Multiple Target Tracking System Research Articles

Multibeam Forming With Arbitrary Radiation Power Ratios Based on a Conformal Amplitude–Phase-Controlled Metasurface

In this communication, we present a novel amplitude–phase-controlled metasurface (APCMS), which features a full <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\pi $ </tex-math></inline-formula> phase coverage and amplitude variation from 0 to 1 for transmissive waves. By alternately cascading three ultrathin printed circuit boards (PCBs) and two curved 3-D-printed substrates, the proposed APCMS is designed to be a cambered surface and can be applied to certain space-constrained scenarios. Different from phase-only controlled metasurfaces (MSs), with the ability to impart a new degree of freedom, we use the APCMS to construct a spatial power divider by superimposing multiple aperture fields assigned to amplitude weighting coefficients. As a proof-of-concept example, two quarter-cylindrical APCMS-based spatial power dividers are designed to generate equal-power and unequal-power triple-beam radiation patterns at different and same elevation angles, respectively. A prototype of an equal-power divider is fabricated and measured to verify the proposed strategy. The results show that upon illumination of a feed horn, the maximum difference among the realized gains of the three beams is less than 0.9 dB at 12 GHz. Their 3 dB gain bandwidths are 26.7%, 19.5%, and 23.3%. Our study holds potential applications in multibeam shaping and multiple target tracking systems.

Passive Sonar Multiple-Target Tracking with Nonlinear Doppler and Bearing Measurements Using Multiple Sensors

The major advantage of the passive multiple-target tracking is that the sonars do not emit signals and thus they can remain covert, which will reduce the risk of being attacked. However, the nonlinearity of the passive Doppler and bearing measurements, the range unobservability problem, and the measurement to target data association uncertainty make the passive multiple-target tracking problem challenging. To deal with the target to measurement data association uncertainty problem from multiple sensors, this paper proposed a batch recursive extended Rauch-Tung-Striebel smoother- (RTSS-) based probabilistic multiple hypothesis tracker (PMHT) algorithm, which can effectively handle a large number of passive measurements including clutters. The recursive extended RTSS which consists of a forward filter and a backward smoothing is used to deal with the nonlinear Doppler and bearing measurements. The target range unobservability problem is avoided due to using multiple passive sensors. The simulation results show that the proposed algorithm works well in a passive multiple-target tracking system under dense clutter environment, and its computing cost is low.

Convex Variational Inference for Multi-Hypothesis Fractional Belief Propagation Based Data Association in Multiple Target Tracking Systems

The success of Multi-hypothesis tracking(MHT) lies in the use of multiple scans, which can often yield improved tracking performance over single scan based data association methods in radar-centric multi-target tracking(MTT) systems. However, with the increase of the number of targets associated with measurements, there is an exponentially increasing need for formulating potential hypotheses, of which the computational cost may be prohibitively expensive. In this paper, a multi-hypothesis fractional belief propagation (MHFBP) based data association algorithm is proposed by the use of a probabilistic graph model for both the previous trajectories and the current measurements. To achieve this idea, there are two main steps. First, the trajectory-related indicators associated state variables are created by making use of a convex fractional free energy (FFE)function. Second, the convex optimization algorithm is used for the objective function and the fractional belief propagation (FBP) is exploited to obtain the best marginal belief of measurement for target association. In this way, incorrect hypotheses with extremely low probability can be eliminated. Finally, the proposed method is applied to multiple scenarios for MTT by indoor radar system. From the results, we can observe that it provides higher tracking performance compared with the classical MHT, feature-aided MHT(FA-MHT) and MHT-belief propagation (MHT-BP). In addition, we see that the computational burden of the proposed method is reduced significantly to avoid the phenomenon of exponential growth with increasing the number of targets.

Passive Tracking of Multiple Underwater Targets in Incomplete Detection and Clutter Environment.

A major advantage of the use of passive sonar in the tracking multiple underwater targets is that they can be kept covert, which reduces the risk of being attacked. However, the nonlinearity of the passive Doppler and bearing measurements, the range unobservability problem, and the complexity of data association between measurements and targets make the problem of underwater passive multiple target tracking challenging. To deal with these problems, the cardinalized probability hypothesis density (CPHD) recursion, which is based on Bayesian information theory, is developed to handle the data association uncertainty, and to acquire existing targets’ numbers and states (e.g., position and velocity). The key idea of the CPHD recursion is to simultaneously estimate the targets’ intensity and the probability distribution of the number of targets. The CPHD recursion is the first moment approximation of the Bayesian multiple targets filter, which avoids the data association procedure between the targets and measurements including clutter. The Bayesian-filter-based extended Kalman filter (EKF) is applied to deal with the nonlinear bearing and Doppler measurements. The experimental results show that the EKF-based CPHD recursion works well in the underwater passive multiple target tracking system in cluttered and noisy environments.

A Track-Before-Detect Strategy Based on Sparse Data Processing for Air Surveillance Radar Applications

In this paper we consider the tracking problem of a moving target competing against noise and clutter in a surveillance radar scenario. For a single array-antenna multiple-target tracking system and according to the Track-Before-Detect paradigm, we present a novel approach based on a three-stage processing chain that involves the Sparse Learning via Iterative Minimization algorithm, the k-means clustering method and the ad hoc detector by exploiting the sparse nature of the operating scenario. Under the latter assumption, the detection strategy declares the presence of targets subsequently to the retrieval of their corresponding tracks performed by jointly processing the received echoes of multiple consecutive radar scans. Simulation results show that the proposed approach is able to provide good tracking and detection capabilities for different multiple target trajectories with low Signal-to-Interference-plus-Noise ratio and results in providing advantages when compared to a number of other reference Track-Before-Detect strategies based on sparse data processing techniques.

A Multiple Target Positioning and Tracking System Behind Brick-Concrete Walls Using Multiple Monostatic IR-UWB Radars.

Recognizing and tracking the targets located behind walls through impulse radio ultra-wideband (IR-UWB) radar provides a significant advantage, as the characteristics of the IR-UWB radar signal enable it to penetrate obstacles. In this study, we design a through-wall radar system to estimate and track multiple targets behind a wall. The radar signal received through the wall experiences distortion, such as attenuation and delay, and the characteristics of the wall are estimated to compensate the distance error. In addition, unlike general cases, it is difficult to maintain a high detection rate and low false alarm rate in this through-wall radar application due to the attenuation and distortion caused by the wall. In particular, the generally used delay-and-sum algorithm is significantly affected by the motion of targets and distortion caused by the wall, rendering it difficult to obtain a good performance. Thus, we propose a novel method, which calculates the likelihood that a target exists in a certain location through a detection process. Unlike the delay-and-sum algorithm, this method does not use the radar signal directly. Simulations and experiments are conducted in different cases to show the validity of our through-wall radar system. The results obtained by using the proposed algorithm as well as delay-and-sum and trilateration are compared in terms of the detection rate, false alarm rate, and positioning error.

Design of Multiple-Target Tracking System on Heterogeneous System-on-Chip Devices

Advanced driver-assistance systems (ADAS) generally embrace heterogeneous platforms consisting of central processing units and field-programmable gate arrays (FPGAs) to achieve higher performance and energy efficiency. The multiple-target tracking (MTT) system is an important component in most ADAS and is particularly suited for heterogeneous implementation to improve responsiveness. However, the platform heterogeneity necessitates numerous design decisions to obtain the optimal application partitioning between the processor and the FPGA. In this paper, multiple configurations of the MTT application have been investigated on the Xilinx Zynq commercial heterogeneous platform. An extensive design space exploration was performed to recommend the optimal configuration with high performance and energy efficiency. A reduction of more than 65%, both in execution time and energy consumption, has been obtained by the utilization of the heterogeneous architecture. Finally, an analytical model is proposed to estimate execution time and energy consumption to enable a rapid exploration of the different configurations and predict the performance that can be expected with future system-on-chip (SoC) platforms and radar sensors in ADAS.

Multitarget detection and tracking for through-the-wall radars

Through-the-wall radars (TWR) play an important role in various applications such as homeland security, search and rescue, and defense. One main challenge of performing automatic TWR data processing including detection and tracking is the strong reverberation and reflections from the walls. In the work presented here, we propose a practical and effective multiple target detection and tracking system for TWR. While background subtraction and envelope detection are used to reduce the false detected targets, an effective multiple target tracker is employed to further suppress the TWR false tracks. Using an experimental TWR, it is shown here that the proposed TWR data processing unit can effectively reduce false targets and false tracks and produce a good final surveillance picture for tracking human movement behind the walls.

Target estimation algorithm design using quantity data and target feature

The estimation algorithm plays an important role in a radar tracking system. An improved estimation approach using both quantity data and target feature is investigated in this article. The advantage of this approach is that the system will have better estimation based on more target information. A data association denoted one-step conditional maximum likelihood algorithm is applied to match between radar measurements and existing target tracks. Moreover, an adaptive estimator is applied to combine the quantity data and target feature for estimation problems. According to the simulation results, this approach can enhance the performance of multiple-target tracking systems.

Object Tracking by Multiple State Management and Eigenbackground Segmentation

This paper presents a multiple target tracking system through a fixed video camera, based on approaches found in literature. The proposed system is composed of three steps: foreground identification through background subtraction techniques; object association through color, area and centroid position matching, by using the Kalman filter to estimate the object’s position in the next frame; object classification according to an object management system. The obtained results showed that the proposed tracking system was able to recognize and track objects in movement on videos, as well as dealing with occlusions and separations, while encouraging future studies in its application on real time security systems.

Deinterleaving pulse trains in unconventional circumstances using multiple hypothesis tracking algorithm

The main function of Electronic Support Measure (ESM) is to receive, measure and deinterleave pulses, and then identify alternative threat emitters. Among these processes, pulse deinterleaving is vitally important because dense electromagnetic environments could cause an ESM system to receive a seemingly random pulse stream consisting of interleaved pulse trains with high noise levels. Only when we segregate different radar pulse trains from the pulse stream can we proceed with further processing. Traditional deinterleaving algorithms have demonstrated instability in unconventional circumstances (such as agility of pulse repetition interval (PRI), large noise and jitter, missing of intercepted pulses). Based on the dynamic process of different emitters, a new Statistical Association Pulse Deinterleaving (SAPD) approach is proposed based on the Multiple Hypothesis Tracking (MHT) algorithm in Multiple Target Tracking system. Simulation results have shown that the proposed algorithm can successfully identify pulse trains with constant, jittered and staggered PRI, and provide much greater accuracy in PRI estimation and pulse classification than traditional algorithms, with the presence of large noise, frequency jitter, and many missing pulses.

A reconfigurable platform architecture for an automotive multiple-target tracking system

This paper reports on our progress in developing a dynamically reconfigurable processing platform for an automotive multiple-target tracking (MTT) system. In addition to motivating our work, we present an overview of our design, some preliminary results, and report on the status of our work.

Probabilistic data association applications to data fusion

We describe a new approach to fusion techniques in a multiple target tracking system for an infrared search and track (IRST) system operating in the mid- and long-wave infrared (IR) bands. The use of the two IR bands allows better performances in terms of detection probability, lower number of false tracks, and shorter time for track declaration. To properly merge data from the two sensors, an enhancement of the probabilistic data association (PDA) technique is introduced in the process. A simplification of the approach used brings back the algorithm to the well-known PDA, allowing its use in conjunction with the interacting multiple model (IMM) with increased tracking ability of the system.

Taxonomy of multiple target tracking methods

A concise summary of techniques for multiple target tracking is provided and their main characterics assessed qualitatively. The techniques have been catergorised into more than 35 different algorithmic types. A comparison chart is provided that lists each algorithm and categorises the processing scheme, data association mechanism, complexity scaling (with number of targets and with state dimension), overall complexity and a subjective performance figure. Although some recent filtering theory developments have been omitted, the survey should serve to demonstrate the large variety of 'classical' estimation theoretic algorithms already available for the design of multiple target tracking systems. A number of areas deserving of further study are identified in the concluding remarks.

Position estimation of three‐dimensional radars with bias errors

AbstractIn a multiple target tracking system combining several radars, the errors in the radar observation coordinates and in the radar position due to alignment of each radar significantly affect the tracking performance. In this paper it is assumed that the former error is negligible. The difference in the position of an aircraft observed simultaneously by the radars in question is linearly approximated. A method is proposed to estimate the bias error of the relative position in several three‐dimensional radars by means of a Kalman filter. When several radars are positioned far away, the reference direction and the absolute horizontal plane of the coordinate axis in reference to each radar are inclined due to the curvature of the earth, so that the bias error estimation accuracy is degraded. The slope of the coordinate axis due to the curvature of the earth is specifically corrected for estimation of the bias error of the relative radar position. Also, by means of simulation, the method is compared with that in which the sea surface altitude of an aircraft is calculated from the observed values (range and elevation angle) and the effect of the inclination of the coordinated axis is corrected. It is shown that the estimation accuracy is improved by 3.7 m for a radar range of 10 km and 19.5 m for 30 km. © 2004 Wiley Periodicals, Inc. Electron Comm Jpn Pt 1, 87(9): 64–74, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecja.10159

Multiple hypothesis tracking for multiple target tracking

Multiple hypothesis tracking (MHT) is generally accepted as the preferred method for solving the data association problem in modern multiple target tracking (MTT) systems. This paper summarizes the motivations for MHT, the basic principles behind MHT and the alternative implementations in common use. It discusses the manner in which the multiple data association hypotheses formed by MHT can be combined with multiple filter models, such as used by the interacting multiple model (IMM) method. An overview of the studies that show the advantages of MHT over the conventional single hypothesis approach is given. Important current applications and areas of future research and development for MHT are discussed.

A multisensor tracking system with an airborne sensor to mitigate the effect of cross‐range errors

AbstractIn this paper, we propose a multiple radar target tracking system that uses a mobile station, which is effective in reducing cross‐range errors. The mobile station is an airplane equipped with radar and tracks the target by radar while in flight. By controlling the motion of the mobile state according to the proposed algorithm, the beams from the radars to the moving target can be orthogonal to or nearly orthogonal to each other compared to a multiple radar target tracking system that uses only conventional stationary stations. Combining the measurements from radars placed so that their beams to the target are orthogonal or nearly orthogonal can lessen the effect of increases in the cross‐range errors and can obtain excellent tracking accuracy. Computer simulations compare the proposed method to a multiple radar target tracking system that used only conventional stationary stations and demonstrate its ability to obtain excellent tracking accuracy for distant targets. © 2002 Wiley Periodicals, Inc. Electron Comm Jpn Pt 1, 85(8): 1–12, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecja.1113

A “soft” assignment algorithm for multiple target tracking

Solving the assignment problem, i.e. correctly associating target measurements to tracks, is critical to the correct working of any multiple-target tracking system. Early assignment algorithms did not account for missing tracks and observations and thus had poor performance. In order to raise the performance level, the modern approach is to include missing measurements and missing tracks into the assignment algorithm. In this paper, we present an alternative approach to raising the performance level without explicitly including missing measurements and tracks. In the new assignment algorithm we generate a subset of high quality track-measurement pairs which optimally satisfy two criteria: (1) each track-measurement pair has a high likelihood of being correct and (2) the number of track-measurement pairs in the subset is close to the number of correct track-measurement pairs expected on the basis of the system parameters. The two criteria are defined using fuzzy membership functions and the optimal solution is found using “soft” decision making.

Comparison of Data Association Methods. Application to Road Obstacle Tracking Using a Doppler Effect Radar

The first purpose of the paper is to compare some data association methods, in order to build a multiple-target tracking system. The sensor is a Doppler effect radar, whose characteristics are known. The tracking is done by an extended Kalman filter.We present four classical algorithms : the Nearest Neighbour algorithm, The Probabilistic Data Association Filter, its extension to the multiple-target case, called the Joint Probabilistic Data Association Filter and finally the Multiple Hypothesis Filter. The comparison of their performance (robustness/autonomy/computation time) is done on simulated data, as well as on real data.Then we build a new method, which use two of the prior algorithms, in order to find the best compromise with regard to the application. This method, called Hybrid Filter, is also compared to the others.

Prediction of track purity and track accuracy in dense target environments

This note is concerned with performance prediction of multiple-target tracking systems, particularly in data-rich, high-target-density environments. Two simple but useful expressions are described: (i) the exponential law for predicting average probability of correct data association, and (ii) the linear-times-exponential law for calculating the effective measurement error variances with possible data misassociation. Estimation of two key multiple-target tracking performance measures, i.e., track purity and track accuracy, are then derived using these two laws. >