Embedded System Optimization of Radar Post-processing in an ARM CPU Core

Algorithms executed on the radar processor system contributes to a significant performance bottleneck of the overall radar system. One key performance concern is the latency in target detection when dealing with hard deadline systems. Research has shown software optimization as one major contributor to radar system performance improvements. This thesis aims at software optimizations using a manual and automatic approach and analyzing the results to make informed future decisions while working with an ARM processor system. In order to ascertain an optimized implementation, a question put forward was whether the algorithms on the ARM processor could work with a 6-antenna implementation without a decline in the performance. However, an answer would also help project how many additional algorithms can still be added without performance decline. The manual optimization was done based on the quantitative analysis of the software execution time. The manual optimization approach looked at the vectorization strategy using the NEON vector register on the ARM CPU to reimplement the initial Constant False Alarm Rate(CFAR) Detection algorithm. An additional optimization approach was eliminating redundant loops while going through the Range Gates and Doppler filters. In order to determine the best compiler for automatic code optimization for the radar algorithms on the ARM processor, the GCC and Clang compilers were used to compile the initial algorithms and the optimized implementation on the radar post-processing stage. Analysis of the optimization results showed that it is possible to run the radar post-processing algorithms on the ARM processor at the 6-antenna implementation without system load stress. In addition, the results show an excellent headroom margin based on the defined scenario. The result analysis further revealed that the effect of dynamic memory allocation could not be underrated in situations where performance is a significant concern. Additional statements from the result demonstrated that the GCC and Clang compiler has their strength and weaknesses when used in the compilation. One limiting factor to note on the optimization using the NEON register is the sample size’s effect on the optimization implementation. Although it fits into the test samples used based on the defined scenario, there might be varying results in varying window cell size situations that might not necessarily improve the time constraints

A comparison of processing approaches for distributed radar sensing

Radar networks received increasing attention in recent years as they can outperform single monostatic or bistatic systems. Further attention is being dedicated to these systems as an application of the MIMO concept, well know in communications for increasing the capacity of the channel and improving the overall quality of the connection. However, it is here shown that radar network can take advantage not only from the angular diversity in observing the target, but also from a variety of ways of processing the received signals. The number of devices comprising the network has also been taken into the analysis. Detection and false alarm are evaluated in noise only and clutter from a theoretical and simulated point of view. Particular attention is dedicated to the statistics behind the processing. Experiments have been performed to evaluate practical applications of the proposed processing approaches and to validate assumptions made in the theoretical analysis. In particular, the radar network used for gathering real data is made up of two transmitters and three receivers. More than two transmitters are well known to generate mutual interference and therefore require additional e�fforts to mitigate the system self-interference. However, this allowed studying aspects of multistatic clutter, such as correlation, which represent a first and novel insight in this topic. Moreover, two approaches for localizing targets have been developed. Whilst the first is a graphic approach, the second is hybrid numerical (partially decentralized, partially centralized) which is clearly shown to improve dramatically the single radar accuracy. Finally the e�ects of exchanging angular with frequency diversity are shown as well in some particular cases. This led to develop the Frequency MIMO and the Frequency Diverse Array, according to the separation of two consecutive frequencies. The latter is a brand new topic in technical literature, which is attracting the interest of the technical community because of its potential to generate range-dependant patterns. Both the latter systems can be used in radar-designing to improve the agility and the effciency of the radar

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

Tracking and control in multi-function radar

The phased array multi-function radar is an effective solution to the requirement for simultaneous surveillance and multiple target tracking. However, since it is performing the jobs usually undertaken by several dedicated radars its radar time and energy resources are limited. For this reason, and also due to the large cost of active phased array antennas, it is important for the strategies adopted in the control of the radar to be efficient. This thesis investigates and develops efficient strategies for multi-function radar control and tracking. Particularly the research has focused on the use of rotating array antennas and simultaneous multiple receive beam processing. The findings of the research challenge the traditional view that three or four fixed (static) array faces is the best antenna configuration for a multi-function radar system. By developing novel methods for the comparison of systems utilising different antenna configurations it is shown that a rotating array multi-function radar performs the surveillance function with a greater efficiency in its use of radar time than a static array system. Also, a rotating array system benefits from the ability to distribute the radar resources over the angular coverage in a way that is impossible with a static array system. A novel strategy is presented to achieve this, which allows the rotating array system to better support the realistic situation of a high concentration of radar tasks in a narrow angular sector. It is shown that the use of broadened transmit beams coupled with simultaneous multiple narrow receive beams can eliminate the compromise on radar beamwidth between the surveillance and tracking functions that is associated with multi-function radars. This technique would allow construction of multi-function radar systems with narrow beamwidths, giving improved tracking performance, without extending search frame times excessively. Efficient tracking strategies for both static array and rotating array multi-function radars are developed. They are applied through computer simulation to demonstrate tracking of highly manoeuvrable targets with a narrow beam multi-function radar. Track robustness is attained through the use of multiple beam track updating strategies at little cost in terms of radar time

Adaptive OFDM Radar for Target Detection and Tracking

We develop algorithms to detect and track targets by employing a wideband orthogonal frequency division multiplexing: OFDM) radar signal. The frequency diversity of the OFDM signal improves the sensing performance since the scattering centers of a target resonate variably at different frequencies. In addition, being a wideband signal, OFDM improves the range resolution and provides spectral efficiency. We first design the spectrum of the OFDM signal to improve the radar\u27s wideband ambiguity function. Our designed waveform enhances the range resolution and motivates us to use adaptive OFDM waveform in specific problems, such as the detection and tracking of targets. We develop methods for detecting a moving target in the presence of multipath, which exist, for example, in urban environments. We exploit the multipath reflections by utilizing different Doppler shifts. We analytically evaluate the asymptotic performance of the detector and adaptively design the OFDM waveform, by maximizing the noncentrality-parameter expression, to further improve the detection performance. Next, we transform the detection problem into the task of a sparse-signal estimation by making use of the sparsity of multiple paths. We propose an efficient sparse-recovery algorithm by employing a collection of multiple small Dantzig selectors, and analytically compute the reconstruction performance in terms of the $ell_1$-constrained minimal singular value. We solve a constrained multi-objective optimization algorithm to design the OFDM waveform and infer that the resultant signal-energy distribution is in proportion to the distribution of the target energy across different subcarriers. Then, we develop tracking methods for both a single and multiple targets. We propose an tracking method for a low-grazing angle target by realistically modeling different physical and statistical effects, such as the meteorological conditions in the troposphere, curved surface of the earth, and roughness of the sea-surface. To further enhance the tracking performance, we integrate a maximum mutual information based waveform design technique into the tracker. To track multiple targets, we exploit the inherent sparsity on the delay-Doppler plane to develop an computationally efficient procedure. For computational efficiency, we use more prior information to dynamically partition a small portion of the delay-Doppler plane. We utilize the block-sparsity property to propose a block version of the CoSaMP algorithm in the tracking filter

Cognitive Radar Detection in Nonstationary Environments and Target Tracking

Target detection and tracking are the most fundamental and important problems in a wide variety of defense and civilian radar systems. In recent years, to cope with complex environments and stealthy targets, the concept of cognitive radars has been proposed to integrate intelligent modules into conventional radar systems. To achieve better performance, cognitive radars are designed to sense, learn from, and adapt to environments. In this dissertation, we introduce cognitive radars for target detection in nonstationary environments and cognitive radar networks for target tracking.For target detection, many algorithms in the literature assume a stationary environment (clutter). However, in practical scenarios, changes in the nonstationary environment can perturb the parameters of the clutter distribution or even alter the clutter distribution family, which can greatly deteriorate the target detection capability. To avoid such potential performance degradation, cognitive radar systems are envisioned which can rapidly recognize the nonstationarity, accurately learn the new characteristics of the environment, and adaptively update the detector. To achieve this cognition, we propose a unifying framework that integrates three functions: (i) change-point detection of clutter distributions by using a data-driven cumulative sum (CUSUM) algorithm and its extended version, (ii) learning/identification of clutter distribution by using kernel density estimation (KDE) methods and similarity measures (iii) adaptive target detection by automatically modifying the likelihood-ratio test and the corresponding detection threshold. We also conduct extensive numerical experiments to show the merits of the proposed method compared to a nonadaptive case, an adaptive matched filter (AMF) method, and the clairvoyant case.For target tracking, with remarkable advances in sensor techniques and deployable platforms, a sensing system has freedom to select a subset of available radars, plan their trajectories, and transmit designed waveforms. Accordingly, we propose a general framework for single target tracking in cognitive networks of radars, including joint consideration of waveform design, path planning, and radar selection. We formulate the tracking procedure using the theories of dynamic graphical models (DGM) and recursive Bayesian state estimation (RBSE). This procedure includes two iterative steps: (i) solving a combinatorial optimization problem to select the optimal subset of radars, waveforms, and locations for the next tracking instant, and (ii) acquiring the recursive Bayesian state estimation to accurately track the target. Further, we use an illustrative example to introduce a specific scenario in 2-D space. Simulation results based on this scenario demonstrate that the proposed framework can accurately track the target under the management of a network of radars

Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays

Massive MIMO (multiple-input multiple-output) is no longer a "wild" or "promising" concept for future cellular networks - in 2018 it became a reality. Base stations (BSs) with 64 fully digital transceiver chains were commercially deployed in several countries, the key ingredients of Massive MIMO have made it into the 5G standard, the signal processing methods required to achieve unprecedented spectral efficiency have been developed, and the limitation due to pilot contamination has been resolved. Even the development of fully digital Massive MIMO arrays for mmWave frequencies - once viewed prohibitively complicated and costly - is well underway. In a few years, Massive MIMO with fully digital transceivers will be a mainstream feature at both sub-6 GHz and mmWave frequencies. In this paper, we explain how the first chapter of the Massive MIMO research saga has come to an end, while the story has just begun. The coming wide-scale deployment of BSs with massive antenna arrays opens the door to a brand new world where spatial processing capabilities are omnipresent. In addition to mobile broadband services, the antennas can be used for other communication applications, such as low-power machine-type or ultra-reliable communications, as well as non-communication applications such as radar, sensing and positioning. We outline five new Massive MIMO related research directions: Extremely large aperture arrays, Holographic Massive MIMO, Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin

Space geodesy altimetry study

Tradeoff analysis on radar and laser space altimeter systems for sea surface geodes