Target Localization Method Research Articles

Combined AOA and TDOA Target Localization Method Under Distance-Dependent Noise Model

Combined AOA and TDOA Target Localization Method Under Distance-Dependent Noise Model

Monopulse Parameter Estimation for FDA-MIMO Radar under Mainlobe Deception Jamming

Multiple input multiple output with frequency diversity array (FDA-MIMO) radar has unique advantages in mainlobe deception jamming suppression and target location. However, if the training sample contains the target signal, it will lead to poor jamming suppression performance and large target measurement error. To deal with the problem, a method of coarse target location in the time domain is proposed based on the cumulative sampling analysis. Taking full advantages of the strongest correlation characteristic between the expected steering vector and the true target, the feature vector and feature value corresponding to the true target are found after feature decomposition. The time domain location of the target is roughly estimated during the cumulative sampling analysis from near to far. Then, a pure jamming training sample can be obtained by avoiding the location. Noise subspace projection algorithm is used to measure the angle and range of the target while suppressing mainlobe jamming. The simulation results show that the proposed method can roughly estimate the target location in the time domain when the mainlobe deception jamming completely covers the target. Compared with conventional methods, the performance of jamming suppression and target localization error are closer to the performance of ideal sampling.

Retracted: Combined GNSS/SLAM-Based High Accuracy Indoor Dynamic Target Localization Method

Retracted: Combined GNSS/SLAM-Based High Accuracy Indoor Dynamic Target Localization Method

Target Localization Method Based on Image Degradation Suppression and Multi-Similarity Fusion in Low-Illumination Environments

Frame buildings as important nodes of urban space. The include high-speed railway stations, airports, residences, and office buildings, which carry various activities and functions. Due to illumination irrationality and mutual occlusion between complex objects, low illumination situations frequently develop in these architectural environments. In this case, the location information of the target is difficult to determine. At the same time, the change in the indoor electromagnetic environment also affects the location information of the target. Therefore, this paper adopts the vision method to achieve target localization in low-illumination environments by feature matching of images collected in the offline state. However, the acquired images have serious quality degradation problems in low-illumination conditions, such as low brightness, low contrast, color distortion, and noise interference. These problems mean that the local features in the collected images are missing, meaning that they fail to achieve a match with the offline database images; as a result, the location information of the target cannot be determined. Therefore, a Visual Localization with Multiple-Similarity Fusions (VLMSF) is proposed based on the Nonlinear Enhancement And Local Mean Filtering (NEALMF) preprocessing enhancement. The NEALMF method solves the problem of missing local features by improving the quality of the acquired images, thus improving the robustness of the visual positioning system. The VLMSF method solves the problem of low matching accuracy in similarity retrieval methods by effectively extracting and matching feature information. Experiments show that the average localization error of the VLMSF method is only 8 cm, which is 33.33% lower than that of the Kears-based VGG-16 similarity retrieval method. Meanwhile, the localization error is reduced by 75.76% compared with the Perceptual hash (Phash) retrieval method. The results show that the method proposed in this paper greatly alleviates the influence of low illumination on visual methods, thus helping city managers accurately grasp the location information of targets under complex illumination conditions.

A Multi-Target Localization and Vital Sign Detection Method Using Ultra-Wide Band Radar.

Life detection technology using ultra-wideband (UWB) radar is a non-contact, active detection technology, which can be used to search for survivors in disaster rescues. The existing multi-target detection method based on UWB radar echo signals has low accuracy and has difficulty extracting breathing and heartbeat information at the same time. Therefore, this paper proposes a new multi-target localization and vital sign detection method using ultra-wide band radar. A target recognition and localization method based on permutation entropy (PE) and K means++ clustering is proposed to determine the number and position of targets in the environment. An adaptive denoising method for vital sign extraction based on ensemble empirical mode decomposition (EEMD) and wavelet analysis (WA) is proposed to reconstruct the breathing and heartbeat signals of human targets. A heartbeat frequency extraction method based on particle swarm optimization (PSO) and stochastic resonance (SR) is proposed to detect the heartbeat frequency of human targets. Experimental results show that the PE-K means++ method can successfully recognize and locate multiple human targets in the environment, and its average relative error is 1.83%. Using the EEMD-WA method can effectively filter the clutter signal, and the average relative error of the reconstructed respiratory signal frequency is 4.27%. The average relative error of heartbeat frequency detected by the PSO-SR method was 6.23%. The multi-target localization and vital sign detection method proposed in this paper can effectively recognize all human targets in the multi-target scene and provide their accurate location and vital signs information. This provides a theoretical basis for the technical system of emergency rescue and technical support for post-disaster rescue.

Real-time underwater target detection for AUV using side scan sonar images based on deep learning

To overcome the shortcomings of traditional methods for underwater target detection in sonar images, a novel target detection algorithm based on deep learning is proposed for the real-time detection for an autonomous underwater vehicle equipped with a side scan sonar (SSS). First, the preprocessing of raw SSS images and data augmentation methods are proposed to improve the quality and quantity of the data set. Second, to improve the detection efficiency, a method based on threshold segmentation and pixel importance value is used to quickly determine whether there are suspected targets in the SSS images. Then, an MA-YOLOv7 network based on YOLOv7 with multi-scale information fusion and an attention mechanism is proposed to detect targets in the screened images, and a target localization method is proposed to obtain the location (latitude and longitude) of the target. Finally, the algorithm is verified through simulation experiments and field tests. The results demonstrate that the proposed algorithm achieves state-of-the-art performance and can be applied to real underwater tasks with a detection recall of 0.836 and time consumption of 0.355 s per image.

A Two-Stage Aerial Target Localization Method Using Time-Difference-of-Arrival Measurements with the Minimum Number of Radars

Distributed radar systems promise to significantly enhance target localization by virtue of the superiority of multi-view observations from widely separated radars, compared to their monostatic counterparts. Nevertheless, when the radar number is limited, performing target localization bears the brunt of the parameter identifiability requirement that the parameter number must be no less than the number of independent measurements. In this way, the canonical two-stage target localization method, as well as its developments, is no longer appropriate for direct application. Hence, in this paper, we propose a novel target localization method using time-difference-of-arrival (TDOA) measurements with the minimum number of radars under platform position uncertainties. The referred distributed system is a bistatic multi-receiver system, where the primary signal is transmitted by a geostationary Earth orbit (GEO) satellite while receivers are equipped on several unmanned aerial vehicles (UAVs). In the first stage, the reference range from the reference radar to the target is estimated by a quadratic function, and then the weighted least squares (WLS) solution of the target location is updated by substituting the range estimate back into it. In the second stage, we invoke the Taylor series approximation to further refine the target localization obtained by the first stage. It can be foreseen that the developed method is beneficial for scenarios with a limited number of radars, including engineering projects such as fire control, surveillance, and guidance, to support high-accuracy target localization. The simulation results show the superiority of the localization performance of the proposed method over other existing methods.

Microwave target location method based on thediamond NV color center.

We propose a method for microwave target source localization based on the diamond nitrogen vacancy color center. We use coherent population oscillation effect and modulation and demodulation techniques to achieve the detection of microwave intensity of microwave target sources, with a minimum detection intensity of 0.59µW. Positioning of the microwave source was achieved within 50×100c m 2 distance from the system 1m away using the cubic spline interpolation algorithm and minimum mean squared error. The maximum positioning error was 3.5cm. This method provides a new, to the best of our knowledge, idea for the passive localization of microwave targets.

Minimum Variance Distortionless Response—Hanbury Brown and Twiss Sound Source Localization

Sound source target localization is an extremely useful technique that is currently utilized in many fields. The Hanbury Brown and Twiss (HBT) interference target localization method based on sound fields is not accurate enough for localization at low signal-to-noise ratios (below 0 dB). To address this problem, this paper introduces Minimum Variance Distortionless Response (MVDR) beamforming and proposes a new MVDR-HBT algorithm. Specifically, for narrowband signals, the inverse of the correlation matrix of the sound signal is calculated, and a guiding vector is constructed to compute the MVDR direction weights. These direction weights are then used to weight the correlation function of the HBT algorithm. Subsequently, the MVDR-HBT algorithm is extended from narrowband signals to broadband signals. As a result, the directivity of the HBT algorithm is optimized for wide- and narrowband signals, resulting in improved localization accuracy. Finally, the target localization accuracy of the MVDR-HBT algorithm is analyzed through simulation and localization experiments. The results show that the MVDR-HBT algorithm can accurately determine the direction of a sound source, with localization errors at different positions that are smaller than those produced by HBT. The localization performance of MVDR-HBT is considerably better than that of HBT, further verifying the simulation results. This study provides a new idea for target localization within an acoustic propagation medium (air).

An Analytical Method for 3-D Target Localization Based on a Four-Element Ultrasonic Sensor Array With TOA Measurement

A new analytical method for 3D target localization based on a four-element ultrasonic sensor array is proposed. In the sensor array, with the consideration of good real-time performance, one ultrasonic sensor (the reference ultrasonic sensor) works both as a transmitter and as a receiver, while the other three sensors work as receivers. A basic target localization model is developed. The necessary and sufficient condition of the proposed method to obtain the unique and analytical solution is analyzed. It is proved that if and only if the four ultrasonic sensors are not in the same plane, the four-element ultrasonic sensor array can obtain a unique and analytical solution. In addition, with the consideration of the stability and the complexity of the analytical solution, a practical four-element ultrasonic sensor array configuration is proposed, which is characterized in that the reference ultrasonic sensor is at the origin, the other three ultrasonic sensors are on the three coordinate axes respectively and the distances from the three ultrasonic sensors to the reference ultrasonic sensor are equal. 3D target localization experiments are carried out. The experimental results verify the effectiveness of the proposed method and the localization performance is satisfactory. The proposed sensor array can realize the 3D localization with the advantages of simple construction and less computation.

A Recursive Robust Set-Membership Estimator for WSN-Assisted Moving Targets Tracking With UBB Anchor Location Uncertainty

In this paper, a recursive robust set-membership fusion estimator (RSMFE) is presented to track the moving targets treated as the mobile nodes in a mobile wireless sensor network (MWSN) system with unknown but bounded (UBB) anchor location uncertainty. Firstly, the accurate ellipsoidal set containing the Lagrange remainder of nonlinear distance measurement, which contains the effect of anchor location uncertainty, is estimated and adopted to improve the stability and accuracy of the moving target tracking. Secondly, the recursive RSMFE-based tracking formulae of reduced computation complexity, consisting of the prediction formulae and the fusion update formulae, are derived to estimate the robust minimized or optimal ellipsoidal set containing the actual moving target state by appropriately fusing the measured distances from the nearby anchors subject to the UBB anchor location uncertainty, where the computation complexity of the fusion update formulae is reduced substantially based on the decoupled techniques and the equivalent transformation. Finally, the numerical simulation and experiment of the WSN-assisted moving target tracking system with various anchor location uncertainty settings are performed to verify the effectiveness and superiority of the proposed recursive RSMFE-based moving target tracking or localization method.

A Feature-Level Fusion-Based Target Localization Method with the Hough Transform for Spatial Feature Extraction

Traditional two-step localization methods and direct localization methods have practical problems when they are used for underwater acoustic source localization. In this paper, a localization method based on the feature-level information fusion is proposed, in which the Hough Transform is exploited to detect the line characteristics of the spatial features of the target. A secondary accumulation procedure is proposed to extract and fuse the good features instead of fusing all features. The possibility to produce a ghost target is greatly reduced. Hence, the robustness of the proposed method in low SNR scenarios is improved. Experimental results validate the efficiency of exploiting the Hough Transform to eliminate interfering spatial features without sacrificing the localization accuracy.

Vision-Based Underwater Target Following Control of an Agile Robotic Manta With Flexible Pectoral Fins

Manta-inspired robots have been increasingly applied in vision-based underwater tasks. However, owing to the refraction effect and motion coupling problem, their accurate target following control is still poorly understood. This paper proposes a vision-based underwater target following scheme for robotic manta. First, an agile robotic manta equipped with a binocular camera system is constructed. The motion modes are further detailed based on the propulsion analysis to the flexible pectoral fins. Second, a color-based underwater target detection and localization method is conceived. Specifically, the binocular refraction correction is employed to improve the localization accuracy. Third, combing the nonsingular terminal sliding mode controller and fuzzy inference system, a robust target following controller is presented to solve the coupling between distance and azimuth control of the robot. Finally, various aquatic experiments are conducted, verifying the effectiveness and robustness of the proposed underwater target following scheme. The obtained results provide valuable insights into the intelligent control of agile bioinspired robots performing vision-based tasks in dynamic underwater environments.

A machine learning-based positioning method for poultry in cage environments

Individuals or groups of animals exhibit different activities that characterize domain behavior. Rapid and accurate localization of poultry in small and complex cage environments helps analyze the poultry domain behavior. This study proposes a machine-learning-based method for locating poultry in small and complex cage environments. Here, the characteristics of ultra-high frequency-radio frequency identification devices were determined, received signal strength indicator values were collected, and the tag-coordinate regression problem was converted into a multi-area classification problem. Different models were used to predict the target position. The results revealed that the neural network model yielded the best prediction, locating the target within a 40 cm × 40 cm area with 88.74% accuracy or within a 30 cm × 30 cm area with 76.81% accuracy, with average errors of 7.61 cm and 7.97 cm, respectively. Finally, experiments with live chickens were performed, and the results were verified using synchronized video, obtaining a Pearson correlation coefficient exceeding 0.909. This study presents a feasible method for target localization in small and complex cage environments, providing valuable modal information for multimodal learning.

A new target localization method for image-guided radiation therapy of prostate cancer

In imaged-guided radiation therapy (IGRT), target localization is usually done with rigid-body registration based on anatomy matching. Problems arise when the target volume can only be matched partially due to inter-fractional organ motion and deformation, resulting in deteriorated target coverage and critical structure sparing. A new target localization method is investigated in which the treatment target volume is aligned with the prescription isodose surface. Our study included 15 prostate patients previously treated with intensity-modulated radiation therapy (IMRT). Patient setup and target localization were performed using a CT-on-rails system before and after the IMRT treatment. IMRT plans were generated on the original simulation CTs (15) and the same MUs and leaf sequences were used to compute the dose distributions on post-treatment CTs (98) with the isocenter adjustments based on either anatomical structure matching or prescription isodose surface alignment. When patients were aligned with the traditional anatomy matching method, the dose to 95% of the CTV, D95, received 74.0 – 77.6 Gy and the minimum CTV dose, Dmin, was 61.9 – 71.6 Gy, respectively, in the cumulative dose distributions. The rectal dose-volume constraints were violated in 35.7% of the treatment fractions. When patients were aligned using the new localization method, the dose to 95% of the CTV, D95, received 74.0 – 78.2 Gy and the minimum CTV dose, Dmin, was 68.4 – 71.6 Gy, respectively, in the cumulative dose distributions. The rectal dose-volume constraints were violated in 17.3% of the treatment fractions. Traditional IGRT target localization based on anatomy matching is effective for population-based PTV margins but not ideal for those patients with large inter-fractional prostate rotation/deformation due to large rectal and bladder volume variation. The new method using the prescription isodose surface to align the target volume could improve the target coverage and rectal sparing for these patients, which can be implemented clinically to improve target dose delivery accuracy.

A novel target detection and localization method in indoor environment for mobile robot based on improved YOLOv5

A novel target detection and localization method in indoor environment for mobile robot based on improved YOLOv5

A Two-Stage Method for Short-Wave Target Localization Using DOA and TDOA Measurements

A Two-Stage Method for Short-Wave Target Localization Using DOA and TDOA Measurements

Global Optimization Method for Ground Target Localization of Electro-Optical Platform under Limited Observation Conditions

Global Optimization Method for Ground Target Localization of Electro-Optical Platform under Limited Observation Conditions

A target location method for aerial images through fast iteration of elevation based on DEM

The predecessor of China Optics was China Optics and Applied Optics Digest, which was founded in 1985. At that time, it was the only retrieval journal in the field of optics in China. At the end of 2008, China Optics and Applied Optics Digest was renamed

Ground Target Location Method Under Fixed Installation of Inertial Measurement Unit and Camera

Ground Target Location Method Under Fixed Installation of Inertial Measurement Unit and Camera