Data acquisition and signal processing on FMCW navigation radar system

A Multiple-Input Multiple-Output (MIMO) Frequency-Modulated Continuous Wave (FMCW) radar can provide a range-angle map that expresses the signal power against each range and angle. It is possible to estimate object locations by detecting the signal power that exceeds a threshold using an algorithm, such as Constant False Alarm Rate (CFAR). However, noise and multipath components often exist over the range-angle map, which could produce false alarms for an undesired location depending on the threshold setting. In other words, the threshold setting is sensitive in noisy range-angle maps. Therefore, if the noise is reduced, the threshold can be easily set to reduce the number of false alarms. In this paper, we propose a method that improves the CFAR threshold tolerance by denoising a range-angle map using Deep Image Prior (DIP). DIP is an unsupervised deep-learning technique that enables image denoising. In the proposed method, DIP is applied to the range-angle map calculated by the Curve-Length (CL) method, and then the object location is detected over the denoised range-angle map based on Cell-Averaging CFAR (CA-CFAR), which is a typical threshold setting algorithm. Through the experiments to estimate human locations in indoor environments, we confirmed that the proposed method with DIP reduced the number of false alarms and estimated the human location accurately while improving the tolerance of the threshold setting, compared to the method without DIP.

This study deals with the performance prediction of constant false alarm rate (CFAR) detector for non-independent and possibly non-independent and non-identically distributed (non-IID) gamma fluctuating targets in the presence of homogeneous background. Accounting for this target model, including the well-known Swerling models as special cases, the authors develop the analytic expressions of detection probability of the cell averaging CFAR (CA-CFAR) and the ordered-statistic CFAR (OS-CFAR) in terms of converging series. At the analysis stage, they study the truncation error and the convergence rate of the derived expressions. Finally, they assess the impacts on detection performance of the target parameters and compare the detection performance between CA-CFAR and OS-CFAR for the non-IID gamma fluctuating targets via numerical simulations.

Two novel constant false alarm rate (CFAR) detectors, the And-CFAR and Or-CFAR, are proposed, The two new CFAR detectors improve the conventional cell averaging CFAR (CA-CFAR) and order statistics CFAR (OS-CFAR) by making full use of the cell information. The novel CFAR processors combine the result of the CA-CFAR and OS-CFAR to get a better detection performance. In a homogeneous background, the mathematical medals of the two new CFAR detectors are derived and their performance has been evaluated and compared with that of CA-CFAR and OS-CFAR.

In this paper, the focus-before-detection (FBD) algorithm in frequency modulation continuous wave (FMCW) radar is considered and a novel FBD algorithm for weak maneuvering target detection, called searching velocity Fourier transform (SVFT), is proposed. This algorithm eliminates the fast-time and the slow-time coupling (FSC) caused by target motion via searching in the velocity parameter space and improves the focus performance of FMCW radar. First of all, SVFT compensates beat signal and removes FSC by searching velocity function (SVF). Secondly, the compensated beat signal is focused by two-dimensional fast Fourier transform (2D-FFT) in the candidate Doppler bins (CDB) which are related to SVF, and the target energy can be integrated into a peak. Then, the peak is obtained by constant false alarm rate (CFAR) detection. After that, according to the result of CFAR detection, the accurate range and velocity of the target can be obtained by solving equations. Finally, some simulations confirm the effectiveness of SVFT. The results show that SVFT performs better on focus ability and detection ability with lower computational complexity.

In this paper, we propose to analyze the binary integration of the cell-averaging constant false-alarm rate (CA-CFAR) and order statistics constant false-alarm rate (OS-CFAR) detectors in the presence of non-Gaussian spiky clutter modeled as a Pearson distribution. We derive new closed form expressions for false alarm and detection probabilities for the CA-CFAR detector in the presence of Pearson-distributed clutter backgrounds. We first show that the use of binary integration improves the detection probabilities of the detectors considered. Secondly, the maximum of detection probability occurs for an optimum choice when the second threshold is set to be equal to M = (3/4) L. For this optimum M-out-of-L rule, the comparison analysis of the CA-CFAR and OS-CFAR binary integrators showed that the latter has better performance in homogeneous Pearson- distributed clutter.

Modern digital system designs have been predominantly software driven, using the hardware description languages. While there are many different types of the hardware description languages, the most commonly used are VHDL (very high-speed integrated circuit hardware description language) and the Verilog. Both lend themselves to the successful realizations of the digital systems. However, even the experienced designers are often confused about which one to use and their relative performance characteristics. The authors discuss the general features of both languages from a design standpoint. Also, VHDL and Verilog based coding are discussed and the design realizations and simulations of the results are presented. We present the case studies of fundamental digital units such as full adders, comparators, counters and shift registers and discuss the timing and other important aspects which can be integrated into the actual designs. Also, the authors present a working model for hardware realizations using the CPLD (complex programmable logic device) platforms of the software designs. While it is acceptable to functionally check the designs with simulations, in actual applications, there is a need for hardware realizations as well. There are instances where the program code is sequential and the simulation results are also sequential. However, with the present complexity of CPLDs, and FPGAs where some hardware elements can be synthesized in concurrence, there can be timing problems. The authors also examine such instances and provide an insight into the arbitration schemes for a reliable digital system realization.

The measurement of optical fibre vibration is a key part of optic fibre pre-warning system, which has gradually focused on phase-sensitive optical time-domain reflectometer. However, for this instrument, false alarm rate is very high and some unstable intrusion signals cannot be detected by using its fixed threshold method in the actual application. It needs to develop new vibration detection method to overcome the above defect. The vibration signals normally consist of three parts, that is, noise, interference and intrusion signals. After a large number of data analysis, the authors find that the system noise is time varying and follows the Rayleigh distribution. Hence, the authors innovatively use the constant false alarm rate (CFAR) method to detect this type of intrusion. Considering interference is also time varying and diverse, a good detection performance cannot be obtained only by using the conventional CFAR. For this reason, a background homogeneity adaptive CFAR (BHA-CFAR) method is further proposed to detect the vibration signals in this study. The BHA-CFAR consists of two detectors, cell averaging CFAR (CA-CFAR) detector and greatest-of/smallest-of CFAR (GO/SO-CFAR) detector. A parameter, homogeneity of background, is estimated first to classify the surrounding. Then CA-CFAR and GO/SO-CFAR are optionally used according to the surrounding is homogeneous or heterogeneous, respectively. This new detection method can adapt to any background surrounding and has a good detection performance. In order to check the feasibility and validity of the BHA-CFAR method, several experiments were carried out in Da Gang oilfield. The detection results show that the proposed method can provide a good tradeoff between the detection performance and computation time.

The ordered-statistics (OS) constant false-alarm rate (CFAR) is relatively immune to the presence of interfering targets among the reference cells used to determine the average background. OS CFAR performance in a multitarget environment was previously studied by simulation. The author obtains analytic expressions for the added detection loss, assuming strong interfering targets. The real target is assumed to be a Rayleigh fluctuating target. Numerical examples are included.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The region of interest (ROI) contains a potential target in synthetic aperture radar (SAR) image. Picking out ROI chips before target recognizing will accelerate the whole automatic target recognition (ATR) system. This paper improves the traditional method to obtain ROI chips through constant false alarm rate (CFAR) algorithm. With the help of integral image, we reduce redundant calculation in traditional cell average CFAR (CA-CFAR) to increase its computing speed, which is denoted as the Integral Image CFAR (II-CFAR). Finally we compare the ROI extraction results of the traditional CA-CFAR and the proposed II-CFAR on the same real SAR image. The results show that the II-CFAR detector can obtain the same detection performance with the CA-CFAR detector but with a much shorter running time.

Constant false alarm rate (CFAR), a target detection method commonly used in the radar systems, has an inconsistent performance against various environments. For improving the radar detectability, this paper proposes a novel scheme of radar target detection using neural network-based adaptive selection CFAR. The proposed method employs cell-averaging, ordered-statistic, greatest-of and smallest-of CFAR thresholds as the basis of references. The pattern of those threshold values combined with the cell under test signal value will be identified and classified by the neural network to compute the raw threshold. Then, the final threshold is selected depending on the nearest value between raw and four referenced CFARs. The performance of the proposed method is examined against three possible cases of the radar systems including homogeneous background, multiple targets and clutter boundary. The result of this research shows that the proposed method outperforms the classical CFARs due to the adaptive selection algorithm can select properly among referenced CFARs against the given cases particularly in the homogeneous and multiple target environments.

Focus on software, data acquisition, and instrumentation

As the first step of automatic image interpretation systems, automatic detection of the targets should be accurate and fast. Constant False Alarm Rate (CFAR) is the most popular target detection framework for Synthetic Aperture Radar (SAR) images. For CFAR, modeling of the clutter is crucial since the decision threshold is calculated based on this model. We have developed a new target detection approach in which clutter is modeled using a Rayleigh mixture model that fits well to a variety of high-resolution SAR imagery. For computational efficiency, we use summed area tables (SAT) for computing background statistics. The resulting approach, called RmSAT-CFAR, is a promising general-purpose SAR target detection tool. This paper describes the open-source software for RmSAT-CFAR. Details of RmSAT-CFAR is published in the study named Fast Target Detection in Radar Images using Rayleigh Mixtures and Summed Area Tables. In addition to Rayleigh mixture and SATs, the software also uses tiling and parallelization to obtain faster and scalable results. This software repository also contains open source implementations for following algorithms: (a) Cell Averaging CFAR (CA-CFAR), (b) Automatic Censored CFAR (AC-CFAR), and (c) Adaptive and Fast CFAR (AAF-CFAR).

As a kind of high resolution radar, Synthetic Aperture Radar (SAR) is very important in many military applications. Frequency modulated continuous wave (FMCW) synthetic aperture radar (SAR), combining FMCW technique with synthetic aperture imaging methods, is a newly proposed imaging radar system, and has the advantages such as small cubage, light weight, cost-effective, high-resolution, etc. However, for FMCW SAR, the platform continuously moves while transmitting and receiving signal. The duration of the transmitted pulse increases. The stop-and-go approximation is invalid. So the proper algorithms have to be used to achieve the expected results. For the peculiarity of FMCW SAR, the signal processing is deduced in detail when stop-and-go approximation is invalid, and a modification to RD is presented in order to compensate Doppler frequency shift. The simulation results are finally presented.

Linearly combined order statistic (LCOS) constant false-alarm rate (CFAR) detectors are examined for efficient and robust threshold estimation applied to exponentially distributed background observations for improved detection. Two optimization philosophies have been employed to determine the weighting coefficients of the order statistics. The first method optimizes the coefficients to obtain efficient estimates of clutter referred to the censored maximum likelihood (CML) and best linear unbiased (BLU) CFAR detectors. The second optimization involves maximizing the probability of detection under Swerling II targets and is referred to as the most powerful linear (MPL) CFAR detector. The BLU-CFAR detector assumes no knowledge of the target distribution in contrast to the MPL-CFAR detector which requires partial knowledge of the target distribution. The design of these CFAR detectors and the probability of detection performance are mathematically analyzed for background observations having homogeneous and heterogeneous distributions wherein the trade-offs between robustness and detection performance are illustrated. >

A collision avoidance system, also known as a pre-crash system, forward collision warning system, or collision mitigation system, is a sophisticated driver-assistance system that aims to avoid or mitigate the severity of a collision. For this research, collision avoidance system will be fabricating to show that this system can detect avoidance range before apply the braking action to prevent collision. The ultrasonic sensor will be used in this system to detect the avoidance range. In this collision avoidance system, there will be uses of Field Programmable Gate Array (FPGA) and Complex Programmable Logic Device (CPLD). This research will observe how to implement FPGA and CPLD in the collision avoidance system using VHSIC Hardware Description Language (VHDL). The VHDL will be done in Quartus II 15.0 Software. In this research, Terasic DE-10 Standard board has been used. It contains FPGA microcontroller model Cyclone V SoC 5CSXFC6D6F31C6N. Max II board is used because it contains CPLD microcontroller model EPM240T100C5.