Target Localization Method Research Articles

The Technology of Moving Target Detection and Tracking Based on Sequential SAR Image

Although the multi-channel SAR-GMTI technology can be used in ground moving target detection, but only can get trace point of the ground moving target, the ground moving target tracking and track reconstruction can’t be achieved. This article presents a new method of ground moving target detection and tracking which is based on sequence SAR images. Firstly, the moving target detection and velocity estimation can be realized using the method of multi-channel processing. Secondly, the location of moving target can be estimated accurately using a high accuracy target location method based on knowledge-aid. The method uses the matching and target tracking technique based on sequential SAR images to realize the tracking and track reconstruction of moving target. Next, the image reference locking technology is used to achieve accurate image registration for a series of sequential SAR images obtained. Finally, the moving target can be tracked. The results of measured data shows the validity of the presented method.

Mine tagless target location method based on combined space and frequency diversity

Mine tagless target location method based on combined space and frequency diversity

3D target localization based on multi–unmanned aerial vehicle cooperation

Ground target three-dimensional positions measured from optical remote-sensing images taken by an unmanned aerial vehicle play an important role in related military and civil applications. The weakness of this system lies in its localization accuracy being unstable and its efficiency being low when using a single unmanned aerial vehicle. In this paper, a novel multi–unmanned aerial vehicle cooperative target localization measurement method is proposed to overcome these issues. In the target localization measurement stage, three or more unmanned aerial vehicles simultaneously observe the same ground target and acquire multiple remote-sensing images. According to the principle of perspective projection, the target point, its image point, and the camera’s optic center are collinear, and nonlinear observation equations are established. These equations are then converted to linear equations using a Taylor expansion. Robust weighted least-squares estimation is used to solve the equations with the objective function of minimizing the weighted square sum of re-projection errors from target points to multiple pairs of images, which can make the best use of the effective information and avoid interference from the observation data. An automatic calculation strategy using a weight matrix is designed, and the weight matrix and target-position coordinate value are updated in each iteration until the iteration stopping condition is satisfied. Compared with the stereo-image-pair cross-target localization method, the multi–unmanned aerial vehicle cooperative target localization method can use more observation information, which results in higher rendezvous accuracy and improved performance. Finally, the effectiveness and robustness of this method is verified by numerical simulation and flight testing. The results show that the proposed method can effectively improve the precision of the target’s localization and demonstrates great potential for providing more accurate target localization in engineering applications.

Wireless-sensor-network-based target localization: A semidefinite relaxation approach with adaptive threshold correction

In a localization environment based on wireless sensor networks, the non-line-of-sight (NLOS) propagation of wireless signal can greatly degrade the performance of the localization system. In this paper, a novel semidefinite-relaxation-based target localization method is presented by utilizing an adaptive threshold correction scheme. First, whether the measurement is in NLOS propagation is identified according to the prediction stage of extended Kalman filtering (EKF) algorithm. Then, the objective function and constraints are, respectively, constructed according to the measurement model of time of arrival (TOA) and received signal strength indicator (RSSI) ranging method. By employing the semidefinite relaxation method, the original problem is converted into the SDP problem, in which the corrected measurement information is obtained and thus the accuracy is improved. In addition, a correction method for NLOS propagation threshold is proposed by which an optimal threshold is obtained quickly. Different from the traditional simulation environment without the consideration of obstacles, in this paper, obstacles are set so as to make it closer to the engineering practice. Finally, the simulation results are given to show the superior performance of the proposed method.

Combined Weighted Method for TDOA-Based Localization

A novel and practical combined weighted (COM-W) method for target localization based on time difference of arrival (TDOA) is proposed in this paper. For 2-D situations with more than three nodes, the main idea of the proposed method is to obtain multiple preliminary localization results corresponding to the different three-node combinations first, and then use the weighted averaging technique based on the Cramer–Rao lower bound (CRLB) to obtain the final estimation. Considering target localization with a set of fixed-positioning nodes employing TDOA measurements as an example, the performance of the proposed method is evaluated by both simulations and acoustic indoor positioning experiments. The experimental results show that the COM-W method is a closed-form and computationally efficient method. It has an excellent positioning performance under both narrow and square geometric arrangements, specifically in the central cross-shaped area and in the vicinity of the nodes. Compared to the three classic weighted linear least squares (WLS)-based positioning methods, the proposed COM-W method is less sensitive to the measurement noise and the NLOS environment. The proposed method has an important application value for the realization of a TDOA-based indoor target positioning system.

An Improved Underwater Electric Field-Based Target Localization Combining Subspace Scanning Algorithm And Meta-EP PSO Algorithm

In this paper, we propose an improved three-dimensional underwater electric field-based target localization method. This method combines the subspace scanning algorithm and the meta evolutionary programming (meta-EP) particle swarm optimization (PSO) algorithm. The subspace scanning algorithm is applied as the evaluation function of the electric field-based underwater target locating problem. The meta-EP PSO method is used to select M elite particles by the q-tournament selection method, which could effectively reduce the computational complexity of the three-dimensional underwater target localization. Moreover, the proposed meta-EP PSO optimization algorithm can avoid subspace scanning trapping into local minima. We also analyze the positioning performance of the uniform circular and cross-shaped electrodes arrays by using the subspace scanning algorithm combined with meta–EP PSO. According to the simulation, the calculation amount of the proposed algorithm is greatly reduced. Moreover, the positioning accuracy is effectively improved without changing the positioning accuracy and search speed.

Target Localization in High-Coherence Multipath Environment Based on Low-Rank Decomposition and Sparse Representation

In a multipath propagation environment, prevalent target localization methods are mainly based on the classical two-ray propagation model without considering other reflected waves. Because the received target echoes are considerably corrupted by multipath reflections in the case of complex terrain, these prevalent methods might fail to work or achieve poor performance. To solve this problem, we first consider a practical multipath propagation scenario to reveal the dynamic structural relationship of the spatial paths based on the spherical earth model. Subsequently, a target localization algorithm based on low-rank decomposition (LRD) and sparse representation (SR) framework is proposed. The proposed algorithm can effectively mitigate the effects of complex multipath interference without using any prior knowledge on the illuminated terrain and the reflecting paths. Experiments on synthetic data and real data validate the effectiveness of the proposed algorithm.

Saliency-Based Diver Target Detection and Localization Method

Diver target automatic detection is indispensable for underwater defense systems, such as the unmanned harbor surveillance system. It is a very challenging task due to various poses and intensity features of diver target. In addition, the background noise in sonar images is complex, which also makes the task more difficult. In this paper, we propose a diver detection method based on saliency detection for sonar images. On the basis of studying the characteristics of diver sonar images, we first decompose the original sonar image and perform median filtering on it, which can significantly improve the quality of the sonar image saliency map. We employ saliency detection technique based on frequency analysis to segment the acoustic highlight region from its surroundings. This segmentation region roughly locates the diver target and generates a region of interest (ROI). We then extract the acoustic shadow region in ROI, which contributes to furtherly improve the localization accuracy. Finally, we merge the segmented highlight region and the extracted acoustic shadow region and compute the minimum outer rectangle of the merged region. Experimental results validate that the proposed method can well detect and locate the diver target, and it can also satisfy the demands of real-time application, and there is almost no false alarm in this method.

Hybrid RSS/AOA Localization using Approximated Weighted Least Square in Wireless Sensor Networks.

We present a target localization method using an approximated error covariance matrix based weighted least squares (WLS) solution, which integrates received signal strength (RSS) and angle of arrival (AOA) data for wireless sensor networks. We approximated linear WLS errors via second-order Taylor approximation, and further approximated the error covariance matrix using a least-squares solution and the variance in measurement noise over the sensor nodes. The algorithm does not require any prior knowledge of the true target position or noise variance. Simulations validated the superior performance of our new method.

Target Localization Methods Based on Iterative Super-Resolution for Bistatic MIMO Radar

The direction-of-departure (DOD) and the direction-of-arrival (DOA) are important localization parameters in bistatic MIMO radar. In this paper, we are interested in DOD/DOA estimation of both single-pulse and multiple-pulse multiple-input multiple-output (MIMO) radars. An iterative super-resolution target localization method is firstly proposed for single-pulse bistatic MIMO radar. During the iterative process, the estimated DOD and DOA can be moved from initial angles to their true values with high probability, and thus can achieve super-resolution estimation. It works well even if the number of targets is unknown. We then extend the proposed method to multiple-pulse configuration to estimate target numbers and localize targets. Compared with existing methods, both of our proposed algorithms have a higher localization accuracy and a more stable performance. Moreover, the proposed algorithms work well even with low sampling numbers and unknown target numbers. Simulation results demonstrate the effectiveness of the proposed methods.

Magnetic target recognition and localization method unaffected by attitude

This letter presents a magnetic target inversion method that does not vary with changes in the coordinate system and is based on the cross-product of the intermediate eigenvectors of any two points in the dipole field which is in the same/opposite direction as the magnetic moment vector. We used tensor geometric invariants to interpret this new physical property and obtain the unit magnetic moment vector. Using this, the unit vector of the measurement point-source displacement vector was derived. The distance between the measurement point and the source was obtained via the Frobenius norm of the gradient tensor matrix. Simulations verified that the proposed method is unaffected by attitudes and yields unique inversion results, and the results revealed that the inversion accuracy of the proposed method is high. The simulation results also show that conditional cosine and measurement noise have considerable influence on the inversion accuracy in the proposed method.

Target detection and localization method for distributed monopulse arrays in the presence of mainlobe jamming

In this paper, we propose a target detection and localization method on distributed monopulse arrays for tracking radar. An optimized mainlobe jamming (MLJ) cancellation filter was designed by maximizing the power ratio of the received siackgnal to the jamming-plus-noise. By exploiting the different correlation characteristics between the target echo and MLJ on distributed antennas, the designed filter is able to cancel MLJ and maintain the target echo. By applying the identical filter on sum-difference beams, MLJ can be cancelled, and the monopulse ratio can be maintained simultaneously. Hence, we simply detect and locate the target on the filtering output of sum-difference beams according to the monopulse principle. Monte Carlo simulations demonstrated that the proposed filter outperforms the conventional algorithms.

Closed-Form Localization Method for Moving Target in Passive Multistatic Radar Network

Utilizing the bistatic range (BR) and bistatic range rate (BRR) obtained from time delay difference and Doppler shift difference, a moving target can be localized with the passive multistatic radar (PMR) network. This paper presents a two-stage closed-form method for target localization in PMR. Different from the conventional two-stage weighted least squares (TSWLS) methods, the proposed method employs the weighted spherical-interpolation method to obtain an initial estimate in the first stage and reduces the error in the initial estimate with the deviation refinement in the second stage. Theoretical analysis through mean-square error (MSE) shows the proposed method approaches the Cramér-Rao lower bound (CRLB) accuracy under the assumption of mild measurement noises. Simulation results validate the analytical results. The proposed method is also shown to achieve the accuracy improvement in target velocity estimate at relatively higher noise levels.

Design and optimization of sensor array for magnetic gradient tensor system

PurposeThe differential magnetic gradient tensor system is usually constructed from the three-axis magnetic sensor array. While the effects of measurement error, sensor performance and baseline distance on localization performance of such systems have been widely reported, the research about the effect of spatial design of sensor array is less presented. This paper aims to provide a spatial design method of sensor array and corresponding optimization strategy to localization based on magnetic tensor gradient to get the optimum design of the sensor array. Based on the results of simulation, magnetic localization systems constructed from the proposed array and the traditional array have been built to carry out a localization experiment. The results of experiment have verified the effectiveness of magnetic localization based on the proposed array.Design/methodology/approachThe authors focus on the localization of the magnetic target based on magnetic gradient by using three-axis magnetic sensor array and combine a design method with corresponding optimization strategy to get the optimum design of the sensor array.FindingsThis paper provides an array design and optimization method for magnetic target localization based on magnetic gradient to improve the localization performance.Originality/valueIn this paper, the authors focus on the magnetic localization based on magnetic gradient by using three-axis magnetic sensors and study the effect of the spatial design of sensor array on localization performance.

Effect of Anesthesia on Microelectrode Recordings During Deep Brain Stimulation Surgery: A Narrative Review.

Deep brain stimulation (DBS) is an effective surgical treatment for patients with various neurological and psychiatric disorders. Clinical improvements rely on careful patient selection and accurate electrode placement. A common method for target localization is intraoperative microelectrode recording (MER). To facilitate MER, DBS surgery is traditionally performed under local or regional anesthesia. However, sedation or general anesthesia is sometimes needed for patients who are unable to tolerate the procedure fully awake because of severe motor symptoms, psychological distress, pain, or other forms of discomfort. The effect of anesthetic drugs on MER is controversial but likely depends on the type and dose of a particular anesthetic agent, underlying disease, and surgical target. In this narrative review, we provide an overview of the current literature on the anesthetic drugs most often used for sedation and anesthesia during DBS surgery, with a focus on their effects on MERs.

Acoustic localization of vehicular sources using distributed sensors.

This paper considers the problem of locating ground vehicles using their acoustic signatures recorded by unattended passive acoustic sensors. Acoustic signatures of the ground sources captured by different sensors within a cluster are used to generate direction of arrival (DoA) of the propagating wavefronts. Using the estimated DoAs of disparate distributed sensor node clusters, this paper introduced and compared several different existing target localization methods that provide the location and velocity estimates of a moving source. A robust source localization method is then proposed to account for large DoA errors and outliers which often occur in realistic settings. This method does not use any prior knowledge of the dynamical model of the moving source. The effectiveness and complexity of these methods are compared using synthesized and real acoustic signature data sets.

Real-time behavior detection and judgment of egg breeders based on YOLO v3

In order to detect the behavior of egg breeders in self-breeding cages rapidly, a method of target location and behavior recognition based on visual images was proposed. In this study, Hy-Line Gray chickens were bred as objects. Through manual marking, the training set, validation set and test set were established, and YOLO v3 model was adopted to detect the collected images. The value of subdivision and batch size were determined by experiment. The learning rate was dynamically adjusted according to the change of loss value in the training model. Finally, the mean average precision of the trained model on the validation set was 92.09%. In this paper, the recognition rates of six kinds of behaviors in the morning and in the afternoon and under different densities were analyzed. Furthermore, a kind of welfare indicator was tested and abnormal behavior was evaluated. The results showed that: The mean precision rate of the six behaviors was followed by mating (94.72%), stand (94.57%), feed (93.10%), spread (92.02%), fight (88.67%) and drink (86.88%). The mean false rate ranged from low to high was spread (0.11%), mating (0.14%), fight (0.20%), drink (0.25%), feed (1.17%) and stand (8.62%). The mean missing rate ranged from low to high was mating (4.65%), stand (5.01%), feed (5.15%), spread (6.25%), fight (14.69%) and drink (15.79%). The method presented in this paper has a good effect on identifying the behavior of egg breeders, which can provide technical support for the promotion of the self-breeding mode.

Application of Iterative Maximum Weighted Likelihood Estimation in 3-D Target Localization

Time-of-flight (ToF)-based 3-D target localization is a very challenging topic because of the pseudo-targets introduced by ToF measurement errors in traditional ToF-based methods. Although the influence of errors in ToF measurement can be reduced by the probability-based ToF method, the accuracy of localization is not very high. This paper proposes a new 3-D target localization method, Iterative Maximum Weighted Likelihood Estimation (IMWLE), that takes into account the spatial distribution of pseudo-targets. In our method, each pseudo-target is initially assigned an equal weight. At each iteration, Maximum Weighted Likelihood Estimation (MWLE) is adopted to fit a Gaussian distribution to all pseudo-target positions and assign new weight factors to them. The weight factors of the pseudo-targets, which are far from the target, are reduced to minimize their influence on localization. Therefore, IMWLE can reduce the influence of pseudo-targets that are far from the target and improve the accuracy of localization. The experiments were carried out in a water tank to test the performance of the IMWLE method. Results revealed that the estimated target area can be narrowed down to the target using IMWLE and a point estimate of target location can also be obtained, which shows that IMWLE has a higher degree of accuracy than the probability-based ToF method.

Spatio-temporal structure of single neuron subthalamic activity identifies DBS target for anesthetized Tourette syndrome patients

Objective. Deep brain stimulation (DBS) of basal ganglia effectively tackles motor symptoms of movement disorders such as Tourette syndrome (TS). The precise location of target stimulation site determines the range of clinical outcome in DBS patients, and the occurrence of side-effects of DBS. DBS implant procedures currently localize stimulation target relying on a combination of pre-surgical imaging, standardized brain atlases and on-the-spot clinical tests. Here we show that temporal structure of single unit activity in subthalamic nucleus (STN) of patients affected by pure TS can contribute to identify the optimal target location of DBS. Approach. Neural activity was recorded at different depths within STN with microelectrodes during DBS implant surgery. Depth specific neural features were extracted and correlated with the optimal depth for tic control. Main results. We describe for the first time temporal spike patterns of single neurons from sensorimotor STN of anesthetized TS patients. A large fraction of units (31.2%) displayed intense bursting in the delta band (<4 Hz). The highest firing irregularity and hence the higher density of bursting units (42%) were found at the optimal spot for tic control. Discharge patterns irregularity and dominant oscillations frequency (but not firing rate) carried significant information on optimal target. Significance. We found single unit activity features in the STN of TS patients reliably associated to optimal DBS target site for tic control. In future works measures of firing irregularity could be integrated with current target localization methods leading to a more effective and safer DBS for TS patients.

Cramér-Rao Lower Bound of Target Localization Method Based on TOA Measurements

The Cramér-Rao bound of target localization method based on time-of-arrival measurements is analyzed. For the localization error analysis, the CRLB is derived under the assumptions that the measurement errors are independent and characterized by zero-mean Gaussian distributed process with identical variance, which is not satisfied in the situation of localizing noncooperative targets. Cramér-Rao bound is deduced by using the fact that the variances of TOA measurements of different sensors are affected by the signal-to-noise ratio of the echo signal and are different from each other. Simulations of Monte Carlo experiments are carried out so as to verify the analytical results.