Target localization by a multistatic UWB radar

Moving target localization & tracking has been currently widely addressed area of research because of its lot of significance in broad varying applications. The major approaches for target localization & tracking in a defined WSN are Range Based, Range Free, Prediction Filter based Approaches aimed at achieving either Localization & Tracking accuracy or Energy Efficiency or Security or combination of these depending upon application at hand. Besides other range based techniques (TOA, TDOA, AOA) aimed at localization/tracking, RSSI-based approach is most widely used in spite of various challenges associated with it. The most important reason is that it does not need the extra hardware as most of electronic gadgets have built-in RSSI Circuitry to read RSS. By exploiting this RSSI measurement, one can find distance between transmitter & receiver using a suitable mathematical model of signal attenuation. However due to high dynamicity of wireless environment, the RSSI measurements are notoriously unpredictable. This paper discusses the RSSI based localization & tracking problem in detail & also surveys various other solutions proposed by the research community in the domain. Recent strategies to improve localization accuracy & tracking accuracy in RSSI based systems especially for dynamic environments & open research issues are also discussed as well.

This study addresses the target localisation problem using bistatic range (BR) measurements in a multi-base system based on external emitter radar. An asymptotically efficient Taylor series method is proposed for target localisation. The initial value for Taylor series method is provided by some algebraic closed-form positioning algorithms, rather than priori initial guesses. The localisation convergence problem can also be avoided unless in an extremely large measurement noise. The proposed method can be applied to both critically determined and over-determined conditions. The performance is verified and evaluated through simulations. The results show that the method can realise Cramer-Rao lower bound (CRLB) precision under Gaussian measurement noise. In addition, the method is superior to the existing localisation estimation in positioning accuracy and robustness.

Wireless Sensor Network (WSN) is a new technology integrated with communications, embedded technology, and network functions; having capability of real-time collecting, communicating, and sensing, for which dynamic target locating and tracking is its critical task, as well as a basic element in its commercial and military application such as battlefield information accessing and traffic monitoring. Sensor target tracking is the technology of target location estimate based on measurements from corresponding sensors. An inaccurate estimated location may very easily lead to target tracking failure which reduces the reliability and stability of the target tracking [1]. Experts and scholars have done a lot of researches to solve problems exited in wireless sensor target tracking, and many new tracking algorithms have been proposed, among which tracking algorithms such as target contrail-fitting, linear prediction, Kalman filter, unscented Kalman filter(UKF) and particle filter[2] are most common applied. Sikdar, with his partners, has proposed target location tracking algorithm based on linear sensor which is proved better than conventional contrail fitting algorithm in its effects. Zhang Xiaoping , with his partners, has proposed WSN target tracking algorithm through modeling for quadratic polynomial motions which has improved target tracking accuracy[3]. However, there is a problem in common: all the algorithms above assume the tracking target contrail-fittings being in linear motion, while the actual contrail fitting is of uncertainty and variability, and the estimated accuracy can hardly be improved after being to a certain level. In this condition, the tracking error of the above algorithms can be greater [4]. Kalman filter algorithm can be applied for weakly nonlinear models, but for strongly nonlinear models, Unscented Kalman Filter algorithm can be better. Kalman filter work well in cases based on model-linear and Gaussian noise, thus its estimated accuracy is not that ideal [5, 6]. Particle filter algorithm is based on optimal regression Bayesian filtering algorithm in Monte Carlo simulation, which can accurately estimate the any distribution state with only a small amount of mobile anchor nodes. PF algorithm has distinguished advantages in processing non-linear, non-Gaussian cases [7]. However, in PF application process after several iterations, diversified degradation of the particles will occur, which is likely to lead a target tracking loss, thus further affect WSN target tracking accuracy [8]. In order to solve the diversified degradation of the particles in Particle Filter algorithm, this paper Research on the Target Tracking Algorithm for Wireless Sensor Network Based on Improved Particle Filter Liang Peng,Xinhan Huang, Li He

The tracking of underwater targets represents a basic unit in underwater surveillance for offering effective search and rescue operations. However, the tracking of the target is a major constraint in finding the underwater target. This paper devises a model for underwater target tracking considering radar signals. Radar signals are subjected to the image reconstruction process to make them suitable for further processing. The gridding is the next step, which is utilized for generating the grids to focus on each region in a precise manner. The features like Ridgelet transform and Local Gabor XOR Pattern (LGXP) are produced. Data augmentation is the next process, which helps to reduce the size of data by performing resizing. At last, the tracking of targets is performed from each grid with a Deep neuro fuzzy network (DNFN). Here, DNFN training is performed with Exponential-Competitive Swarm Optimization (E-CSO), which is derived by integrating Exponential Weighted Moving Average (EWMA) and Competitive Swarm Optimization (CSO). The proposed E-CSO-based DNFN offered high efficiency with a maximal detection rate of 98.8%, minimal Mean square error of 0.012, and Mean Absolute Percentage Error (MAPE) of 17.883.

Human respiration state is an important indicator to reflect health conditions. Recent advances in wireless human sensing have enabled device-free respiration monitoring using narrow-band wireless signals, which, however, fail to map the estimated respiration states to multiple persons. In this paper, we present Multi-Breath, a UWB-based system to achieve separate respiration monitoring for multiple persons. The UWB radar can accurately measure the travelling distance of the signals, which helps to separate the signals affected by different persons and map the detected respiration patterns to the corresponding persons with the location information. However, the radar signal time series of each person are quite noisy due to the multi-path effects caused by the respiration movements of other persons, making it difficult to accurately estimate the respiration state. To overcome this challenge, we propose to transform the UWB radar signal matrices of different persons as separate RGB images to reveal the respiration pattern of each individual. Then, the image processing operations, including image smoothing, edge detection, dilation and erosion, are applied to identify the breathing cycles. Finally, the respiration state, including the respiration rate and the presence of apnea, is estimated via blob detection and calibration. Extensive experiments show that the mean absolute error on respiration rate estimation is 0.3 - 0.6 bpm, and the percentage of missed and false detected apnea is 3% - 7%.

Target positioning and tracking in wireless sensor networks (WSNs) are necessary for real ap-plications. This paper uses an artificial fish swarm algorithm (AFSA) with receive signal strength indicator (RSSI) for indoor target positioning and tracking. The performance of random and fixed artificial fish deployment topologies in AFSA for target positioning and tracking is investigated when the number of artificial fish is 12, 24, 52, 72, and 100, respectively. The simulation results show that the difference in average error between the random and fixed deployments is 5%, and the average positioning time is 2% for target position. The target tracking performance of fixed deployment is better than that of random one when the number of artificial fish is small.

Current image-guided prostate radiotherapy often relies on the use of implanted fiducial markers (FMs) or transducers for target localization. Fiducial or transducer insertion requires an invasive procedure that adds cost and risks for bleeding, infection and discomfort to some patients. We are developing a novel markerless prostate localization strategy using a pre-trained deep learning model to interpret routine projection kV X-ray images without the need for daily cone-beam computed tomography (CBCT). A deep learning model was first trained by using several thousand annotated projection X-ray images. The trained model is capable of identifying the location of the prostate target for a given input X-ray projection image. To assess the accuracy of the approach, three patients with prostate cancer received volumetric modulated arc therapy (VMAT) were retrospectively studied. The results obtained by using the deep learning model and the actual position of the prostate were compared quantitatively. The deviations between the target positions obtained by the deep learning model and the corresponding annotations ranged from 1.66 mm to 2.77 mm for anterior-posterior (AP) direction, and from 1.15 mm to 2.88 mm for lateral direction. Target position provided by deep learning model for the kV images acquired using OBI is found to be consistent that derived from the implanted FMs. This study demonstrates, for the first time, that highly accurate markerless prostate localization based on deep learning is achievable. The strategy provides a clinically valuable solution to daily patient positioning and real-time target tracking for image-guided radiotherapy (IGRT) and interventions.

Target localization is an important task for a broad range of ubiquitous computing applications. A great deal of previous work has focused on applying advanced machine learning methods to address image-based target detection and localization problems. Some work has also explored the localizing capabilities of radar signals that describe target distances and velocities. This paper proposes a novel machine learning architecture, SoliNet, to localize target positions from radar images alone. Radar data were acquired by Google’s Soli sensors and processed to generate range-Doppler images describing the motion of a radar target. Corresponding camera images of the target were used as validation data. Over 60 experiments including six unique target trajectories were performed using a robotic actuator rig. The SoliNet machine learning architecture was then trained to accomplish localization using the known location of the target over time as dictated by the programmed robot trajectory. SoliNet is an end-to-end trained combination of deep convolutional and recurrent neural networks. Experimental results showed an overall accuracy of 98.6% and equivalent effectiveness to camera-based target localization, demonstrating the promising capabilities of the proposed technique.

Tracking human body is an important problem in computer vision field. Tracking failures caused by occlusion can lead to wrong rectification of the target position. In this paper, a robust human tracking algorithm is proposed to address the problem of occlusion, rotation and improve the tracking accuracy. It is based on Tracking-Learning-Detection framework. The key auxiliary information is used in the framework which motivated by the fact that a tracking target is usually embedded in the context that provides useful information. First, face localization method is utilized to find key face location information. Second, the relative position relationship is established between the auxiliary information and the target location. With the relevant model, the key face information will get the current target position when a target has disappeared. Thus, the target can be stably tracked even when it is partially or fully occluded. Experiments are conducted in various challenging videos. In conjunction with online update, the results demonstrate that the proposed method outperforms the traditional TLD algorithm, and it has a relatively better tracking performance than other state-of-the-art methods.

This article topics based on multi-feature fusion the Mean shift target tracking technology belongs to the field of intelligent video analysis, moving target tracking is interested in moving target location each image in a video sequence to find and acquire the target movement. Moving target tracking problem can be stated as interested in moving target movement prediction in the video sequence, feature extraction, feature matching and template update problem. In this paper, we consider using compressed domain features as a complement of the color features to extract the compressed domain features first need to understand the compressed domain detection technology. Detection based on the compressed domain, that is, in the case of not decoding or a small amount of decoding, directly on the compression characteristics of the image analysis, in order to achieve the detection of the image moving object.

Suppose that we have the distances to p + 1 targets obtained from a moving platform at discrete times with the ( p + 1)st target being a reference beacon. Assume that the distances are measured with an additive error which has a zero mean and that the errors are uncorrelated in time and space. Given the knowledge of the error structure, the position of the reference target, and the set of the successive simultaneous ranges, we present a statistical method for accurately determining the positions of the remaining targets in a computationally reasonable manner, assuming that the platform positions satisfy a certain constraint. Alternatively, reversing the roles of targets and observer, the method can be used to track a moving target platform using ranges simultaneously measured from p observers whose fixed positions are known, provided that there is a ( p + 1)st observer at a known position. As long as the target track is sufficiently variable, the discrete-time range data can be used to estimate the position of the p observers and the target track. In practice the method could be used to refine the location estimates of the observers based on successive simultaneous range measurements of the target while tracking the target. If {\sl two} observer positions {sl are known}, {sl no constraint} has to be put on the target track. The technique is analyzed using several artificial data sets for one reference target (or observer). We show that when the error is small we obtain an accurate map of ten fixed targets from 500 platform locations, and the map is also reasonably accurate even when the mean error is 70 percent of the average true distance. We also analyze another model that has 14 targets observed from 1000 locations but where the location of two of these targets are known. In any of the scenerios, as the number of successive range measurements whose errors are uncorrelated grows to infinity, the coordinate estimates of the p target (observer) locations converges in probability to the true coordinates.

Image-free single-pixel detection, which has the advantages of low computing power requirements, a wide working waveband and high detection speed, provides a new paradigm for target tracking. A dedicated mask-based four-pixel parallel detection system is proposed for ultraviolet (UV) target positioning and tracking. The system consists of four parallel branches. Four masks with linearly increasing transmittance provide one-order geometric moment modulation of the scene simultaneously. The four branches are arranged in a diamond structure to ensure complementarity and symmetry in the x and y directions, respectively. A chrome mask and a photomultiplier tube module are selected to ensure the system performance in the UV waveband. Target positioning is achieved by centroid calculation with a geometric moment and a global system calibration method is proposed to ensure accuracy. The validity of the proposed system is demonstrated with tracking experiments using a UV target. This work provides a practical and low-cost solution for real-time UV target positioning and tracking.

We present a novel hybrid localization algorithm for wireless sensor networks in the absence of knowledge regarding the transmit power and path-loss exponent. Transmit power and the path-loss exponent are critical parameters for target localization algorithms in wireless sensor networks, which help extract target position information from the received signal strength. In the absence of information on transmit power and path-loss exponent, it is critical to estimate them for reliable deployment of conventional target localization algorithms. In this paper, we propose a simultaneous estimation of transmit power and path-loss exponent based on Kalman filter. The unknown transmit power and path-loss exponent are estimated using a Kalman filter with the tentatively estimated target position based solely on angle information. Subsequently, the target position is refined using a hybrid method incorporating received signal strength measurements based on the estimated transmit power and path-loss exponent. Our proposed algorithm accurately estimates transmit power and path-loss exponent and yields almost the same target position accuracy as the simulation results confirm, as the hybrid target localization algorithms with known transmit power and path-loss exponent. Simulation results confirm the proposed algorithm achieves 99.7% accuracy of the target localization performance with known transmit power and path-loss exponent, even in the presence of severe received signal strength measurement noise.

In this paper, we present a sparsity-based approach for target location and velocity estimation using a network of distributed dual-frequency radar units. A single dual-frequency radar can only estimate the range and radial velocity component of the moving target. The distributed configuration permits not only target localization in cross-range and downrange, but also provides Doppler velocity diversity, which enables the estimation of the horizontal and vertical target velocity components. We develop a linear signal model for the distributed radar network configuration under dual-frequency operation, and perform joint optimization for simultaneously recovering the target location and motion parameters. Supporting simulation results are provided, which validate the effectiveness of the proposed method.

This paper presents a real life experimental study on moving human target localization using dual frequency continuous wave (DFCW) radar combined with Doppler Direction-of-Arrival (DDOA) algorithm. The DDOA method was adopted for moving target localization in both open-space and through-the-wall scenarios, which estimates target location based on range information and DOA information derived from Doppler phase. DOA estimation method taking into consideration of phase delay instability of RF cables is presented to eliminate cable effect. Experimental results for both scenarios are conducted and results show that good performance can be obtained for open-space trials but limited performance is observed for through-the-wall trials due to limited SNR. However, target can still be tracked to some degree in the latter case.