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1
Pitre, Ryan. "A Comparison of Multiple-Model Target Tracking Algorithms." ScholarWorks@UNO, 2004. http://louisdl.louislibraries.org/u?/NOD,168.
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Thesis (M.S.)--University of New Orleans, 2004.Title from electronic submission form. "A thesis ... in partial fulfillment of the requirements for the degree of Master of Science in the Department of Electrical Engineering."--Thesis t.p. Vita. Includes bibliographical references.
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Vestin, Albin, and Gustav Strandberg. "Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms." Thesis, Linköpings universitet, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160020.
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Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, instead of filtering to estimate the true target states. With this method, validation data for online, causal, target tracking algorithms can be obtained for all traffic scenarios without the need of extra sensors. We investigate how non-causal algorithms affects the target tracking performance using multiple sensors and dynamic models of different complexity. This is done to evaluate real-time methods against estimates obtained from non-causal filtering. Two different measurement units, a monocular camera and a LIDAR sensor, and two dynamic models are evaluated and compared using both causal and non-causal methods. The system is tested in two single object scenarios where ground truth is available and in three multi object scenarios without ground truth. Results from the two single object scenarios shows that tracking using only a monocular camera performs poorly since it is unable to measure the distance to objects. Here, a complementary LIDAR sensor improves the tracking performance significantly. The dynamic models are shown to have a small impact on the tracking performance, while the non-causal application gives a distinct improvement when tracking objects at large distances. Since the sequence can be reversed, the non-causal estimates are propagated from more certain states when the target is closer to the ego vehicle. For multiple object tracking, we find that correct associations between measurements and tracks are crucial for improving the tracking performance with non-causal algorithms.
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Naeem, Asad. "Single and multiple target tracking via hybrid mean shift/particle filter algorithms." Thesis, University of Nottingham, 2010. http://eprints.nottingham.ac.uk/12699/.
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This thesis is concerned with single and multiple target visual tracking algorithms and their application in the real world. While they are both powerful and general, one of the main challenges of tracking using particle filter-based algorithms is to manage the particle spread. Too wide a spread leads to dispersal of particles onto clutter, but limited spread may lead to difficulty when fast-moving objects and/or high-speed camera motion throw trackers away from their target(s). This thesis addresses the particle spread management problem. Three novel tracking algorithms are presented, each of which combines particle filtering and Kernel Mean Shift methods to produce more robust and accurate tracking. The first single target tracking algorithm, the Structured Octal Kernel Filter (SOK), combines Mean Shift (Comaniciu et al 2003) and Condensation (Isard and Blake 1998a). The spread of the particle set is handled by structurally placing the particles around the object, using eight particles arranged to cover the maximum area. Mean Shift is then applied to each particle to seek the global maxima. In effect, SOK uses intelligent switching between Mean Shift and particle filtering based on a confidence level. Though effective, it requires a threshold to be set and performs a somewhat inflexible search. The second single target tracking algorithm, the Kernel Annealed Mean Shift tracker (KAMS), uses an annealed particle filter (Deutscher et al 2000), but introduces a Mean Shift step to control particle spread. As a result, higher accuracy and robustness are achieved using fewer particles and annealing levels. Finally, KAMS is extended to create a multi-object tracking algorithm (MKAMS) by introducing an interaction filter to handle object collisions and occlusions. All three algorithms are compared experimentally with existing single/multiple object tracking algorithms. The evaluation procedure compares competing algorithms' robustness, accuracy and computational cost using both numerical measures and a novel application of McNemar's statistic. Results are presented on a wide variety of artificial and real image sequences.
4
Hadzagic, Melita. "Comparative analysis of the IMM-JVC and the IMM-JPDA algorithms for multiple-target tracking." Thesis, McGill University, 2001. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=32959.
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When tracking closely maneuvering targets, the critical role is played by both the chosen method of data association and the target-tracking algorithm. Without an effective association, state estimation is at risk. Without an efficient state prediction, the performance of an associator can be degraded. In developing an assignment strategy the crucial issue is whether to assign a track or observation as belonging uniquely to another observation or track, or to allow a track to be associated non-uniquely with multiple candidate observations.This thesis presents a comparative study of two assignment alternatives, namely the NC (unique association of a measurement to an existing track) and JPDA (nonunique association of a measurement to an existing track) algorithms. These assignment strategies were combined with an Interacting Multiple Model (IMM) positional estimator, which superiority over the other single scan algorithms has been largely documented. The respective tracking performance of the IMM-JVC and EV1M-JPDAF algorithms for multiple target tracking has been evaluated. After a detailed description of the IMM-JVC and IMM-JPDAF formalisms, and the IMM-JPDAF implementation issues, an analysis of the results of NC association compared to JPDA association is presented. Simulation results obtained on two scenarios involving two closely maneuvering aircraft confirm the superiority of the IMM-JVC.
5
Munir, Arshed. "Manoeuvring target tracking using different forms of the interacting multiple model algorithm." Thesis, University of Sussex, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.240430.
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Alat, Gokcen. "A Variable Structure - Autonomous - Interacting Multiple Model Ground Target Tracking Algorithm In Dense Clutter." Phd thesis, METU, 2013. http://etd.lib.metu.edu.tr/upload/12615512/index.pdf.
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Tracking of a single ground target using GMTI radar detections is considered. A Variable Structure-
Autonomous- Interactive Multiple Model (VS-A-IMM) structure is developed to address challenges
of ground target tracking, while maintaining an acceptable level computational complexity at the same
time. The following approach is used in this thesis: Use simple tracker structuresincorporate a priori information such as topographic constraints, road maps as much as possible
use enhanced gating techniques to minimize the eect of clutter
develop methods against stop-move motion and hide motion of the target
tackle on-road/o-road transitions and junction crossings
establish measures against non-detections caused by environment. The tracker structure is derived using a composite state estimation set-up that incorporate multi models and MAP and MMSE estimations. The root mean square position and velocity error performances of the VS-A-IMM algorithm are compared with respect to the baseline IMM and the VS-IMM methods found in the literature. It is observed that the newly developed VS-A-IMM algorithm performs better than the baseline methods in realistic conditions such as on-road/o-road transitions, tunnels, stops, junction crossings, non-detections.
7
Ege, Emre. "A Comparative Study Of Tracking Algorithms In Underwater Environment Using Sonar Simulation." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/2/12608866/index.pdf.
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Target tracking is one the most fundamental elements of a radar system. The aim of
target tracking is the reliable estimation of a target's true state based on a time history of noisy sensor observations. In real life, the sensor data may include substantial noise. This noise can render the raw sensor data unsuitable to be used directly. Instead, we must filter the noise, preferably in an optimal manner. For land, air and surface marine vehicles, very successful filtering methods are developed. However, because of the significant differences in the underwater propagation environment and the associated differences in the corresponding sensors, the successful use of similar principles and techniques in an underwater scenario is still an active topic of research. A comparative study of the effects of the underwater environment on a number of tracking algorithms is the focus of the present thesis. The tracking algorithms inspected are: the Kalman Filter, the Extended Kalman Filter and the Particle Filter. We also investigate in particular the IMM extension to KF and EKF filters. These algorithms are tested under several underwater environment scenarios.
8
Niedfeldt, Peter C. "Recursive-RANSAC: A Novel Algorithm for Tracking Multiple Targets in Clutter." BYU ScholarsArchive, 2014. https://scholarsarchive.byu.edu/etd/4195.
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Multiple target tracking (MTT) is the process of identifying the number of targets present in a surveillance region and the state estimates, or track, of each target. MTT remains a challenging problem due to the NP-hard data association step, where unlabeled measurements are identified as either a measurement of an existing target, a new target, or a spurious measurement called clutter. Existing techniques suffer from at least one of the following drawbacks: divergence in clutter, underlying assumptions on the number of targets, high computational complexity, time-consuming implementation, poor performance at low detection rates, and/or poor track continuity. Our goal is to develop an efficient MTT algorithm that is simple yet effective and that maintains track continuity enabling persistent tracking of an unknown number of targets. A related field to tracking is regression analysis, where the parameters of static signals are estimated from a batch or a sequence of data. The random sample consensus (RANSAC) algorithm was developed to mitigate the effects of spurious measurements, and has since found wide application within the computer vision community due to its robustness and efficiency. The main concept of RANSAC is to form numerous simple hypotheses from a batch of data and identify the hypothesis with the most supporting measurements. Unfortunately, RANSAC is not designed to track multiple targets using sequential measurements.To this end, we have developed the recursive-RANSAC (R-RANSAC) algorithm, which tracks multiple signals in clutter without requiring prior knowledge of the number of existing signals. The basic premise of the R-RANSAC algorithm is to store a set of RANSAC hypotheses between time steps. New measurements are used to either update existing hypotheses or generate new hypotheses using RANSAC. Storing multiple hypotheses enables R-RANSAC to track multiple targets. Good tracks are identified when a sufficient number of measurements support a hypothesis track. The complexity of R-RANSAC is shown to be squared in the number of measurements and stored tracks, and under moderate assumptions R-RANSAC converges in mean to the true states. We apply R-RANSAC to a variety of simulation, camera, and radar tracking examples.
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Day, Nathalie Anna. "Significant measurements of a multiple target tracking system utilizing munkre's algorithm as a correlation scheme." Master's thesis, University of Central Florida, 1988. http://digital.library.ucf.edu/cdm/ref/collection/RTD/id/72470.
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University of Central Florida College of Engineering ThesisThis thesis presents and discusses the principles of multiple target tracking. A simulation written in Turbo Pascal provides the results of using a modified version of Munkre's algorithm for correlating targets with observations. The number and types of measurments necessary to obtain acceptable results are examined. The measurements under scrutiny are range, range rate, azimuth angle and elevation angle. A track-while-scan system is assumed and the nearest neighbor correlation scheme as well as rectangular gating are used for association.
M.S.
Masters
Engineering
Engineering
79 p.
vi, 79 leaves, bound : ill. ; 28 cm.
10
Sahin, Mehmet Alper. "Performance Optimization Of Monopulse Tracking Radar." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/2/12605364/index.pdf.
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An analysis and simulation tool is developed for optimizing system parameters of the monopulse target tracking radar and observing effects of the system parameters on the performance of the system over different scenarios.
A monopulse tracking radar is modeled for measuring the performance of the radar with given parameters, during the thesis studies. The radar model simulates the operation of a Class IA type monopulse automatic tracking radar, which uses a planar phased array. The interacting multiple model (IMM) estimator with the Probabilistic Data Association (PDA) technique is used as the tracking filter. In addition to modeling of the tracking radar model, an optimization tool is developed to optimize system parameters of this tracking radar model. The optimization tool implements a Genetic Algorithm (GA) belonging to a GA Toolbox distributed by
Department of Automatic Control and System Engineering at University of Sheffield.
The thesis presents optimization results over some given optimization scenarios and concludes on effect of tracking filter parameters, beamwidth and dwell interval for the confirmed track.
11
Turkcu, Ozlem. "Development Of An Electronic Attack (ea) System In Multi&." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12609045/index.pdf.
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In this study, an expert system based EA and tracking system is developed
and the performances of these systems are optimized.
Tracking system consists of a monopulse tracking radar and a Multiple
Hypothesis Tracking (MHT) algorithm. MHT is modelled as a
measurement‐
oriented approach, which is capable of initiating tracks. As each measurement is received, probabilities are calculated for the hypotheses and target states are estimated using a Kalman filter. Range Gate Pull-Off (RGPO) is selected as an EA technique to be developed because it is accepted to be the primary deception technique employed against tracking radar. Two modes of RGPO technique
linear and parabolic, according to time delay controller are modelled. Genetic Algorithm (GA) Toolbox of MATLAB is used for the optimization of these systems over some predetermined scenarios. It is observed that the performance of the tracking radar system is improved significantly and successful tracking is achieved over all given scenarios, even for closely spaced targets. RGPO models are developed against this improved tracking performance and deception of tracking radar is succeeded for all given target models.
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Yilmaz, Mehmet. "Multiple Target Tracking Using Multiple Cameras." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/2/12609477/index.pdf.
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Video surveillance has long been in use to monitor security sensitive areas such as banks, department stores, crowded public places and borders. The rise in computer speed, availability of cheap large-capacity storage devices and high speed network infrastructure enabled the way for cheaper, multi sensor video surveillance systems. In this thesis, the problem of tracking multiple targets with multiple cameras has been discussed. Cameras have been located so that they have overlapping fields of vision. A dynamic background-modeling algorithm is described for segmenting moving objects from the background, which is capable of adapting to dynamic scene changes and periodic motion, such as illumination change and swaying of trees. After segmentation of foreground scene, the objects to be tracked have been acquired by morphological operations and connected component analysis. For the purpose of tracking the moving objects, an active contour model (snakes) is one of the approaches, in addition to a Kalman tracker. As the main tracking algorithm, a rule based tracker has been developed first for a single camera, and then extended to multiple cameras. Results of used and proposed methods are given in detail.
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Kharbouch, Mohamed M. "Some investigations of multiple target tracking." Thesis, University of Sussex, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.290997.
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Trailović, Lidija. "Ranking and optimization of target tracking algorithms." online access from Digital Dissertation Consortium access full-text, 2002. http://libweb.cityu.edu.hk/cgi-bin/er/db/ddcdiss.pl?3074810.
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Benko, Matej. "Hledaní modelů pohybu a jejich parametrů pro identifikaci trajektorie cílů." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-445467.
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Táto práca sa zaoberá odstraňovaním šumu, ktorý vzniká z tzv. multilateračných meraní leteckých cieľov. Na tento účel bude využitá najmä teória Bayesovských odhadov. Odvodí sa aposteriórna hustota skutočnej (presnej) polohy lietadla. Spolu s polohou (alebo aj rýchlosťou) lietadla bude odhadovaná tiež geometria trajektórie lietadla, ktorú lietadlo v aktuálnom čase sleduje a tzv. procesný šum, ktorý charakterizuje ako moc sa skutočná trajektória môže od tejto líšiť. Odhad spomínaného procesného šumu je najdôležitejšou časťou tejto práce. Je odvodený prístup maximálnej vierohodnosti a Bayesovský prístup a ďalšie rôzne vylepšenia a úpravy týchto prístupov. Tie zlepšujú odhad pri napr. zmene manévru cieľa alebo riešia problém počiatočnej nepresnosti odhadu maximálnej vierohodnosti. Na záver je ukázaná možnosť kombinácie prístupov, t.j. odhad spolu aj geometrie aj procesného šumu.
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Hagfalk, Erik, and Ianke Erik Eriksson. "Vision Sensor Scheduling for Multiple Target Tracking." Thesis, Linköping University, Automatic Control, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-57717.
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This thesis considers the problem of tracking multiple static or moving targets with one single pan/tilt-camera with a limited field of view. The objective is to minimize both the time needed to pan and tilt the camera's view between the targets and the total position uncertainty of all targets. To solve this problem, several planning methods have been developed and evaluated by Monte Carlo simulations and real world experiments. If the targets are moving and their true positions are unknown, both their current and future positions need to be estimated in order to calculate the best sensor trajectory. When dealing with static and known targets the problem is reduced to a deterministic optimization problem.
The planners have been tested through experiments using a real camera mounted above a car track using toy cars as targets. An algorithm has been developed to detect the cars and associate the detections with the correct target.
The Monte Carlo simulations show that, in the case of static targets, there is a huge advantage to arrange the targets into groups to be able to view more than one target at the time. In the case of moving targets with estimated positions it can be concluded that if the objective is to minimize the error in the position estimation the best planning choice is to always move to the target with the highest position uncertainty.
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Ahmeda, Shubat Senoussi. "Adaptive target tracking algorithms for phased array radar." Thesis, University of Nottingham, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.336953.
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Lin, Horng-Jyh. "Investigations of manoeuvring target tracking using IMM algorithms." Thesis, University of Sussex, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.332662.
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Pablo, Rodriguez Juan Manuel. "Multiple Target Detection and Tracking in a Multiple Camera Network." Thesis, KTH, Kommunikationsnät, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-175884.
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Given synchronized video sequences from a number of cameras withoverlapping fields of view, the detection and tracking of a prioriunknownnumber of individuals entering a determined area is considered,showingthat a generative model can accurately follow the individuals and handleeffectively such problems as occlusions in each view independently. Theaim of this thesis is to implement the exchange of information betweenthe cameras where the detection and tracking processes take place. Theinputs are obtained from synchronized videos and the frames are takenindividually to treat them as independent images. The proposed algo-rithm was implemented in MATLAB and results obtained on a personalcomputer are presented. The results show that the algorithm achievesgood tracking accuracy, has relatively low computational complexity, andat the same time it allows to observe the communication requirementsbetween the cameras and the processing node.
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Nagarajan, Nishatha. "Target Tracking Via Marine Radar." University of Toledo / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1345125374.
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Leven, William Franklin. "Approximate Cramer-Rao Bounds for Multiple Target Tracking." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/10507.
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The main objective of this dissertation is to develop mean-squared error performance predictions for multiple target tracking. Envisioned as an approximate Cramer-Rao lower bound, these performance predictions allow a tracking system designer to
quickly and efficiently predict the general performance trends of a tracking system.
The symmetric measurement equation (SME) approach to multiple target tracking
(MTT) lies at the heart of our method. The SME approach, developed by Kamen
et al., offers a unique solution to the data association problem. Rather than deal directly with this problem, the SME approach transforms it into a nonlinear estimation
problem. In this way, the SME approach sidesteps report-to-track associations.
Developing performance predictions using the SME approach requires work in several areas: (1) extending SME tracking theory, (2) developing nonlinear filters for SME tracking, and (3) understanding techniques for computing Cramer-Rao error bounds in nonlinear filtering. First, on the SME front, we extend SME tracking theory by deriving a new set of SME equations for motion in two dimensions. We also develop
the first realistic and efficient method for SME tracking in three dimensions. Second,
we apply, for the first time, the unscented Kalman filter (UKF) and the particle filter
to SME tracking. Using Taylor series analysis, we show how different SME implementations affect the performance of the EKF and UKF and show how Kalman filtering degrades for the SME approach as the number of targets rises. Third, we explore the Cramer-Rao lower bound (CRLB) and the posterior Cramer-Rao lower bound (PCRB)
for computing MTT error predictions using the SME. We show how to compute performance predictions for multiple target tracking using the PCRB, as well as address confusion in the tracking community about the proper interpretation of the PCRB for tracking scenarios.
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Li, Jun Feng. "Sequential Monte Carlo methods for multiple target tracking." Thesis, University of Cambridge, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.612269.
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Tolman, Skyler. "Multiple Agent Target Tracking in GPS-Denied Environments." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/9047.
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Unmanned aerial systems (UAS) are effective for surveillance and monitoring, but struggle with persistent, long-term tracking, especially without GPS, due to limited flight time. Persistent tracking can be accomplished using multiple vehicles if one vehicle can effectively hand off the tracking information to another replacement vehicle. This work presents a solution to the moving-target handoff problem in the absence of GPS. The proposed solution (a) a nonlinear complementary filter for self-pose estimation using only an IMU, (b) a particle filter for relative pose estimation between UAS using a relative range (c) visual target tracking using a gimballed camera when the target is close to the handoff UAS, and (d) track correlation logic using Procrustes analysis to perform the final target handoff between vehicles. We present hardware results of the self-pose estimation and visual target tracking, as well as an extensive simulation result that demonstrates the effectiveness of our full system, and perform Monte-Carlo simulations that indicate a 97% successful handoff rate using the proposed methods.
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Ingersoll, Kyle. "Vision Based Multiple Target Tracking Using Recursive RANSAC." BYU ScholarsArchive, 2015. https://scholarsarchive.byu.edu/etd/4398.
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In this thesis, the Recursive-Random Sample Consensus (R-RANSAC) multiple target tracking (MTT) algorithm is further developed and applied to video taken from static platforms. Development of R-RANSAC is primarily focused in three areas: data association, the ability to track maneuvering objects, and track management. The probabilistic data association (PDA) filter performs very well in the R-RANSAC framework and adds minimal computation cost over less sophisticated methods. The interacting multiple models (IMM) filter as well as higher-order linear models are incorporated into R-RANSAC to improve tracking of highly maneuverable targets. An effective track labeling system, a more intuitive track merging criteria, and other improvements were made to the track management system of R-RANSAC. R-RANSAC is shown to be a modular algorithm capable of incorporating the best features of competing MTT algorithms. A comprehensive comparison with the Gaussian mixture probability hypothesis density (GM-PHD) filter was conducted using pseudo-aerial videos of vehicles and pedestrians. R-RANSAC maintains superior track continuity, especially in cases of interacting and occluded targets, and has fewer missed detections when compared with the GM-PHD filter. The two algorithms perform similarly in terms of the number of false positives and tracking precision. The concept of a feedback loop between the tracker and sensor processing modules is extensively explored; the output tracks from R-RANSAC are used to inform how video processing is performed. We are able to indefinitely detect stationary objects by zeroing out the background update rate of target-associated pixels in a Gaussian mixture models (GMM) foreground detector. False positive foreground detections are eliminated with a minimum blob area threshold, a ghost suppression algorithm, and judicious tuning of the R-RANSAC parameters. The ability to detect stationary targets also allows R-RANSAC to be applied to a class of problems known as stationary object detection. Additionally, moving camera foreground detection techniques are applied to the static camera case in order to produce measurements with a velocity component; this is accomplished by using sequential-RANSAC to cluster optical flow vectors of FAST feature pairs. This further improves R-RANSAC's track continuity, especially with interacting targets. Finally, a hybrid algorithm composed of R-RANSAC and the Sequence Model (SM), a machine learner, is presented. The SM learns sequences of target locations and is able to assist in data association once properly trained. In simulation, we demonstrate the SM's ability to significantly improve tracking performance in situations with infrequent measurement updates and a high proportion of clutter measurements.
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Dagnew, Tewodros Mulugeta <1988>. "Multiple Target Tracking As a Graph Transduction Game." Master's Degree Thesis, Università Ca' Foscari Venezia, 2013. http://hdl.handle.net/10579/3474.
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This thesis talks about a semi-supervised learning applied on tracking multiple people in a video surveillance scenarios as graph transduction based on the notion of game theoretic approach.
Graph transduction is a semi supervised learning technique that tries to do classification over a graph of labeled and unlabeled data points (i.e. the labeled nodes with zero entropy, and the unlabeled ones with maximum entropy); here the data points are the detected persons in each frame.
As we know, Videos are composed of frames and in each frame there are peoples. And using people detectors (this topic is another issue and it is out of the scope of this thesis), we can detect people. Then each picture of detected patches will be treated as a graph nodes
And there will be a similarity comparison between the nodes. In the beginning targets to be tracked will be labeled, and then the provided labels propagate to the unlabeled ones consistently which means the target will be tracked in each frame of the video.
The frame work is based on game theoretic notion. The transduction or information propagation is formulated in terms of a non-cooperative multi player game, where equilibrium is in a sense of consistent labeling of the data or assigning targets to each patches of the frames, which the video is composed of. And multiple targets can be tracked simultaneously.
It can be seen as a learning approach that considers the tracking problem as a semi supervised learning problem, where given few target samples, we look forward for searching target occurrences in the video stream. The people’s appearances are modeled by using covariance matrices on color and gradient information which lie on Riemannian manifolds. Experiments tested on some video datasets show promising good results.
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Mambelli, Iacopo. "Algoritmi per l'inseguimento di target multipli in sistemi radar distribuiti." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/4098/.
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Yagoob, Muhammad Moeen. "Computationally efficient algorithms for non-linear target tracking problems." Thesis, Imperial College London, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.499109.
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Zhao, Zhanlue. "Performance Appraisal of Estimation Algorithms and Application of Estimation Algorithms to Target Tracking." ScholarWorks@UNO, 2006. http://scholarworks.uno.edu/td/394.
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This dissertation consists of two parts. The first part deals with the performance appraisal of estimation algorithms. The second part focuses on the application of estimation algorithms to target tracking. Performance appraisal is crucial for understanding, developing and comparing various estimation algorithms. In particular, with the evolvement of estimation theory and the increase of problem complexity, performance appraisal is getting more and more challenging for engineers to make comprehensive conclusions. However, the existing theoretical results are inadequate for practical reference. The first part of this dissertation is dedicated to performance measures which include local performance measures, global performance measures and model distortion measure. The second part focuses on application of the recursive best linear unbiased estimation (BLUE) or lineae minimum mean square error (LMMSE) estimation to nonlinear measurement problem in target tracking. Kalman filter has been the dominant basis for dynamic state filtering for several decades. Beyond Kalman filter, a more fundamental basis for the recursive best linear unbiased filtering has been thoroughly investigated in a series of papers by Dr. X. Rong Li. Based on the so-called quasirecursive best linear unbiased filtering technique, the constraints of the Kalman filter Linear-Gaussian assumptions can be relaxed such that a general linear filtering technique for nonlinear systems can be achieved. An approximate optimal BLUE filter is implemented for nonlinear measurements in target tracking which outperforms the existing method significantly in terms of accuracy, credibility and robustness.
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Mauroy, Gilles Patrick. "Multiple target tracking using neural networks and set estimation." Diss., Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/13748.
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Clark, Daniel Edward. "Multiple target tracking with the probability hypothesis density filter." Thesis, Heriot-Watt University, 2006. http://hdl.handle.net/10399/161.
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Chu, Maurice Kyojin 1973. "Target breakup detection in the multiple hypothesis tracking formulation." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/10872.
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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.Includes bibliographical references (leaves 114-115).
by Maurice Kyojin Chu.
M.Eng.
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Pablo, Rodriguez Juan Manuel. "Multiple Target Detection and Tracking in aMultiple Camera Network." Thesis, KTH, Kommunikationsnät, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-175787.
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Given synchronized video sequences from a number of cameras withoverlapping fields of view, the detection and tracking of a priori unknownnumber of individuals entering a determined area is considered, showingthat a generative model can accurately follow the individuals and handleeffectively such problems as occlusions in each view independently. Theaim of this thesis is to implement the exchange of information betweenthe cameras where the detection and tracking processes take place. Theinputs are obtained from synchronized videos and the frames are takenindividually to treat them as independent images. The proposed algo-rithm was implemented in MATLAB and results obtained on a personalcomputer are presented. The results show that the algorithm achievesgood tracking accuracy, has relatively low computational complexity, andat the same time it allows to observe the communication requirementsbetween the cameras and the processing node.
33
Nguyen, Trang. "Comparison of Sampling-Based Algorithms for Multisensor Distributed Target Tracking." ScholarWorks@UNO, 2003. http://scholarworks.uno.edu/td/20.
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Nonlinear filtering is certainly very important in estimation since most real-world problems are nonlinear. Recently a considerable progress in the nonlinear filtering theory has been made in the area of the sampling-based methods, including both random (Monte Carlo) and deterministic (quasi-Monte Carlo) sampling, and their combination. This work considers the problem of tracking a maneuvering target in a multisensor environment. A novel scheme for distributed tracking is employed that utilizes a nonlinear target model and estimates from local (sensor-based) estimators. The resulting estimation problem is highly nonlinear and thus quite challenging. In order to evaluate the performance capabilities of the architecture considered, advanced sampling-based nonlinear filters are implemented: particle filter (PF), unscented Kalman filter (UKF), and unscented particle filter (UPF). Results from extensive Monte Carlo simulations using different configurations of these algorithms are obtained to compare their effectiveness for solving the distributed target tracking problem.
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Wu, Jiande. "Parallel Computing of Particle Filtering Algorithms for Target Tracking Applications." ScholarWorks@UNO, 2014. http://scholarworks.uno.edu/td/1953.
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Particle filtering has been a very popular method to solve nonlinear/non-Gaussian state estimation problems for more than twenty years. Particle filters (PFs) have found lots of applications in areas that include nonlinear filtering of noisy signals and data, especially in target tracking. However, implementation of high dimensional PFs in real-time for large-scale problems is a very challenging computational task.
Parallel & distributed (P&D) computing is a promising way to deal with the computational challenges of PF methods. The main goal of this dissertation is to develop, implement and evaluate computationally efficient PF algorithms for target tracking, and thereby bring them closer to practical applications. To reach this goal, a number of parallel PF algorithms is designed and implemented using different parallel hardware architectures such as Computer Cluster, Graphics Processing Unit (GPU), and Field-Programmable Gate Array (FPGA). Proposed is an improved PF implementation for computer cluster - the Particle Transfer Algorithm (PTA), which takes advantage of the cluster architecture and outperforms significantly existing algorithms. Also, a novel GPU PF algorithm implementation is designed which is highly efficient for GPU architectures. The proposed algorithm implementations on different parallel computing environments are applied and tested for target tracking problems, such as space object tracking, ground multitarget tracking using image sensor, UAV-multisensor tracking. Comprehensive performance evaluation and comparison of the algorithms for both tracking and computational capabilities is performed. It is demonstrated by the obtained simulation results that the proposed implementations help greatly overcome the computational issues of particle filtering for realistic practical problems.
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Lopez, Remy. "Développement d'une nouvelle algorithmie de localisation adaptée à l'ensemble des mobiles suivis par le système ARGOS." Phd thesis, INSA de Toulouse, 2013. http://tel.archives-ouvertes.fr/tel-00949319.
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Depuis 1978, le système ARGOS assure à l'échelle mondiale la collecte de données et la localisation de plateformes pour des applications liées au suivi d'animaux, à l'océanographie et à la sécurité maritime. La localisation exploite le décalage Doppler affectant la fréquence de porteuse des messages émis par les plateformes et réceptionnés par des satellites dédiés. Au cours des vingt dernières années, les puissances d'émission des plateformes se sont réduites pour des conditions d'utilisation toujours plus extrêmes, augmentant le nombre de localisations de moindre qualité. Paradoxalement, les utilisateurs ont cherché à identifier des comportements à des échelles de plus en plus petites. L'objectif de ce projet est de développer un algorithme de localisation plus performant dans le contexte actuel afin de remplacer le traitement temps réel historique basé sur un ajustement par moindres carrés. Un service hors ligne, permettant de déterminer des localisations encore plus précises, est proposé dans un second temps. Le problème est reformulé comme l'estimation de l'état d'un système dynamique stochastique, tenant compte d'un ensemble de modèles de déplacement admissibles pour les plateformes. La détermination exacte de la loi a posteriori de l'état présente alors une complexité exponentiellement croissante avec le temps. Le filtre "Interacting Multiple Model" (IMM) est devenu l'outil standard pour approximer en temps réel la loi a posteriori avec un coût de calcul constant. Pour des applications hors ligne, de nombreuses solutions sous-optimales de lissage multi-modèle ont aussi été proposées. La première contribution méthodologique de ce travail présente l'extension du cadre initial de l'IMM à un ensemble de modèles hétérogènes, c.-à-d. dont les vecteurs d'état sont de tailles et de sémantiques différentes. En outre, nous proposons une nouvelle méthode pour le lissage multi-modèle qui offre une complexité réduite et de meilleures performances que les solutions existantes. L'algorithme de localisation ARGOS a été réécrit en y incorporant le filtre IMM en tant que traitement temps réel et le lisseur multi-modèle comme service hors ligne. Une étude, menée sur un panel de 200 plateformes munies d'un récepteur GPS utilisé comme vérité terrain, montre que ces stratégies améliorent significativement la précision de localisation quand peu de messages sont reçus. En outre, elles délivrent en moyenne 30% de localisations supplémentaires et permettent de caractériser systématiquement l'erreur de positionnement.
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Iovenitti, Pio Gioacchino, and piovenitti@swin edu au. "Three-dimensional measurement using a single camera and target tracking." Swinburne University of Technology, 1997. http://adt.lib.swin.edu.au./public/adt-VSWT20060724.151747.
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This thesis involves the development of a three-dimensional measurement system
for digitising the surface of an object. The measurement system consists of a single
camera and a four point planar target of known size. The target is hand held, and is
used to probe the surface of the object being measured. The position of the target is
tracked by the camera, and the contact point on the object is determined. The vision
based digitising technique can be used in the industrial and engineering design fields
during the product development phase.
The accuracy of measurement is an important criterion for establishing the success
of the 3-D measurement system, and the factors influencing the accuracy are
investigated. These factors include the image processing algorithm, the intrinsic
parameters of the camera, the algorithm to determine the position, and various
procedural variables. A new iterative algorithm is developed to calculate position.
This algorithm is evaluated, and its performance is compared to that of an analytic
algorithm. Simple calibration procedures are developed to determine the intrinsic
parameters, and mathematical models are constructed to justify these procedures.
The performance of the 3-D measurement system is established and compared to
that of existing digitising systems.
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Rowe, Daniel. "Towards Robust Multiple-Target Tracking in Unconstrained Human-Populated Environments." Doctoral thesis, Universitat Autònoma de Barcelona, 2008. http://hdl.handle.net/10803/5786.
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Jiang, Lan. "Joint state and parameter learning for multiple target tracking models." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.709232.
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Sakamaki, Joshua Y. "Cooperative Estimation for a Vision-Based Multiple Target Tracking System." BYU ScholarsArchive, 2016. https://scholarsarchive.byu.edu/etd/6072.
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In this thesis, the Recursive-Random Sample Consensus (R-RANSAC) algorithm is applied to a vision-based, cooperative target tracking system. Unlike previous applications, which focused on a single camera platform tracking targets in the image frame, this work uses multiple camera platforms to track targets in the inertial or world frame. The process of tracking targets in the inertial frame is commonly referred to as geolocation.In practical applications sensor biases cause the geolocated target estimates to be biased from truth. The method for cooperative estimation developed in this thesis first estimates the relative rotational and translational biases that exist between tracks from different vehicles. It then accounts for the biases and performs the track-to-track association, which determines if the tracks originate from the same target. The track-to-track association is based on a sliding window approach that accounts for the correlation between tracks sharing common process noise and the correlation in time between individual estimation errors, yielding a chi-squared distribution. Typically, accounting for the correlation in time requires the inversion of a Nnx x Nnx covariance matrix, where N is the length of the window and nx is the number of states. Note that this inversion must occur every time the track-to-track association is to be performed. However, it is shown that by making a steady-state assumption, the inverse has a simple closed-form solution, requiring the inversion of only two nx x nx matrices, and can be calculated offline. Distributed data fusion is performed on tracks where the hypothesis test is satisfied. The proposed method is demonstrated on data collected from an actual vision-based tracking system.A novel method is also developed to cooperatively estimate the location and size of occlusions. This capability is important for future target tracking research involving optimized path planning/gimbal pointing, where a geographical map is unavailable. The method is demonstrated in simulation.
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Guner, Onur. "Evaluation Of Multi Target Tracking Algorithms In The Presence Of Clutter." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/2/12606406/index.pdf.
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ABSTRACT
EVALUATION OF MULTI TARGET TRACKING ALGORITHMS
IN THE PRESENCE OF CLUTTER
Güner, Onur M.S., Department of Electrical and Electronics Engineering Supervisor: Prof. Dr. Mustafa Kuzuoglu August 2005, 88 Pages This thesis describes the theoretical bases, implementation and testing of a multi target tracking approach in radar applications. The main concern in this thesis is the evaluation of the performance of tracking algorithms in the presence of false alarms due to clutter. Multi target tracking algorithms are composed of three main parts: track initiation, data association and estimation. Two methods are proposed for track initiation in this work. First one is the track score function followed by a threshold comparison and the second one is the 2/2 &
M/N method which is based on the number of detections. For data association problem, several algorithms are developed according to the environment and number of tracks that are of interest. The simplest method for data association is the nearest-neighbor data association technique. In addition, the methods that use multiple hypotheses like probabilistic data association and joint probabilistic data association are introduced and investigated. Moreover, in the observation to track assignment, gating is an important issue since it reduces the complexity of the computations. Generally, ellipsoidal gates are used for this purpose. For estimation, Kalman filters are used for state prediction and measurement update. In filtering, target kinematics is an important point for the modeling. Therefore, Kalman filters based on different target kinematic models are run in parallel and the outputs of filters are combined to yield a single solution. This method is developed for maneuvering targets and is called interactive multiple modeling (IMM). All these algorithms are integrated to form a multi target tracker that works in the presence (or absence) of clutter. Track score function, joint probabilistic data association (JPDAF) and interactive multiple model filtering are used for this purpose. Keywords: clutter, false alarms, track initiation, data association, gating, target kinematics, IMM, JPDAF
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Hammond, Victor W., Ralph L. Stegall, Dana F. Gumb, and William H. Wilson. "CONTROL OF MULTIPLE TARGET DRONES USING THE AN/MPS-39 MULTIPLE OBJECT TRACKING RADAR AND VEGA TARGET CONTROL SYSTEM." International Foundation for Telemetering, 1989. http://hdl.handle.net/10150/614539.
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International Telemetering Conference Proceedings / October 30-November 02, 1989 / Town & Country Hotel & Convention Center, San Diego, CaliforniaModern aircraft testing and training increasingly demand the use of multiple targets. A novel method to meet this requirement is to use the new AN/MPS-39 Multiple Object Tracking Radar (MOTR) with Vega Target Control System equipment. The AN/MPS-39 can be loosely described as the equivalent of ten AN/FPS-16 radars. This equivalency, due largely to the AN/MPS-39’s phased array antenna, immediately suggests the controlling of multiple target drones as an added capability to the radar’s basic and demonstrated function as a precision metric instrument. This paper demonstrates the adaptability of the AN/MPS-39 MOTR to the use of VTCS, thus exploiting the AN/MPS-39’s inherent capability to control multiple target drones simultaneously.
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Korkmaz, Yusuf. "Tracking Of Multiple Ground Targets In Clutter With Interacting Multiple Model Estimator." Master's thesis, METU, 2013. http://etd.lib.metu.edu.tr/upload/12615727/index.pdf.
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In this thesis study, single target tracking algorithms including IMM-PDA and IMM-IPDA algorithmsOptimal approaches in multitarget tracking including IMM-JPDA, IMM-IJPDA and IMM-JIPDA algorithms and an example of Linear Multi-target approaches in multitarget tracking including IMM-LMIPDA algorithm have been studied and implemented in MATLAB for comparison. Simulations were carried out in various realistic test scenarios including single target tracking, tracking of multiple targets moving in convoy fashion, two targets merging in a junction, two targets merging-departing in junctions and multitarget tracking under isolated tracks situations. RMSE performance, track loss and computational load evaluations were done for these algorithms under the test scenarios dealing with these situations. Benchmarkings are presented relying on these outcomes.
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White, Jacob Harley. "Real-Time Visual Multi-Target Tracking in Realistic Tracking Environments." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7486.
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This thesis focuses on visual multiple-target tracking (MTT) from a UAV. Typical state-of-the-art multiple-target trackers rely on an object detector as the primary detection source. However, object detectors usually require a GPU to process images in real-time, which may not be feasible to carry on-board a UAV. Additionally, they often do not produce consistent detections for small objects typical of UAV imagery.In our method, we instead detect motion to identify objects of interest in the scene. We detect motion at corners in the image using optical flow. We also track points long-term to continue tracking stopped objects. Since our motion detection algorithm generates multiple detections at each time-step, we use a hybrid probabilistic data association filter combined with a single iteration of expectation maximization to improve tracking accuracy.We also present a motion detection algorithm that accounts for parallax in non-planar UAV imagery. We use the essential matrix to distinguish between true object motion and apparent object motion due to parallax. Instead of calculating the essential matrix directly, which can be time-consuming, we design a new algorithm that optimizes the rotation and translation between frames. This new algorithm requires only 4 ms instead of 47 ms per frame of the video sequence.We demonstrate the performance of these algorithms on video data. These algorithms are shown to improve tracking accuracy, reliability, and speed. All these contributions are capable of running in real-time without a GPU.
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Krout, David Wayne. "Intelligent ping sequencing for multiple target tracking in distributed sensor fields /." Thesis, Connect to this title online; UW restricted, 2006. http://hdl.handle.net/1773/6045.
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Holsinger, Seth D. "Multiple Target Tracking Via Dynamic Point Clustering on a UAV Platform." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1552380066855365.
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Li, Lingjie Luo Zhi-Quan. "Data fusion and filtering for target tracking and identification /." *McMaster only, 2003.
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Bertozzi, Enrico. "Development of Reinforcement Learning Algorithms for Non-cooperative Target Localization and Tracking." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.
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The problem addressed in this thesis is to use swarm agents to find the optimal placement to reach optimal localization performance of a target node in a wireless sensor network scenario. Localization can be based on simply received signal strength (RSSI) and trilateration. To measure the accuracy of the localization process, geometric dilution of precision (GDOP) has been used. Trilateration is performed by mobile anchors that, in this work, will be supposed to be drones. Three anchors are used. The anchors are free to move in an environment represented by a grid. Each drone can assume a grid cell as location. To move from a cell to another there are five actions allowed. Each agent can move one cell square north, south, east, west or remain in its current position, if possible. Localization is performed on a target node arbitrarily positioned in the environment. Each time drones make a move, a reward is awarded to them depending on the estimated distance from the target and the GDOP. This allows drones to determine whether or not the action taken in a particular cell was valid. Three different algorithms have been proposed and implemented. The first one called 'Multi agent Q-learning' is used in small gridworld. Each executable action in a cell is assigned a certain value, called q-value, indicating how much that action is useful to reach the final goal. The tested scenarios include both environments with and without obstacles. A deep reinforcement learning approach was used to shift the problem even to larger environments. Thanks to the use of neural networks, an algorithm called 'actor-critic' has been implemented. The action will be chosen over a distribution of probabilities. Finally, the two algorithms have been united in a hybrid technique that allows trilateration to be performed even on mobile targets.
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Wahlberg, Fredrik. "Parallel algorithms for target tracking on multi-coreplatform with mobile LEGO robots." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-155537.
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The aim of this master thesis was to develop a versatile and reliable experimentalplatform of mobile robots, solving tracking problems, for education and research.Evaluation of parallel bearings-only tracking and control algorithms on a multi-corearchitecture has been performed. The platform was implemented as a mobile wirelesssensor network using multiple mobile robots, each using a mounted camera for dataacquisition. Data processing was performed on the mobile robots and on a server,which also played the role of network communication hub. A major focus was toimplement this platform in a flexible manner to allow for education and futureresearch in the fields of signal processing, wireless sensor networks and automaticcontrol. The implemented platform was intended to act as a bridge between the idealworld of simulation and the non-ideal real world of full scale prototypes.The implemented algorithms did estimation of the positions of the robots, estimationof a non-cooperating target's position and regulating the positions of the robots. Thetracking algorithms implemented were the Gaussian particle filter, the globallydistributed particle filter and the locally distributed particle filter. The regulator triedto move the robots to give the highest possible sensor information under givenconstraints. The regulators implemented used model predictive control algorithms.Code for communicating with filters in external processes were implementedtogether with tools for data extraction and statistical analysis.Both implementation details and evaluation of different tracking algorithms arepresented. Some algorithms have been tested as examples of the platformscapabilities, among them scalability and accuracy of some particle filtering techniques.The filters performed with sufficient accuracy and showed a close to linear speedupusing up to 12 processor cores. Performance of parallel particle filtering withconstraints on network bandwidth was also studied, measuring breakpoints on filtercommunication to avoid weight starvation. Quality of the sensor readings, networklatency and hardware performance are discussed. Experiments showed that theplatform was a viable alternative for data acquisition in algorithm development and forbenchmarking to multi-core architecture. The platform was shown to be flexibleenough to be used a framework for future algorithm development and education inautomatic control.
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Jerrelind, Jakob. "Tracking of Pedestrians Using Multi-Target Tracking Methods with a Group Representation." Thesis, Linköpings universitet, Reglerteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-172579.
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Multi-target tracking (MTT) methods estimate the trajectory of targets from noisy measurement; therefore, they can be used to handle the pedestrian-vehicle interaction for a moving vehicle. MTT has an important part in assisting the Automated Driving System and the Advanced Driving Assistance System to avoid pedestrian-vehicle collisions. ADAS and ADS rely on correct estimates of the pedestrians' position and velocity, to avoid collisions or unnecessary emergency breaking of the vehicle. Therefore, to help the risk evaluation in these systems, the MTT needs to provide accurate and robust information of the trajectories (in terms of position and velocity) of the pedestrians in different environments. Several factors can make this problem difficult to handle for instance in crowded environments the pedestrians can suffer from occlusion or missed detection. Classical MTT methods, such as the global nearest neighbour filter, can in crowded environments fail to provide robust and accurate estimates. Therefore, more sophisticated MTT methods should be used to increase the accuracy and robustness and, in general, to improve the tracking of targets close to each other. The aim of this master's thesis is to improve the situational awareness with respect to pedestrians and pedestrian-vehicle interactions. In particular, the task is to investigate if the GM-PHD and the GM-CPHD filter improve pedestrian tracking in urban environments, compared to other methods presented in the literature. The proposed task can be divided into three parts that deal with different issues. The first part regards the significance of different clustering methods and how the pedestrians are grouped together. The implemented algorithms are the distance partitioning algorithm and the Gaussian mean shift clustering algorithm. The second part regards how modifications of the measurement noise levels and the survival of targets based on the target location, with respect to the vehicle's position, can improve the tracking performance and remove unwanted estimates. Finally, the last part regards the impact the filter estimates have on the tracking performance and how important accurate detections of the pedestrians are to improve the overall tracking. From the result the distance partitioning algorithm is the favourable algorithm, since it does not split larger groups. It is also seen that the proposed filters provide correct estimates of pedestrians in events of occlusion or missed detections but suffer from false estimates close to the ego vehicle due to uncertain detections. For the comparison, regarding the improvements, a classic standard MTT filter applying the global nearest neighbour method for the data association is used as the baseline. To conclude; the GM-CPHD filter proved to be the best out of the two proposed filters in this thesis work and performed better also compared to other methods known in the literature. In particular, its estimates survived for a longer period of time in presence of missed detection or occlusion. The conclusion of this thesis work is that the GM-CPHD filter improves the tracking performance and the situational awareness of the pedestrians.
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MILWAY, WILLIAM B. "MULTIPLE TARGET INSTRUMENTATION RADARS FOR MILITARY TEST AND EVALUATION." International Foundation for Telemetering, 1985. http://hdl.handle.net/10150/615734.
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International Telemetering Conference Proceedings / October 28-31, 1985 / Riviera Hotel, Las Vegas, NevadaMilitary aerospace test ranges are increasingly being called upon to conduct missions utilizing large numbers of participating units, or targets. Precision, position and trajectory data must be recorded on all participants. In addition, weapon/target engagements must be scored and real-time range safety considerations must be accommodated. This requires precision metric data be available in real-time on all participating targets. One solution to these problems, is utilization of multiple target tracking radars which incorporate electronic beam steering to quickly move from one target to another in sequence. This paper briefly recounts the history of range instrumentation radars, points out some of the advantages of using multi-target radars, and highlights the specifications and design of a multiple target instrumentation radar now being acquired by the U.S. Army for use at White Sands Missile Range and the Kwajalein Missile Range.