On the Application of Digital Moving Target Indication Techniques to Short-Range FMCW Radar Data

In this paper, we describe three digital moving target indication (MTI) and moving target segmentation techniques (based on target speed) and apply them to short-range frequency-modulated continuous wave (FMCW) radar data. The described approaches are applicable to many short-range radar sensors. In particular, we focus on FMCW radar, which are ubiquitous in numerous applications, including gesture recognition radar, automotive radar, and imaging radar. The three digital MTI filtering methods explored are background subtraction, finite impulse response (FIR) filtering, and infinite impulse response (IIR) filtering. Each of the methods is implemented in the time domain for simpler logic implementation. We apply the MTI methods on data sets gathered using a C-band FMCW radar in both a short-range, direct line-of-sight scenario and a complex cluttered through wall radar scenario. Based on the analyses, it is shown that each of the MTI techniques are extremely effective when deployed in the right scenario. Background subtraction is found to be well suited for slow-moving targets. FIR and IIR filtering techniques provide the simplest, one-step processes for moving target segmentation

Soft-core dataflow processor architecture optimized for radar signal processing

Current radar signal processors (RSPs) lack either performance or flexibility. Custom soft-core processors exhibit potential in high-performance signal processing applications, yet remain relatively unexplored in research literature. In this paper, we use an iterative design methodology to propose a novel soft-core streaming processor architecture. The datapaths of this architecture are arranged in a circular pattern, with multiple operands simultaneously flowing between switching multiplexers and functional units each cycle. By explicitly specifying instruction-level parallelism and software pipelining, applications can fully exploit the available computational resources. The proposed architecture exceeds the clock cycle performance of a commercial high-end digital signal processor (DSP) processor by an average factor of 14 over a range of typical operating parameters in an RSP application.http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6928471hb201

A scalable real-time processing chain for radar exploiting illuminators of opportunity

Includes bibliographical references.This thesis details the design of a processing chain and system software for a commensal radar system, that is, a radar that makes use of illuminators of opportunity to provide the transmitted waveform. The stages of data acquisition from receiver back-end, direct path interference and clutter suppression, range/Doppler processing and target detection are described and targeted to general purpose commercial off-the-shelf computing hardware. A detailed low level design of such a processing chain for commensal radar which includes both processing stages and processing stage interactions has, to date, not been presented in the Literature. Furthermore, a novel deployment configuration for a networked multi-site FM broadcast band commensal radar system is presented in which the reference and surveillance channels are record at separate locations

Sensing and Signal Processing in Smart Healthcare

In the last decade, we have witnessed the rapid development of electronic technologies that are transforming our daily lives. Such technologies are often integrated with various sensors that facilitate the collection of human motion and physiological data and are equipped with wireless communication modules such as Bluetooth, radio frequency identification, and near-field communication. In smart healthcare applications, designing ergonomic and intuitive human–computer interfaces is crucial because a system that is not easy to use will create a huge obstacle to adoption and may significantly reduce the efficacy of the solution. Signal and data processing is another important consideration in smart healthcare applications because it must ensure high accuracy with a high level of confidence in order for the applications to be useful for clinicians in making diagnosis and treatment decisions. This Special Issue is a collection of 10 articles selected from a total of 26 contributions. These contributions span the areas of signal processing and smart healthcare systems mostly contributed by authors from Europe, including Italy, Spain, France, Portugal, Romania, Sweden, and Netherlands. Authors from China, Korea, Taiwan, Indonesia, and Ecuador are also included

The University Defence Research Collaboration In Signal Processing

This chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations. The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour

Survey of FPGA applications in the period 2000 – 2015 (Technical Report)

Romoth J, Porrmann M, Rückert U. Survey of FPGA applications in the period 2000 – 2015 (Technical Report).; 2017.Since their introduction, FPGAs can be seen in more and more different fields of applications. The key advantage is the combination of software-like flexibility with the performance otherwise common to hardware. Nevertheless, every application field introduces special requirements to the used computational architecture. This paper provides an overview of the different topics FPGAs have been used for in the last 15 years of research and why they have been chosen over other processing units like e.g. CPUs

An FPGA Based Controller for a MEMS Tri-mode FMCW Radar

In this thesis a Xilinx Virtex 5 FPGA platform based signal processing algorithm has been developed and experimentally verified for use in a MEMS based tri-mode 77GHz FMCW automotive radar to determine range and velocity of targets in the vicinity of a host vehicle. The Xilinx Virtex 5 FPGA based signal processing and control algorithm dynamically reconfigures a MEMS based FMCW radar to provide a short, medium, and long-range coverage using the same hardware. The MEMS radar comprises of MEMS SP3T RF switches, microfabricated Rotman lens and a microstrip antenna embedded with MEMS SPST switches, in additional to other microelectronic components. A CA-CFAR module has been used to eliminate false targets in a multi target clutter affected scenario. The refresh rate for the current design is 2.048ms for each mode of radar which is nearly 40 times lower than the BOSCH LRR3. The maximum percent difference from analytical to HDL calculated range values was found to be 0.16% which can be lowered with further refinement. The developed FPGA based radar signal processing algorithm can be implemented as an ASIC which can be batch fabricated to lower down the production cost so that automotive radars can become a standard item for all the vehicles on the road

Aerial Vehicles

This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space