Miodrag Bolic

Resampling algorithms and architectures for distributed particle filters

In this paper, we propose novel resampling algorithms with architectures for efficient distributed implementation of particle filters. The proposed algorithms improve the scalability of the filter architectures affected by the resampling process. Problems in the particle filter implementation due to resampling are described, and appropriate modifications of the resampling algorithms are proposed so that distributed implementations are developed and studied. Distributed resampling algorithms with proportional allocation (RPA) and nonproportional allocation (RNA) of particles are considered. The components of the filter architectures are the processing elements (PEs), a central unit (CU), and an interconnection network. One of the main advantages of the new resampling algorithms is that communication through the interconnection network is reduced and made deterministic, which results in simpler network structure and increased sampling frequency. Particle filter performances are estimated for the bearings-only tracking applications. In the architectural part of the analysis, the area and speed of the particle filter implementation are estimated for a different number of particles and a different level of parallelism with field programmable gate array (FPGA) implementation. In this paper, only sampling importance resampling (SIR) particle filters are considered, but the analysis can be extended to any particle filters with resampling.

Resampling algorithms for particle filters: a computational complexity perspective

Newly developed resampling algorithms for particle filters suitable for real-time implementation are described and their analysis is presented. The new algorithms reduce the complexity of both hardware and DSP realization through addressing common issues such as decreasing the number of operations and memory access. Moreover, the algorithms allow for use of higher sampling frequencies by overlapping in time the resampling step with the other particle filtering steps. Since resampling is not dependent on any particular application, the analysis is appropriate for all types of particle filters that use resampling. The performance of the algorithms is evaluated on particle filters applied to bearings-only tracking and joint detection and estimation in wireless communications. We have demonstrated that the proposed algorithms reduce the complexity without performance degradation.

Taxonomy and Challenges of the Integration of RFID and Wireless Sensor Networks

Radio frequency identification and wireless sensor networks are two important wireless technologies that have a wide variety of applications in current and future systems. RFID facilitates detection and identification of objects that are not easily detectable or distinguishable by using conventional sensor technologies. However, it does not provide information about the condition of the objects it detects. WSN, on the other hand, not only provides information about the condition of the objects and environment but also enables multihop wireless communications. Hence, the integration of these technologies expands their overall functionality and capacity. This article investigates recent research work and applications that integrate RFID with sensor networks. Four classes of integration are discussed: integrating tags with sensors, integrating tags with WSN nodes and wireless devices, integrating readers with WSN nodes and wireless devices, and a mix of RFID and WSNs. Finally, a discussion of new challenges and future work is presented.

Resampling Methods for Particle Filtering: Classification, implementation, and strategies

Two decades ago, with the publication, we witnessed the rebirth of particle filtering (PF) as a methodology for sequential signal processing. Since then, PF has become very popular because of its ability to process observations represented by nonlinear state-space models where the noises of the model can be non-Gaussian. This methodology has been adopted in various fields, including finance, geophysical systems, wireless communications, control, navigation and tracking, and robotics. The popularity of PF has also spurred the publication of several review articles. In this article, the state of the art of resampling methods was reviewed. The methods were classified and their properties were compared in the framework of the proposed classifications. The emphasis in the article was on the classification and qualitative descriptions of the algorithms. The intention was to provide guidelines to practitioners and researchers.

New resampling algorithms for particle filters

Resampling is a critically important operation in the implementation of particle filtering. In parallel hardware implementations, resampling becomes a bottleneck due to its sequential nature and the increased complexity it imposes on the traffic of the designed interconnection network. To circumvent some of these difficulties, we propose two new resampling algorithms. The first one, called residual-systematic resampling, combines the merits of both systematic and residual resampling and is suitable for pipelined implementation. It also guarantees the fixed duration of the resampling procedure irrespective of the weight distribution of the particles. The second algorithm, referred to as partial resampling, has low complexity and reduces traffic load through the hardware network. These two algorithms should also be considered as resampling methods in simulations on standard computers.

Electrocardiogram-Assisted Blood Pressure Estimation

Accurate automatic noninvasive assessment of blood pressure (BP) presents a challenge due to conditions like arrhythmias, obesity, and postural changes that tend to obfuscate arterial amplitude pulsations sensed by the cuff. Researchers tried to overcome this challenge by analyzing oscillometric pulses with the aid of a higher fidelity signal-the electrocardiogram (ECG). Moreover, pulse transit time (PTT) was employed to provide an additional method for BP estimation. However, these methods were not fully developed, suitably integrated, or tested. To address these issues, we present a novel method whereby ECG-assisted oscillometric and PTT (measured between ECG R-peaks and maximum slope of arterial pulse peaks) analyses are seamlessly integrated into the oscillometric BP measurement paradigm. The method bolsters oscillometric analysis (amplitude modulation) with more reliable ECG R-peaks provides a complementary measure with PTT analysis (temporal modulation) and fuses this information for robust BP estimation. We have integrated this technology into a prototype that comprises a BP cuff with an embedded conductive fabric ECG electrode, associated hardware, and algorithms. A pilot study has been undertaken on ten healthy subjects (150 recordings) to validate the performance of our prototype against United States Food and Drug Administration approved Omron oscillometric monitor (HEM-790IT). Our prototype achieves mean absolute difference of less than 5 mmHg and grade A as per the British Hypertension Society protocol for estimating BP, with the reference Omron monitor.

Generic Hardware Architectures for Sampling and Resampling in Particle Filters

Particle filtering is a statistical signal processing methodology that has recently gained popularity in solving several problems in signal processing and communications. Particle filters (PFs) have been shown to outperform traditional filters in important practical scenarios. However their computational complexity and lack of dedicated hardware for real-time processing have adversely affected their use in real-time applications. In this paper, we present generic architectures for the implementation of the most commonly used PF, namely, the sampling importance resampling filter (SIRF). These provide a generic framework for the hardware realization of the SIRF applied to any model. The proposed architectures significantly reduce the memory requirement of the filter in hardware as compared to a straightforward implementation based on the traditional algorithm. We propose two architectures each based on a different resampling mechanism. Further, modifications of these architectures for acceleration of resampling process are presented. We evaluate these schemes based on resource usage and latency. The platform used for the evaluations is the Xilinx Virtex II pro FPGA. The architectures presented here have led to the development of the first hardware (FPGA) prototype for the particle filter applied to the bearings-only tracking problem.

Novel Semi-Passive RFID System for Indoor Localization

In this paper, we present a novel semi-passive radio-frequency identification (RFID) system for accurate indoor localization. The system is composed of a standard ultra high frequency (UHF) ISO-18000-6C compliant RFID reader, a set of standard passive RFID tags whose locations are known, and a newly developed tag-like RFID component, which is attached to the items that need to be localized. The new semi-passive component, referred to as sensatag (sense-a-tag), has a dual functionality: it can sense and decode communication between the reader and standard tags in its proximity, and can communicate with the reader like standard tags using backscatter modulation. Based on the information conveyed by the sensatags to the reader, localization algorithms based on binary sensor principles can be developed. We conduct a number of experiments in a laboratory to quantify the performance of our system, including two real applications, one finding the exact placement of items on shelves, and the other estimating the direction of item movement.

Brief paper: Robust computationally efficient control of cooperative closed-chain manipulators with uncertain dynamics

This article presents a decentralized control scheme for the complex problem of simultaneous position and internal force control in cooperative multiple manipulator systems. The proposed controller is composed of a sliding mode control term and a force robustifying term to simultaneously control the payloads position/orientation as well as the internal forces induced in the system. This is accomplished independently of the manipulators dynamics. Unlike most controllers that do not require prior knowledge of the manipulators dynamics, the suggested controller does not use fuzzy logic inferencing and is computationally inexpensive. Using a Lyapunov stability approach, the controller is proven to be robust in the face of varying systems dynamics. The payloads position/orientation and the internal force errors are also shown to asymptotically converge to zero under such conditions.

Coefficient-Free Blood Pressure Estimation Based on Pulse Transit Time–Cuff Pressure Dependence

Oscillometry is a popular technique for automatic estimation of blood pressure (BP). However, most of the oscillometric algorithms rely on empirical coefficients for systolic and diastolic pressure evaluation that may differ in various patient populations, rendering the technique unreliable. A promising complementary technique for automatic estimation of BP, based on the dependence of pulse transit time (PTT) on cuff pressure (CP) (PTT-CP mapping), has been proposed in the literature. However, a theoretical grounding for this technique and a nonparametric BP estimation approach are still missing. In this paper, we propose a novel coefficient-free BP estimation method based on PTT-CP dependence. PTT is mathematically modeled as a function of arterial lumen area under the cuff. It is then analytically shown that PTT-CP mappings computed from various points on the arterial pulses can be used to directly estimate systolic, diastolic, and mean arterial pressure without empirical coefficients. Analytical results are cross-validated with a pilot investigation on ten healthy subjects where 150 simultaneous electrocardiogram and oscillometric BP recordings are analyzed. The results are encouraging whereby the mean absolute errors of the proposed method in estimating systolic and diastolic pressures are 5.31 and 4.51 mmHg, respectively, relative to the Food and Drug Administration approved Omron monitor. Our work thus shows promise toward providing robust and objective BP estimation in a variety of patients and monitoring situations.