Multiple Target Tracking Research Articles

Joint Sensor Selection and Power Allocation Algorithm for Multiple-Target Tracking of Unmanned Cluster based on Fuzzy Logic Reasoning.

The unmanned aerial vehicle (UAV) cluster is gradually attracting more attention, which takes advantage over a traditional single manned platform. Because the size of the UAV platform limits the transmitting power of its own radar, how to reduce the transmitting power while meeting the detection accuracy is necessary. Aim at multiple-target tracking (MTT), a joint radar node selection and power allocation algorithm for radar networks is proposed. The algorithm first uses fuzzy logic reasoning (FLR) to obtain the priority of targets to radars, and designs a radar clustering algorithm based on the priority to form several subradar networks. The radar clustering algorithm simplifies the problem of multiple-radar tracking multiple-target into several problems of multiple-radar tracking a single target, which avoids complex calculations caused by multiple variables in the objective function of joint radar node selection and power allocation model. Considering the uncertainty of the target RCS in practice, the chance-constraint programming (CCP) is used to balance power resource and tracking accuracy. Through the joint radar node selection and power allocation algorithm, the radar networks can use less power resource to achieve a given tracking performance, which is more suitable for working on drone platforms. Finally, the simulation proves the effectiveness of the algorithm.

Simultaneous tracking of a maneuvering ship and its wake using Gaussian processes

Maneuvering ship tracking has applications in maritime surveillance and the performance of tracking algorithms is affected by background clutter and ship-generated wake. Previous works consider the wake as a form of clutter with some specific spatial distribution. In this paper, the Kelvin wake, which assumes that the ship-generated wake is V-shaped, is modelled as an extended target with an irregular and time-varying shape. Thus, the problem of maneuvering ship tracking in the presence of wake becomes a special multiple target tracking problem, where the ship is a point target and the wake is an extended target. A Gaussian Processes based Multiple Model Joint Probabilistic Data Association (GP-MM-JPDA) method is proposed to estimate the ship state and the wake state simultaneously. Two versions of the GP-MM-JPDA are implemented. The first one can be applied to most multiple target tracking problems and the second one is designed especially for the ship tracking problem. Both versions can improve the ship tracking performance. In addition, the proposed methods can also infer the ship length using the estimated wake angle, which facilitates ship classification. Simulation results show the effectiveness of the proposed methods.

Adaptive simultaneous multibeam resource management for colocated MIMO radar in multiple targets tracking

Different from conventional phased array radar, the colocated multiple-input multiple-output (MIMO) radar can form multiple beams simultaneously through transmitting orthogonal waveforms by its sub-arrays. The illuminating mode of multiple beams can be changed with the sub-array number, providing greater flexibility in system resource management. In this paper, a novel resource management optimization model for the colocated MIMO radar in multiple targets tracking is proposed, where the average time and energy resource consumption of the system are minimized under the guarantee that the illuminated targets can be effectively detected. When solving the proposed optimization problem, the adaptive simultaneous multibeam resource management (ASMRM) algorithm for the colocated MIMO radar is obtained, where the sub-array number, the beam directions, the system sampling period, the transmit waveform energy and the working mode can be controlled adaptively. Simulation results demonstrate that the working parameters of the colocated MIMO radar can be changed effectively. Furthermore, in terms of the average system resource consumption, the proposed algorithm is superior to the algorithms with fixed parameters while realizing the effective multiple targets tracking.

Dual‐mode multiple‐target tracking in wireless sensor networks based on sensor grouping and maximum likelihood estimation

SummaryThe problem of tracking multiple mobile targets, using a wireless sensor network, is investigated in this paper. We propose a new sensor grouping algorithm, based on the maximum sensor separation distances (G‐MSSD), for estimating the location of multiple indistinguishable targets, either jointly or individually, depending on the distances between the generated groups. The joint tracking algorithm is formulated as a maximum likelihood (ML) estimator and solved through a modified version of the well‐known Gauss‐Newton (MGN) iterative method. We propose two candidate initial guesses for MGN based on G‐MSSD in joint tracking mode, while for the individual mode, the information of each group is used to estimate the location of only the corresponding target. The Cramer‐Rao lower bound (CRLB) for the variance of the proposed ML estimator is derived, and the potential conditions for reducing the CRLB are presented. Since tracking efficiency is affected by poor estimates, we present two criteria to evaluate the quality of estimates and detect the poor ones. An approach is also proposed for correcting the poor estimates, based on additional initial guesses. We demonstrate the effectiveness and accuracy of our proposed dual‐mode algorithm via simulation results and compare our results with the Multi‐Resolution search algorithm.

Improved interacting multiple model algorithm airport surface target tracking based on geomagnetic sensors

To avoid the inherent defects of current airport surface surveillance systems, a distributed non-cooperative surface surveillance scheme based on geomagnetic sensor technology is proposed in this article. Furthermore, a surface target tracking algorithm based on improved interacting multiple model (WIMM) is presented for use when the target is perceptible. In this algorithm, the weighted sum of the mean values of the residual errors, which is used to reconstruct the model probabilistic likelihood function, and the Markov model transition probability are updated using posterior information. When a target is imperceptible, its trajectory can be predicted by the target identified motion model and the adaptive model transition probability. Simulation results show that the WIMM algorithm can be used efficiently together with an observed small sample of velocity information for target tracking and trajectory prediction. Compared with the interacting multiple model and residual-mean interacting multiple model algorithms, the frequency of model switching and the rate of model identification were increased during the imperceptible period, and target prediction error was greatly reduced.

Autonomous collision detection and avoidance for ARAGON USV: Development and field tests

AbstractThis study addresses the development of algorithms for multiple target detection and tracking in the framework of sensor fusion and its application to autonomous navigation and collision avoidance systems for the unmanned surface vehicle (USV) Aragon. To provide autonomous navigation capabilities, various perception sensors such as radar, lidar, and cameras have been mounted on the USV platform and automatic ship detection algorithms are applied to the sensor measurements. The relative position information between the USV and nearby objects is obtained to estimate the motion of the target objects in a sensor‐level tracking filter. The estimated motion information from the individual tracking filters is then combined in a central‐level fusion tracker to achieve persistent and reliable target tracking performance. For automatic ship collision avoidance, the combined track data are used as obstacle information, and appropriate collision avoidance maneuvers are designed and executed in accordance with the international regulations for preventing collisions at sea (COLREGs). In this paper, the development processes of the vehicle platform and the autonomous navigation algorithms are described, and the results of field experiments are presented and discussed.

Continuous Tracking of Targets for Stereoscopic HFSWR Based on IMM Filtering Combined with ELM

High frequency surface wave radar (HFSWR) plays an important role in marine surveillance on account of its ability to provide wide-range early warning detection. However, vessel target track breakages are common in large-scale marine monitoring, which limits the continuous tracking ability of HFSWR. The following are the possible reasons for track fracture: highly maneuverable vessels, dense channels, target occlusion, strong clutter/interference, long sampling intervals, and low detection probabilities. To solve this problem, we propose a long-term continuous tracking method for multiple targets with stereoscopic HFSWR based on an interacting multiple model extended Kalman filter (IMMEKF) combined with an extreme learning machine (ELM). When the trajectory obtained by IMMEKF breaks, a new section of the track will start on the basis of the observation data. For multiple-target tracking, a number of broken tracks can be obtained by IMMEKF tracking. Then the ELM classifies the segments from the same vessel by extracting different features including average velocity, average curvature, ratio of the arc length to the chord length, and wavelet coefficient. Both the simulation and the field experiment results validate the method presented here, showing that this method can achieve long-term continuous tracking for multiple vessels, with an average correct track segment association rate of over 91.2%, which is better than the tracking performance of conventional algorithms, especially when the vessels are in dense channels and strong clutter/interference area.

An aerial intelligent relay-road side unit (AIR-RSU) framework for modern intelligent transportation system

In transportation environment, Intelligent Transportation System (ITS) is being used in various stages of every transport mode (road, rail, air & sea). The aim of intelligent transportation system is to provide the On-Road Vehicles (ORVs) with reliable Internet access connectivity, emergency notification and other safety information via infrastructure-based Road Side Units (RSUs). In the past decade, intelligent transportation system has been started in many countries where RSUs are provided with all type of communication services to connect, compute, communicate and share the information between its neighboring RSUs and on-road vehicles. However, there are certain diffcuilties in which the information cannot be delivered reliably such as Line Of Sight (LOS) communication with on-road vehicles, packet loss, latency, retransmission overhead and multiple target tracking of objects over time-to-time etc. Thus, the intelligent transportation system becomes a complex network of entities. Modern intelligent transportation systems use relay-RSU nodes (i.e., small sized Unmanned Aerial Vehicles) also referred as RRSU node network deployed between satellites and on-road vehicles as they provide many advanages like coverage, reliability and information sharing without depending more on knowledge provided by satellites. In this paper, a novel Aerial Intelligent Relay-Road Side Unit (AIR-RSU) framework has been proposed to determine the network connectivity status level and analyze the communication link stability among RRSUs, and between RRSUs and on-road vehicles for every time instants Ti based on certain performance metrics discussed in this paper. The proposed AIR-RSU framework holds the precise status/information about the network connectivity needed in the transportation environment carefully and effectively accounting the frequent changes in positions. The performance of the proposed AIR-RSU framework is evaluated using rectangle waypoint mobility model for on-road vehicles and steady state random waypoint mobility model for RRSU nodes, which measures the network connectivity status and the performance metrics periodically. Furthermore, a comparative analysis is done to both individual nodes and entire network in terms of network load.

Multiple Sensor Linear Multi-Target Integrated Probabilistic Data Association for Ultra-Wide Band Radar

Ultra-Wide Band (UWB) radar has a number of advantages of resolving multipath, exceptional spatial resolution, and ranging performance. However, several difficulties are confronted for multiple target tracking using UWB radars such as clutter signals which contaminate target signals, and unidentified number and behavior of the targets. Hence, this paper presents to develop a multiple moving target tracking algorithm, consisting of preprocessing and multiple target tracking steps. In the preprocessing step, static clutter reduction and constant false alarm rate (CFAR) detection extract the target candidate range measurements from each UWB radar. Then, two multiple target tracking (MTT) steps are developed: range- based MTT and position-based MTT. The range-based MTT is mainly based on existing linear multi-target integrated probabilistic data association (LM-IPDA) from each UWB radar measurement. Then the outputs of each LM-IPDA are gathered in the positioning center to estimate the position of multiple targets. On the other hands, the position-based MTT is based on multiple sensor LM-IPDA (msLM-IPDA) as an accurate target tracking method for various uncertainties by improving the probabilistic model of LM-IPDA. The tracking performance of two MTT methods is investigated with both numerical simulation and experiments.

Optimal Resource Allocation for Asynchronous Multiple Targets Tracking in Heterogeneous Radar Networks

In this paper, two optimal resource allocation schemes are developed for asynchronous multiple targets tracking (MTT) in heterogeneous radar networks. The key idea of heterogeneous resource allocation (HRA) schemes is to coordinate the heterogeneous transmit resource (transmit power, dwell time, etc.) of different types of radars to achieve a better resource utilization efficiency. We use the Bayesian Cramer-Rao lower bound (BCRLB) as a metric function to quantify the target tracking performance and build the following two HRA schemes: For a given system resource budget: (1) Minimize the total resource consumption for the given BCRLB requirements on multiple targets and (2) maximize the overall MTT accuracy. Instead of updating the state of each target recursively at different measurement arrival times, we combine multiple asynchronous measurements into a single composite measurement and use it as an input of the tracking filter for state estimation. In such a case, target tracking BCRLB no longer needs to be recursively calculated, and thus, we can formulate the HRA schemes as two convex optimization problems. We subsequently design two efficient methods to solve these problems by exploring their unique structures. Simulation results demonstrate that the HRA processes can either provide a smaller overall MTT BCRLB for given resource budgets or require fewer resources to establish the same tracking performance for multiple targets.

Tracking Multiple Maneuvering Targets Hidden in the DBZ Based on the MM-GLMB Filter

Tracking multiple maneuvering targets hidden in the Doppler blind zone (DBZ) is a challenging problem. To overcome the complicated problem, we proposed a tracker based on the multiple model probability hypothesis density (MM-PHD) filter. However, the PHD filter is only the first-order moment approximation of the multi-target Bayesian filter, and it cannot output track labels in principle. In order to improve the tracking performance, another novel tracker is proposed in this paper. To track multiple maneuvering targets, the proposed tracker is built on the latest multiple model generalized labeled multi-Bernoulli (MM-GLMB) filter. Moreover, it incorporates the minimum detectable velocity (MDV) to suppress the DBZ masking. Finally, a measurement-driven adaptive track initiation is introduced to address the fixed track initiation problem of the standard MM-GLMB filter. It is demonstrated through numerical examples that the proposed tracker outperforms the existing work significantly, especially in terms of both accuracy and robustness of cardinality estimation.

Antenna Array Aperture Resource Management of Opportunistic Array Radar for Multiple Target Tracking

In this paper, an antenna array aperture resource management scheme of opportunistic array radar (OAR) based on chance-constraint programming (CCP) is proposed for multiple target tracking (MTT). In the multi-target tracking scenario, multiple beams need to be synthesized to illuminate the targets. According to the predicted distance of targets, the OAR can intelligently segment the antenna array aperture, and select the optimal combination of array elements to synthesize the satisfactory beams with the element-level antenna steering. Owing to the unique array arrangement and work mode of opportunistic array, the element number in working modes is uncertain. To represent the uncertainty, a fuzzy CCP model is introduced. The element number in working modes is viewed as a fuzzy variable. The maximum tracking error, which is denoted by Bayesian Cramer-Rao lower bound(BCRLB), of the targets is regarded as the objective function. The fuzzy simulation algorithm is embedded into genetic algorithm (GA) to compose a hybrid intelligent optimization algorithm to solve the CCP problem. The simulation results verify the validity and practicability of the antenna array aperture resource allocation scheme.

Adaptive Sensor Scheduling and Resource Allocation in Netted Collocated MIMO Radar System for Multi-Target Tracking

In this paper, an effective solution for adaptive sensor scheduling integrated with power and bandwidth allocation is developed for centralized multiple target tracking (MTT) in the netted collocated multiple-input multiple-output (C-MIMO) radar system. By incorporating a modified particle filter, the predicted posterior Cramer-Rao lower bound (PCRLB) is calculated. Aiming at improving the sum of target tracking accuracy of location, the predicted PCRLBs for the location of multiple targets is derived as the optimization metric. Owing to the dynamic sensor scheduling is NP-hard, a convex relaxation technique is adopted for the sensor scheduling. Taking account of the problem of power and bandwidth allocation is non-convex, a joint convex relaxation technique and cycle minimizer algorithm is adopted to convert the non-convex optimization problem into a series of convex problems. After the results of resource allocation are fed back to each node, an adaptive closed-loop system is established. Numerical results demonstrate that the location tracking accuracy can be improved efficiently by the proposed algorithm.

Predikcija sudara aviona na osnovu binomne raspodele

Introduction/purpose: Based on the binomial distribution of the probability density function, a new probabilistic model for aircraft position predicting is presented in this paper. Methods: The proposed algorithm is composed of three different blocks: Data Association, Tracking/Hybrid State Estimation and Calculation of Probability of Conflict. The information about aircraft current positions and flight plans is used to derive an algorithm for detecting possible conflicts between aircraft. The situations where aircraft may come closer than a certain distance to one another are predicted with high probability. The position estimate and indeterminacy refer to target association when two tracks fall in a validation region by using the Probabilistic Data Association Filter. Results: An efficient collision detection algorithm is designed and tested for a lot of multiple target tracking. Conclusion: The simulation results of aircraft conflict prevention in two trajectory scenarios verify the efficiency of the proposed algorithm.

Distributed Sensor Management Based on Target Losing Probability for Maneuvering Multi-Target Tracking

This paper proposed a distributed sensor management method in the constraint of target losing probability for maneuvering multi-target tracking. Three main contributions existed in this paper, including adding the emission interception risk to the sensor management objective function, analyzing actual working modes of radars and regarding the target losing probability as one of constraints in sensor management. Two kind of radar working modes were introduced. They were the single target tracking mode and the multiple target tracking mode, leading to different kinds of sensor management methods. Then two sensor management models, containing sensor management in a large defending scope and in a small defending scope, were built. After that, the distributed sensor management process was introduced. Finally, simulations were conducted to show the effectiveness and efficiency of the proposed sensor management methods. By applying the proposed sensor management method, targets couldn't be easily lost and radar could be well protected at the same time.

Auxiliary-Filter-Free Incompressible Particle Flow Filtering Using Direct Estimation of the Log-Density Gradient with Target Tracking Examples

This paper presents an incompressible particle flow filtering method that does not require an auxiliary filter by estimating log-density gradients directly from particles. Particle flow filter (PFF) is likely to avoid particle impoverishment and degeneracy problems that occur in particle filters because particles themselves move toward desired density to perform measurement updates. There are various implementation forms for PFF depending on the assumptions made about the flow. This paper deals with PFF using incompressible flow. Incompressible PFF requires the log-density gradient to calculate the flow. The well-known gradient estimation method for incompressible PFF is a finite difference method collaborating with k nearest neighbors(kNN) method. Since this method requires the prior knowledge about the prior density value in each particle, it is necessary to use an auxiliary filter or a density estimation technique. As a result, the performance of an auxiliary filter or a density estimation technique can directly affect the PFF performance, and the finite difference method is more likely to be inaccurate than directly estimating the log-density gradient. Therefore, this paper presents a PFF structure applying least-squares log-density gradient (LSLDG) method that estimates the log-density gradient directly from particles. In order to verify the performance of the presented structure, this paper performs both single and multiple target tracking simulations. Simulation results demonstrate that the presented structure has a relatively good estimation performance and works more robustly for various situations.

Indoor Target Tracking Using Dual-Frequency Continuous-Wave Radar Based on the Range-Only Measurements

A dual-frequency continuous-wave (DF-CW) radar is a cost-effective device for multiple target tracking in indoor environments. The traditional DF-CW radar adopts a quasi-monostatic structure to acquire the target range and direction of arrival (DOA), to position the target. However, the traditional DF-CW radar usually suffers from the drawbacks of low positioning accuracy, biased target information, and severe ambiguous frequency interference. For overcoming these drawbacks, the traditional DF-CW radar structure is modified, and an improved target tracking algorithm, which is based only on the range measurements, is proposed in this article. Compared with the traditional DF-CW radar-based detection, the proposed algorithm can achieve not only higher positioning accuracy, reveal more abundant and precise target information, but can also help to suppress the ambiguous frequency interference, which is of great importance in the multiple target tracking scenarios. Besides, the proposed DF-CW radar can also effectively reduce the coupling interference, thus contributing to the improvement of the dynamic range of the radar. A series of experiments are conducted to demonstrate the effectiveness of the proposed algorithm, and the obtained results illustrate a better performance of the proposed algorithm compared with the traditional algorithms.

Construction of intelligent building sky-eye system based on multi-camera and speech recognition

The use of advanced science and technology to build a smart new community is the need of today’s social development. Smart communities use IoT technology and cloud computing technology to make life in the community more intelligent. Smart buildings can make better use of resources and can save energy and at the same time serve community residents efficiently and conveniently. The development of smart buildings has also led to the progress of smart cities. The degree of intelligence of the community is a manifestation of the level of development of smart cities. Based on the single-camera multi-target tracking system, due to the limitations of the camera’s field of view, it is often impossible to track the moving target, and it is difficult to solve the target occlusion problem in complex scenes. We can use multiple cameras to obtain the information of the target in different perspectives and use the corresponding relationship of the same target in different views to achieve continuous tracking of multiple targets. In this paper, we propose a smart building sky-eye system based on multi-camera recognition technology. The simulation results show that the proposed method has higher efficiency.

Underwater Target Tracking Using Forward-Looking Sonar for Autonomous Underwater Vehicles.

In the scenario where autonomous underwater vehicles (AUVs) carry out tasks, it is necessary to reliably estimate underwater-moving-target positioning. While cameras often give low-precision visibility in a limited field of view, the forward-looking sonar is still an attractive method for underwater sensing, which is especially effective for long-range tracking. This paper describes an online processing framework based on forward-looking-sonar (FLS) images, and presents a novel tracking approach based on a Gaussian particle filter (GPF) to resolve persistent multiple-target tracking in cluttered environments. First, the character of acoustic-vision images is considered, and methods of median filtering and region-growing segmentation were modified to improve image-processing results. Second, a generalized regression neural network was adopted to evaluate multiple features of target regions, and a representation of feature subsets was created to improve tracking performance. Thus, an adaptive fusion strategy is introduced to integrate feature cues into the observation model, and the complete procedure of underwater target tracking based on GPF is displayed. Results obtained on a real acoustic-vision AUV platform during sea trials are shown and discussed. These showed that the proposed method is feasible and effective in tracking targets in complex underwater environments.

A K-Band 3-D Printed Focal-Shifted Two-Dimensional Beam-Scanning Lens Antenna With Nonuniform Feed

A K-band (18-26.5 GHz) two-dimensional (2-D) beam scanning antenna is proposed. The hyperbolic lens is fed by an array of focal-shifted horns with nonuniform aperture. The feed at the focal point of the lens generates the boresight vertical beam, while the off-focal feeds are responsible for tilted beams. The nonuniform horn aperture helps to mitigate the gain decrement from the lens aperture tapering. It is fabricated taking full advantage of dielectric and metallic 3-D printing technologies. The lens is printed by stereolithography apparatus (SLA) in resin. The feed horn array is printed by selective laser melting (SLM). Process-wise, it features low cost, short turnaround time, and the capability to implement complex geometries versus traditional computer numerical control machining and mold casting. Simulation and measurement indicate that the antenna features desirable matching below -15 dB and mutual coupling between adjacent port below -25 dB. The gain is above 23 dBi across the K-band. Multiple beams at ±32°, ±12°, and 0° are observed on E- and H-planes. The proposed design can either function as multibeam antenna or a beam-scanning antenna together with switches. It is a capable candidate for applications such as multiple target tracking and wide region monitoring.