Decentralized kalman filter approach for multi-sensor multi-target tracking problems

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Doğru pozisyon ve hedeflerin sayısı hava trafik kontrol ve füze savunması için çok önemli bilgilerdir. Bu çalışma, çoklu sensorlü çoklu hedef takibi sistemlerindeki veri füzyonu ve durum tahmini problemlerı için dağıtık Kalman Filtreleme Algoritması sunmaktadır. Problem, radar olarak her biri kendi veri işleme birimine sahip aktif sensörlerin hedef alanını gözlemlemesini esas almaktadır. Bu durumda her bir sistemin iz sayısı olacaktır. Çalışmada önerilen dağıtık Kalman Filtresi, başta füze sistemleri olmak üzere savunma sistemlerinde hareketli hedeflerin farklı sensörlerle izlerini kestirmek ve farklı hedefleri ayrıd etmek için kullanmaktır. Önerilen teknik, çoklu sensör sisteminden gelen verileri işleyen iki aşamalı veri işleme yaklaşımını içermektedir. İlk aşamada, her yerel işlemci kendi verilerini ve standart Kalman filtresi ise en iyi kestirimi yapmak için kullanılmaktadır. Sonraki aşamada bu kestirimler en iyi küresel bir kestirimi yapmak amacıyla dağıtık işlem modunda elde edilir. Bu çalışmada iki radar sistemi iki yerel Kalman filtresi ile uçakların pozisyonunu kestirmek amacıyla kullanılmakta, ardından bu kestirimler merkez işlemciye iletilmektedir. Merkez işlemci doğrulama maksadıyla bu bilgileri birleştirip küresel bir kestirim üretmektedir. Önerilen model uygulama olarak dört senaryo üzerinde test edildi. İlk senaryoda, tek bir hedef iki sensor tarafından izlenirken, ikincisinde, iki hedeften oluşan uzay herhangi bir sensor tarafından izlenmekte, üçüncüsünde, iki hedefin de herhangi bir sensor tarafından aynı anda izlenmesi, son olarak ise iki sensörden her birinin toplam üç hedeften herhangi ikisini izlediği senaryo göz önüne alınmıştır. Önerilen tekniğin performansı hata kovaryans matrisi kullanılarak değerlendirildi ve yüksek doğruluk ve optimal kestirim elde edildi. Uygulama sonuçları önerilen tekniğin yeteneğinin, yerel sensörlerce belirlenen ortak hedeflerin merkezi sistem tarafından ayırd edilebildiğini göstermiştir.For air traffic control and missile defense, the accurate position and the numbers of targets are the most important information needed. This thesis presents a decentralized kalman filtering algorithm (DKF) for data fusion and state estimation problems in multi-sensor multi-target tracking system. The problem arises when several sensors carry out surveillance over a certain area and each sensor has its own data processing system. In this situation, each system has a number of tracks. The DKF is used to estimate and separate the tracks from different sensors represent the targets, when the ability to track targets is essential in missile defense. The proposed technique is a two stage data processing technique which processes data from multi sensor system. In the first stage, each local processor uses its own data to make the best local estimation using standard kalman filter and then these estimations are then obtained in parallel processing mode to make best global estimation. In this work, two radar systems are used as sensors with two local Kalman filters to estimate the position of an aircraft and then they transmit these estimations to a central processor, which combines this information to produce a global estimation. The proposed model is tested on four scenarios, firstly, when there is one target and the two sensors are tracking the same target, secondly, when there are two targets and any sensor is tracking one of them, thirdly, when there are two targets and any sensor is tracking both of them and finally, when two sensors are used to track three targets and any sensor tracks any two of them. The performance of the proposed technique is evaluated using measures such as the error covariance matrix and it gave high accuracy and optimal estimation. The experimental results showed that the proposed method has the ability to separate the joint targets detected by the local sensors

Proceedings of the Fourth MIT/ONR Workshop on Distributed Information and Decision Systems Motivated by Command-Control-Communications (C3) Problems, June 15-June 26, 1981, San Diego, California

"OSP number 85552"--Cover.Library has v. 2 only.Includes bibliographies.Workshop suppported by the Office of Naval Research under contract ONR/N00014-77-C-0532edited by Michael Athans ... [et al.].v.1. Surveillance and target tracking--v.2. Systems architecture and evaluation--v.3. Communication, data bases & decision support--v.4. C3 theory

Estimation, Decision and Applications to Target Tracking

This dissertation mainly consists of three parts. The first part proposes generalized linear minimum mean-square error (GLMMSE) estimation for nonlinear point estimation. The second part proposes a recursive joint decision and estimation (RJDE) algorithm for joint decision and estimation (JDE). The third part analyzes the performance of sequential probability ratio test (SPRT) when the log-likelihood ratios (LLR) are independent but not identically distributed. The linear minimum mean-square error (LMMSE) estimation plays an important role in nonlinear estimation. It searches for the best estimator in the set of all estimators that are linear in the measurement. A GLMMSE estimation framework is proposed in this disser- tation. It employs a vector-valued measurement transform function (MTF) and finds the best estimator among all estimators that are linear in MTF. Several design guidelines for the MTF based on a numerical example were provided. A RJDE algorithm based on a generalized Bayes risk is proposed in this dissertation for dynamic JDE problems. It is computationally efficient for dynamic problems where data are made available sequentially. Further, since existing performance measures for estimation or decision are effective to evaluate JDE algorithms, a joint performance measure is proposed for JDE algorithms for dynamic problems. The RJDE algorithm is demonstrated by applications to joint tracking and classification as well as joint tracking and detection in target tracking. The characteristics and performance of SPRT are characterized by two important functions—operating characteristic (OC) and average sample number (ASN). These two functions have been studied extensively under the assumption of independent and identically distributed (i.i.d.) LLR, which is too stringent for many applications. This dissertation relaxes the requirement of identical distribution. Two inductive equations governing the OC and ASN are developed. Unfortunately, they have non-unique solutions in the general case. They do have unique solutions in two special cases: (a) the LLR sequence converges in distributions and (b) the LLR sequence has periodic distributions. Further, the analysis can be readily extended to evaluate the performance of the truncated SPRT and the cumulative sum test

Joint probabilistic data association filter with unknown detection probability and clutter rate

This paper proposes a novel joint probabilistic data association (JPDA) filter for joint target tracking and track maintenance under unknown detection probability and clutter rate. The proposed algorithm consists of two main parts: (1) the standard JPDA filter with a Poisson point process birth model for multi-object state estimation; and (2) a multi-Bernoulli filter for detection probability and clutter rate estimation. The performance of the proposed JPDA filter is evaluated through empirical tests. The results of the empirical tests show that the proposed JPDA filter has comparable performance with ideal JPDA that is assumed to have perfect knowledge of detection probability and clutter rate. Therefore, the algorithm developed is practical and could be implemented in a wide range of application

Fusion of Video and Multi-Waveform FMCW Radar for Traffic Surveillance

Modern frequency modulated continuous wave (FMCW) radar technology provides the ability to modify the system transmission frequency as a function of time, which in turn provides the ability to generate multiple output waveforms from a single radar unit. Current low-power multi-waveform FMCW radar techniques lack the ability to reliably associate measurements from the various waveform sections in the presence of multiple targets and multiple false detections within the field-of-view. Two approaches are developed here to address this problem. The first approach takes advantage of the relationships between the waveform segments to generate a weighting function for candidate combinations of measurements from the waveform sections. This weighting function is then used to choose the best candidate combinations to form polar-coordinate measurements. Simulations show that this approach provides a ten to twenty percent increase in the probability of correct association over the current approach while reducing the number of false alarms in generated in the process, but still fails to form a measurement if a detection form a waveform section is missing. The second approach models the multi-waveform FMCW radar as a set of independent sensors and uses distributed data fusion to fuse estimates from those individual sensors within a tracking structure. Tracking in this approach is performed directly with the raw frequency and angle measurements from the waveform segments. This removes the need for data association between the measurements from the individual waveform segments. A distributed data fusion model is used again to modify the radar tracking systems to include a video sensor to provide additional angular and identification information into the system. The combination of the radar and vision sensors, as an end result, provides an enhanced roadside tracking system

Robust Multi-target Tracking with Bootstrapped-GLMB Filter

This dissertation presents novel multi-target tracking algorithms that obviate the need for prior knowledge of system parameters such as clutter rate, detection probabilities, and birth models. Information on these parameters is unknown but important to tracking performance. The proposed algorithms exploit the advantages of existing RFS trackers and filters by bootstrapping them. This configuration inherits the efficiency of tracking target trajectories from the RFS trackers and low complexity in parameter estimation from the RFS filters

Multiple-objective sensor management and optimisation

One of the key challenges associated with exploiting modern Autonomous Vehicle technology for military surveillance tasks is the development of Sensor Management strategies which maximise the performance of the on-board Data-Fusion systems. The focus of this thesis is the development of Sensor Management algorithms which aim to optimise target tracking processes. Three principal theoretical and analytical contributions are presented which are related to the manner in which such problems are formulated and subsequently solved.Firstly, the trade-offs between optimising target tracking and other system-level objectives relating to expected operating lifetime are explored in an autonomous ground sensor scenario. This is achieved by modelling the observer trajectory control design as a probabilistic, information-theoretic, multiple-objective optimisation problem. This novel approach explores the relationships between the changes in sensor-target geometry that are induced by tracking performance measures and those relating to power consumption. This culminates in a novel observer trajectory control algorithm based onthe minimax approach.The second contribution is an analysis of the propagation of error through a limited-lookahead sensor control feedback loop. In the last decade, it has been shown that the use of such non-myopic (multiple-step) planning strategies can lead to superior performance in many Sensor Management scenarios. However, relatively little is known about the performance of strategies which use different horizon lengths. It is shown that, in the general case, planning performance is a function of the length of the horizon over which the optimisation is performed. While increasing the horizon maximises the chances of achieving global optimality, by revealing information about the substructureof the decision space, it also increases the impact of any prediction error, approximations, or unforeseen risk present within the scenario. These competing mechanisms aredemonstrated using an example tracking problem. This provides the motivation for a novel sensor control methodology that employs an adaptive length optimisation horizon. A route to selecting the optimal horizon size is proposed, based on a new non-myopic risk equilibrium which identifies the point where the two competing mechanisms are balanced.The third area of contribution concerns the development of a number of novel optimisation algorithms aimed at solving the resulting sequential decision making problems. These problems are typically solved using stochastic search methods such as Genetic Algorithms or Simulated Annealing. The techniques presented in this thesis are extensions of the recently proposed Repeated Weighted Boosting Search algorithm. In its originalform, it is only applicable to continuous, single-objective, ptimisation problems. The extensions facilitate application to mixed search spaces and Pareto multiple-objective problems. The resulting algorithms have performance comparable with Genetic Algorithm variants, and offer a number of advantages such as ease of implementation and limited tuning requirements