Distributed estimation over a low-cost sensor network: a review of state-of-the-art

Proliferation of low-cost, lightweight, and power efficient sensors and advances in networked systems enable the employment of multiple sensors. Distributed estimation provides a scalable and fault-robust fusion framework with a peer-to-peer communication architecture. For this reason, there seems to be a real need for a critical review of existing and, more importantly, recent advances in the domain of distributed estimation over a low-cost sensor network. This paper presents a comprehensive review of the state-of-the-art solutions in this research area, exploring their characteristics, advantages, and challenging issues. Additionally, several open problems and future avenues of research are highlighted

Distributed joint probabilistic data association filter with hybrid fusion strategy

This paper investigates the problem of distributed multitarget tracking (MTT) over a large-scale sensor network, consisting of low-cost sensors. Each local sensor runs a joint probabilistic data association filter to obtain local estimates and communicates with its neighbors for information fusion. The conventional fusion strategies, i.e., consensus on measurement (CM) and consensus on information (CI), are extended to MTT scenarios. This means that data association uncertainty and sensor fusion problems are solved simultaneously. Motivated by the complementary characteristics of these two different fusion approaches, a novel distributed MTT algorithm using a hybrid fusion strategy, e.g., a mix of CM and CI, is proposed. A distributed counting algorithm is incorporated into the tracker to provide the knowledge of the total number of sensor nodes. The new algorithm developed shows advantages in preserving boundedness of local estimates, guaranteeing global convergence to the optimal centralized version and being implemented without requiring no global information, compared with other fusion approaches. Simulations clearly demonstrate the characteristics and tracking performance of the proposed algorithm

5G mmWave Cooperative Positioning and Mapping Using Multi-Model PHD Filter and Map Fusion

5G millimeter wave (mmWave) signals can enable accurate positioning in vehicular networks when the base station and vehicles are equipped with large antenna arrays. However, radio-based positioning suffers from multipath signals generated by different types of objects in the physical environment. Multipath can be turned into a benefit, by building up a radio map (comprising the number of objects, object type, and object state) and using this map to exploit all available signal paths for positioning. We propose a new method for cooperative vehicle positioning and mapping of the radio environment, comprising a multiple-model probability hypothesis density filter and a map fusion routine, which is able to consider different types of objects and different fields of views. Simulation results demonstrate the performance of the proposed method

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

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

PMBM-Based SLAM Filters in 5G mmWave Vehicular Networks

Radio-based vehicular simultaneous localization and mapping (SLAM) aims to localize vehicles while mapping the landmarks in the environment. We propose a sequence of three Poisson multi-Bernoulli mixture (PMBM) based SLAM filters, which handle the entire SLAM problem in a theoretically optimal manner. The complexity of the three proposed SLAM filters is progressively reduced while sustaining high accuracy by deriving SLAM density approximation with the marginalization of nuisance parameters (either vehicle state or data association). Firstly, the PMBM SLAM filter serves as the foundation, for which we provide the first complete description based on a Rao-Blackwellized particle filter. Secondly, the Poisson multi-Bernoulli (PMB) SLAM filter is based on the standard reduction from PMBM to PMB, but involves a novel interpretation based on auxiliary variables and a relation to Bethe free energy. Finally, using the same auxiliary variable argument, we derive a marginalized PMB SLAM filter, which avoids particles and is instead implemented with a low-complexity cubature Kalman filter. We evaluate the three proposed SLAM filters in comparison with the probability hypothesis density (PHD) SLAM filter in 5G mmWave vehicular networks and show the computation-performance trade-off between them

Radar networks: A review of features and challenges

Networks of multiple radars are typically used for improving the coverage and tracking accuracy. Recently, such networks have facilitated deployment of commercial radars for civilian applications such as healthcare, gesture recognition, home security, and autonomous automobiles. They exploit advanced signal processing techniques together with efficient data fusion methods in order to yield high performance of event detection and tracking. This paper reviews outstanding features of radar networks, their challenges, and their state-of-the-art solutions from the perspective of signal processing. Each discussed subject can be evolved as a hot research topic.Comment: To appear soon in Information Fusio

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

Estimation and control of multi-object systems with high-fidenlity sensor models: A labelled random finite set approach

Principled and novel multi-object tracking algorithms are proposed, that have the ability to optimally process realistic sensor data, by accommodating complex observational phenomena such as merged measurements and extended targets. Additionally, a sensor control scheme based on a tractable, information theoretic objective is proposed, the goal of which is to optimise tracking performance in multi-object scenarios. The concept of labelled random finite sets is adopted in the development of these new techniques