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A coherent two-dimensional (2D) multiple signal classification (MUSIC) processing for the simultaneous estimation of angular and range target positions has been presented. A 2D spatial smoothing technique is also introduced to cope with the coherent behaviour of the received echoes, which may result in a rank deficiency of the signals covariance matrix. The algorithm is analysed with respect to its application to coherent multiple-input multiple-output (MIMO) arrays. The extended baseline which is synthesised can indeed be used to further improve the performance of the system. The results of the algorithm are shown for both simulated and experimental data scenarios. In the latter case, the data are collected by a frequency-modulated continuous wave radar with MIMO functionality that has been designed and realised in cooperation with TNO, the Netherlands.

Ground target detection and positioning systems based on lightweight unmanned aerial vehicles (UAVs) are increasing in value for aerial reconnaissance and surveillance. However, the current method for estimating the target’s position is limited by the field of view angle, rendering it challenging to fulfill the demands of a real-time omnidirectional reconnaissance operation. To address this issue, we propose an Omnidirectional Optimal Real-Time Ground Target Position Estimation System (Omni-OTPE) that utilizes a fisheye camera and LiDAR sensors. The object of interest is first identified in the fisheye image, and then, the image-based target position is obtained by solving using the fisheye projection model and the target center extraction algorithm based on the detected edge information. Next, the LiDAR’s real-time point cloud data are filtered based on position–direction constraints using the image-based target position information. This step allows for the determination of point cloud clusters that are relevant to the characterization of the target’s position information. Finally, the target positions obtained from the two methods are fused using an optimal Kalman fuser to obtain the optimal target position information. In order to evaluate the positioning accuracy, we designed a hardware and software setup, mounted on a lightweight UAV, and tested it in a real scenario. The experimental results validate that our method exhibits significant advantages over traditional methods and achieves a real-time high-performance ground target position estimation function.

In this study the authors investigate the two-dimensional target localisation capabilities of a passive radar system based on WiFi transmissions. It is well known that the most straightforward way to achieve the target position estimation in the horizontal plane with a passive radar exploits the measurements either of a single bistatic range plus a direction of arrival (DoA) or of two bistatic ranges collected by two separate receivers. However, for a practical application it is interesting to clearly define which one of the two approaches provides the passive radar target localisation with a higher accuracy and whether combining both multiple bistatic range plus DoA measurements provides a further advantage. A multistatic configuration is considered which allows to collect a set of range/Doppler/angle measurements for a given target. Different target localisation strategies are devised and compared, based on subsets of the available measurements with the aim of understanding the localisation accuracies achievable using the different combinations of measurements. Experimental results are shown based on a passive radar prototype developed and fielded at the DIET Department – Sapienza University of Rome. This will contribute to demonstrate the fruitful application of the passive radar concept for short range surveillance.

The guzheng is one of the oldest plucked instruments in China, and few studies have been conducted on the guzheng, supplemented by physics. The guzheng playing techniques involving mechanical principles mainly include, “pinching” and “shaking”. The study is based on the 3D-DGR network model to identify the three kinds of 3D dynamic finger postures. Mean shift joint localisation and displacement velocity-based target point position estimation are used to track and identify the finger movements during the playing process. At the same time, the tangent comparison method is used to detect and calculate the finger joint angles, and to judge the finger function of the performer from the angle change when applying the basic playing techniques. The recognition accuracy and the recognition time of the finger movement gesture recognition model constructed in the study are 96% and 1.07ms, respectively, which is a high recognition efficiency. The results of the evaluation of finger function by professionals are consistent with the results of the evaluation using the change of knuckle angle, which proves the recognition accuracy of the computer vision technique.

This article is focused on the analytical solution of a TDOA (Time Difference of Arrival) localization method, including analysis of accuracy and unambiguity of a target position estimation in 2D space. The method is processed under two conditions - sufficiently determined localization system and an overdetermined localization system. It is assumed that the TDOA localization system operates in a LOS (Line of Sight) situation and several time-synchronized sensors are placed arbitrarily across the area. The main contribution of the article is the complete description of the TDOA localization method in analytical form only. It means, this paper shows a geometric representation and an analytical solution of the TDOA localization technique model. In addition, analyses of unambiguity and solvability of the method algorithm are presented, together with accuracy analysis of this TDOA technique in analytical form. Finally, the description of this TDOA method is extended to an overdetermined TDOA system. This makes it possible to determine and subsequently optimize its computational complexity, for example increase its computational speed. It seems that such a description of the TDOA localization technique creates a simple and effective tool for technological implementation of this method into military localization systems.

This study describes the processing scheme of the FKIE (Fraunhofer Institute for Communication, Information Processing and Ergonomics) GSM-based passive coherent location (PCL) system, which consists of an antenna and signal processing adapted to the GSM waveform and of target tracking based on multi-hypothesis tracking. To overcome the limitations from a single bistatic transmitter–receiver pair, fusion of the measurements from different geometries is the key component of a GSM PCL system. The authors demonstrate a significant improvement in target position estimation from the tracking process on the basis of real data and theoretical performance bounds. The impact of the transmitter–target–receiver geometry is discussed and the effect of the exploitation of prior context knowledge (e.g. clutter and land maps) on maritime traffic surveillance is shown.

Earth observation with unmanned aerial vehicles (UAVs) offers an extraordinary opportunity to bridge the gap between field observations and traditional air and space-borne remote sensing. In this regard, ground landing stations (GLS) systems play a central role to increase the time and area coverage of UAV missions. Bluetooth low energy (BLE) technology and the received signal strength indicator (RSSI) techniques have been proposed for target location during UAV landing. However, these RSSI-based techniques present a lack of precision due to the propagation medium characteristics, which leads to UAV position vagueness. In this sense, the development of a novel low-cost GLS system for UAV tracking and landing is proposed. The GLS system has been embodied for the purpose of testing the UAV landing navigation capability. The maximum likelihood estimator (MLE) algorithm is addressed on an embedded microcontroller for the position estimation based on the RSSI acquired from an array of BLE devices. Experimental results demonstrate the feasibility and accuracy of the ground landing station system, achieving average errors of less than 0.04 m with the UAV-MLE target position estimation approach. This 0.04 m distance represents an order of magnitude increase in location precision over other currently available solutions. In many cases, this increased precision can enable more innovative docking mechanisms, less likelihood of mishaps in docking, and also quicker docking. It may also facilitate docking procedures where the docking station is itself moving, which may be the case if the docking unit is a mobile ground rover.

On the basis of the technique of time reversal (TR), a new method for low dielectric contrast target detection in clutter by adding dispersive delay lines (DDLs) to each element of the TR mirror (TRM) is proposed. When compared with a conventional TR system, the proposed method improves refocusing to a target by reducing the impact of other scatterers in the environment. The proposed method makes it unnecessary to estimate the position of the target and removes the need for subsequent subtraction as traditionally required. Theoretical and numerical simulated results demonstrate the proposed method.

This paper focuses on the problem of ground-motion target localization tracking and motion state estimation for high-altitude reconnaissance using fixed-wing UAVs. Our goal is to accurately locate and track ground-moving targets and estimate their motion using visible light images, laser measurements of distance, and UAV position and attitude information. Firstly, this paper uses the target detection model of YOLOv8 to obtain the target pixel positions, combined with the measurement data, to establish the geolocalization model of the ground-motion target. Secondly, a motion state estimation algorithm with hierarchical filtering is proposed, and this algorithm performs motion state estimation for optoelectronic loads and ground-motion targets separately. Using the laser range sensor measurements as constraints, the optoelectronic load angle state quantities are involved together in estimating the ground target motion state, resulting in improved accuracy of ground-motion target localization tracking and motion state estimation. The experimental data show that the UAV ground-motion target localization tracking and motion estimation algorithm using hierarchical filtering reduces the localization tracking error by at least 7.5 m and the motion state estimation error by at least 0.8 m/s compared to other algorithms.

In the past few years, multiple-input multiple-output (MIMO) radar with electromagnetic vector sensor (EMVS) array, or called EMVS-MIMO radar, has attracted extensive attention in target detection. Unlike the traditional scalar sensor-based MIMO radar, an EMVS-MIMO radar can not only provides a two-dimensional (2D) direction finding of the targets but also offers 2D polarization parameter estimation, which may be important for detecting weak targets. In this paper, we investigate into multiple parameter estimations for a bistatic EMVS-MIMO radar in the presence of coherent targets, whose transmitting EMVS and receiving EMVS are placed in an arbitrary topology. Three tensor-aware spatial smoothing estimators are introduced. The core of the proposed estimators is to de-correlate the coherent targets via the spatial smoothing technique and then formulate the covariance matrix into a third-order parallel factor (PARAFAC) tensor. After the PARAFAC decomposition of the tensor, the factor matrices can be obtained. Thereafter, the 2D direction finding can be accomplished via the normalized vector cross-product technique. Finally, the 2D polarization parameter can be estimated via the least squares method. Unlike the state-of-the-art PARAFAC estimator, the proposed estimators are suitable for arbitrary sensor geometries, and they are robust to coherent targets as well as sensor position errors. In addition, they have better estimation performance than the current matrix-based estimators. Moreover, they are computationally efficient than the current subspace methods, especially in the presence of a large-scale sensor array. In addition, the proposed estimators are analyzed in detail. Numerical experiments coincide with our theoretical findings.