Akshay Athalye

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.

A Radio Frequency Identification System for accurate indoor localization

In this paper we present a novel Radio Frequency Identification (RFID) system for accurate indoor localization. The system is composed of a standard Ultra High Frequency (UHF), ISO-18006C compliant RFID reader, a large set of standard passive RFID tags whose locations are known, and a newly developed tag-like RFID component that 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 wherein it can sense the communication between the reader and standard tags which are in its proximity, and also 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 present results from real measurements that show the accuracy of the proposed system.

Design Methodology for Domain Specific Parameterizable Particle Filter Realizations

This paper presents a reconfigurable particle filter design methodology for a real-time bearings-only tracking application. The methodology provides the capability of selecting a single particle filter from multiple particle filter realizations with maximum resource sharing. The autonomous buffer controller mechanism for the architecture ensures correct operation of the particle filters. Parameter adaptation and algorithm reconfiguration can be accomplished with negligible reconfiguration overhead through buffer controllers and a set of switches for transforming dataflow structures such that any desired particle filter can be implemented. Two target particle filters, sample importance resample filter (SIRF) and Gaussian particle filter (GPF), are realized using field programmable gate array (FPGA) based on the proposed methodology. However, the architecture can be extended for a wide range of particle filters with different sets of dynamics. This paper successfully demonstrates that implementation of a domain specific processor for particle filters is feasible with performance that is much higher than that of commercially available digital signal processors (DSPs).

Particle filtering for indoor RFID tag tracking

In this paper, we propose a particle filtering (PF) method for indoor tracking using radio frequency identification (RFID) based on aggregated binary measurements. We use an Ultra High Frequency (UHF) RFID system that is composed of a standard RFID reader, a large set of standard passive tags whose locations are known, and a newly designed, special semi-passive tag attached to an object that is tracked. This semi-passive tag has the dual ability to sense the backscatter communication between the reader and other passive tags which are in its proximity and to communicate this sensed information to the reader using backscatter modulation. We refer to this tag as a sense-a-tag (ST). Thus, the ST can provide the reader with information that can be used to determine the kinematic parameters of the object on which the ST is attached. We demonstrate the performance of the method with data obtained in a laboratory environment.

Indoor Tracking With RFID Systems

This paper addresses the problem of indoor tracking of tagged objects with Ultra High Frequency (UHF) Radio Frequency Identification (RFID) systems. A new and more realistic observation model of the system is proposed, where the probability of detecting a tag by a reader is described by a Beta distribution. We model the probability of detection as a function of both the distance and the angle between the tag and the reader. The considered model also accounts for the possibility of a tag being in a dead-zone, that is, in a space where the tag cannot be detected even if it is well within the range of a reader. For tracking, we propose the use of the particle filtering methodology that takes into account the asynchronous nature of the measurements. The needed parameters for modeling the system are obtained from laboratory experiments and the performance of the algorithm is shown by extensive computer simulations.

Study of Algorithmic and Architectural Characteristics of Gaussian Particle Filters

In this paper, we analyze algorithmic and architectural characteristics of a class of particle filters known as Gaussian Particle Filters (GPFs). GPFs approximate the posterior density of the unknowns with a Gaussian distribution which limits the scope of their applications in comparison with the universally applied sample-importance resampling filters (SIRFs) but allows for their implementation without the classical resampling procedure. Since there is no need for resampling, we propose a modified GPF algorithm that is suitable for parallel hardware realization. Based on the new algorithm, we propose an efficient parallel and pipelined architecture for GPF that is superior to similar architectures for SIRF in the sense that it requires no memories for storing particles and it has very low amount of data exchange through the communication network. We analyze the GPF on the bearings-only tracking problem and the results are compared with results obtained by SIRF in terms of computational complexity, potential throughput, and hardware energy. We consider implementation on FPGAs and we perform detailed comparison of the GPF and SIRF algorithms implemented in different ways on this platform. GPFs that are implemented in parallel pipelined fashion on FPGAs can support higher sampling rates than SIRFs and as such they might be a more suitable candidate for real-time applications.

Phase Cancellation in Backscatter-Based Tag-to-Tag Communication Systems

In this paper, we investigate a unique phase cancellation problem that occurs in backscatter-based tag-to-tag (BBTT) communication systems. These are systems wherein two or more radio-less devices (tags) communicate with each other purely by reflecting (backscattering) an external signal (whether ambient or intentionally generated). A transmitting tag modulates baseband information onto the reflected signal using backscatter modulation. At the receiving tag, the backscattered signal is superimposed to the external excitation and the resulting signal is demodulated using envelope detection techniques. The relative phase difference between the backscatter signal and the external excitation signal at the receiving tag has a large impact on the envelope of the resulting signal. This often causes a complete cancellation of the baseband information contained in the envelope, and it results in a loss of communication between the two tags. This problem is ubiquitous in all BBTT systems and greatly impacts the reliability, robustness, and communication range of such systems. We theoretically analyze and experimentally demonstrate this problem for devices that use both ASK and PSK backscattering. We then present a solution to the problem based on the design of a new backscatter modulator for tags that enables multiphase backscattering. We also propose a new combination method that can further enhance the detection performance of BBTT systems. We examine the performance of the proposed techniques through theoretical analysis, computer simulations, and laboratory experiments with a prototype tag that we have developed.

Distributed Architecture and Interconnection Scheme for Multiple Model Particle Filters

In this paper, we present a hardware architecture for a sampling importance resampling filter (SIRF) applied to systems with multiple interacting models. This filter outperforms traditional filters in practical scenarios due to superior abilities of the SIRFs in dealing with nonlinear and/or non-Gaussian models. Compared to existing approaches, our method does not require knowledge of model transition probabilities and keeps a constant number of particles per model at all times. This allows for a regular hardware structure with deterministic execution time. A highly scalable, parallel architecture consisting of distributed processing elements and a central unit is described. We propose an interconnection scheme and data exchange protocol using the concept of distributed resampling that greatly speeds up filter execution and drastically reduces the required interconnect to a single bus without causing any communication bottleneck. The proposed architecture is evaluated on a Xilinx FPGA platform for a multiple model target tracking application and its efficiency and scalability is shown

Design of a backscatter-based Tag-to-Tag system

Practical technologies for the Internet of Things (IoT) must provide connectivity to all objects under a common framework irrespective of their size or value. Power requirement, cost of wireless devices and scalability have proved critical bottlenecks for the universal deployment of the IoT. One approach to address these issues is the use of a communication paradigm where the devices communicate via backscattering and exploit harvested power from an external RF source. In a Backscattering Tag-to-Tag Network (BTTN), the tags themselves are able to read and interpret the backscattered communications from other neighboring tags. In the tag-to-tag link, the BTTN tag has to demodulate a receiving signal with a low modulation index. In order to improve the link range, we propose a power-efficient demodulator design that enables the receiving tag to quantify the amplitude-shift keying (ASK) modulated signal with a modulation index as low as 0.6%. The demodulator consumes 1.21 µW at 1.1 V supply voltage at a data rate of 10 kbps.