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Exploiting a single-antenna receiver to realise three-dimensional (3D) positioning in a millimetre-wave (mmWave) system is considered. The primary motivation is that the massive antenna arrays will be deployed in the fifth-generation (5G) base stations shortly soon, which not only tremendously promote the data transmission rate, but also enable the user equipment to realise high-precision positioning with a single antenna. Based on the sparsity of the mmWave channel, the tensor decomposition is proposed to be utilised as an effective mathematical tool to realise 3D positioning. Specifically, the authors model the received signals as a third-order tensor for the inherent third-order low-rank tensor structure of the mmWave channel with a single-antenna receiver and then, the positioning parameters (including the angles of departure and the time of arrival) are estimated from the corresponding factor matrices via CAMDECOMP/PARAFAC (CP) decomposition. Moreover, Cramer–Rao bounds (CRBs) on 3D position uncertainty are derived. Numerical results demonstrate that the proposed method based on CP decomposition realises nearly the same positioning accuracy as the state-of-the-art compressed sensing-based algorithm in the 5G mmWave systems with lower computation complexity, and the root mean square errors of the 3D positioning results obtained via the proposed approach are close to their CRBs.

This study presents a novel method that utilizes Region-based Convolution Neural Network (R-CNN) for identifying objects in an image, and then, localizes the image using a pre-stored map of these objects. The algorithm follows the next steps. First, an image, taken by a smart phone, is uploaded to the server, where a pre-trained R-CNN and template images are combined to identify the objects in the image. In the second step, the location of the image is derived according to the spatial relationships of the objects in the image with a prior knowledge of the objects location in a digital map. Finally, if more than one location solutions are obtained from the previous step, a range-based localization method is utilized to eliminate the wrong results. To prove the concept, a test is conducted in typical yet challenging indoor environment. Selected objects were labeled in 600 images, taken randomly around the test environment, to train the R-CNN and used for template matching. Another 42 images, with known capture locations, were utilized to assess the performance of the method. The proposed object-based indoor localization method detected 81.7% of the objects in the images, and provided 59.5% success rate with 1-5 m accuracy.

When flying through undulating terrain or flying with a large tilt angle, the existing LiDAR navigation algorithms have difficulty in outputting continuous, stable and high-precision navigation information, which seriously affects the accuracy and robustness of LiDAR/INS integrated navigation system. To solve the above problems, this paper proposes an adaptive switching algorithm based on Sigmoid function. By judging the platform inclination and fluctuation degree of the scanning area, an adaptive switching mechanism of two LiDAR navigation methods of ICP and SLPF is constructed. The simulation results show that the airborne LiDAR/INS integrated navigation method proposed in this paper can greatly improve the position estimation accuracy when the platform is flying through the undulating terrain or flying with a large tilt angle (pitch angle, roll angle). Compared with the existing CMLP, ICP and SLPF algorithms, the proposed LiDAR/INS integrated navigation algorithm has improved the three-dimensional positioning accuracy by 78.7%, 53.7% and 18.3% respectively.

Long Term Evolution (LTE) is a promising positioning system for its appropriate positioning characteristics and excellent coverage, especially in the harsh environments such as urban canyons and indoor scenarios. However, most of the researches so far on LTE positioning algorithms are based on time parameters. Thus in this paper, an algorithm based on signal parameters estimation via rotational invariance techniques (ESPRIT) to jointly estimate the time-of- arrival (TOA) and the direction-of-arrival (DOA) from received LTE signals in multipath environment is presented. This method estimates the TOA parameters first after modeling the received LTE signals in the frequency domain. Then the DOA parameters via the difference of the TOA are obtained, and thereby the joint TOA and DOA estimation is gained. Simulation results show that this method performs well on different signal-to-noise-ratios, numbers of multipath and transmission bandwidths. Moreover, the simulation results show that the proposed algorithm has much better performance than multiple signal classification (MUSIC) algorithm and propagator method (PM) and its positioning accuracy can achieve centimeter level.