem of how to rapidly remove phase ambiguity in only single time-slot. A direction of arrival (DOA) estimator of maximizing received power (Max-RP) is proposed to find the maximum value of $K$ -subarray output powers, where each subarray is in charge of one sector, and the center angle of the sector corresponding to the maximum output is the estimated true DOA. To make an enhancement on precision, Max-RP plus quadratic interpolation (Max-RP-QI) method is designed. In the proposed Max-RP-QI, a quadratic interpolation scheme is adopted to interpolate the three DOA values corresponding to the largest three receive powers of Max-RP. To achieve the Cramer Rao lower bound, a Root-MUSIC plus Max-RP-QI scheme is developed. Simulation results show that the proposed three methods eliminate the phase ambiguity during one time-slot and also show low computational complexities. The proposed Root-MUSIC plus Max-RP-QI scheme can reach the Cramer Rao lower bound, and the proposed Max-RP and Max-RP-QI are still some performance losses 2–4 dB compared to the Cramer Rao lower bound.

For a passive direction of arrival (DOA) measurement system using massive multiple input multiple output (MIMO), it is mandatory to infer whether the emitter exists or not before performing DOA estimation operation. Inspired by the detection idea from radio detection and ranging (radar), three high-performance detectors are proposed to infer the existence of single or multiple passive emitter from the eigen-space of sample covariance matrix of receive signal vector. The first test statistic (TS) is defined as the ratio of maximum eigen-value (Max-EV) to minimum eigen-value (R-MaxEV-MinEV) while the second one is defined as the ratio of Max-EV to noise variance (R-MaxEV-NV). The third TS is the mean of maximum eigen-value (EV) and minimum EV(M-MaxEV-MinEV). Their closed-form expressions are presented and the corresponding detection performance is given. Simulation results show that the proposed M-MaxEV-MinEV and R-MaxEV-NV methods can approximately achieve the same detection performance better than the traditional generalized likelihood ratio test method and energy detection method with a given false alarm probability.

The angle of arrival (AOA) is widely used to locate a wireless signal emitter in unmanned aerial vehicle (UAV) localization. Compared with received signal strength (RSS) and time of arrival (TOA), AOA has higher accuracy and is not sensitive to the time synchronization of the distributed sensors. However, there are few works focusing on three-dimensional (3-D) scenarios. Furthermore, although the maximum likelihood estimator (MLE) has a relatively high performance, its computational complexity is ultra-high. Therefore, it is hard to employ it in practical applications. This paper proposed two center of inscribed sphere-based methods for 3-D AOA positioning via multiple UAVs. The first method could estimate the source position and angle measurement noise at the same time by seeking the center of an inscribed sphere, called the CIS. Firstly, every sensor measures two angles, the azimuth angle and the elevation angle. Based on that, two planes are constructed. Then, the estimated values of the source position and the angle noise are achieved by seeking the center and radius of the corresponding inscribed sphere. Deleting the estimation of the radius, the second algorithm, called MSD-LS, is born. It is not able to estimate angle noise but has lower computational complexity. Theoretical analysis and simulation results show that proposed methods could approach the Cramér–Rao lower bound (CRLB) and have lower complexity than the MLE.