Object Detector on Coastal Surveillance Radar Using Two-Dimensional Order-Statistic Constant-False Alarm Rate Algoritm

This paper describes the development of radar object detection using two dimensional constant false alarm rate (2D-CFAR). Objective of this development is to minimize noise detection if compared with the previous algorithm that uses one dimensional constant false alarm rate (1D-CFAR) algorithm such as order-statistic (OS) CFAR, cell-averaging (CA) CFAR, AND logic (AND) CFAR and variability index (VI) CFAR where has been implemented on coastal surveillance radar. The optimum detection result in coastal surveillance radar testing when Pfa set to 1e-2, Kth set to 3/4*Nwindow and Guard Cell set to 0. Principle of 2D-CFAR algorithm is combining of two CFAR algorithms for each array data of azimuth and range. Order statistic (OS) CFAR algoritm is implemented on this 2D-CFAR by fusion rule of AND logic.The algorithm of 2D-CFAR is developed using Microsoft Visual C++ 2008 and the output of 2D-CFAR is plotted on PPI scope radar using GDI+ library. The result of 2D-CFAR development shows that 2D-CFAR can minimize noise detected if compared with 1D-CFAR with the same parameter of CFAR. Best performance of 2D-CFAR in object detection when Nwindow set to 128. The time of software processing of 2D-CFAR is about two times longer than the 1D-CFAR

Evaluation of AND-CFAR and OR-CFAR Processors Under Different Clutter Models

In this paper an evaluation the detection performances for (AND-CFAR) and (OR-CFAR) processors under different clutter models is done for pulsed radar system. The clutter models used in this paper are three types of distribution (Exponential Clutter distribution, Rayleigh Clutter distribution and Weibull Clutter distribution). The two detectors (AND-CFAR and OR-CFAR) are the improved conventional Cell Average-CFAR (CA-CFAR) and Order Statistics CFAR (OS-CFAR) by making full use of the cell information. The two CFAR processors combine the results of the CA-CFAR and OS-CFAR to get a better detection performance. The mathematical equations of the probability of detection and probability of false alarm to the two detectors (AND-CFAR and OR-CFAR) for the three clutter models are founded under the assumptions that a homogenous background with a Swerling I target, and the reference cells are Independent and Identically Distributed (IID), also the detection performances of these detectors (AND-CFAR and OR-CFAR) have been evaluated for the three clutter models first and compared between them with those of CA-CFAR and OS-CFAR for Rayleigh distribution using MATLAB-Programming (M-File). In this paper is founded that the detection performance to AND-CFAR and OR-CFAR has not been affected by changing clutter models (clutter probability density function) for fixed probability of false alarm, also for the same probability of detection (Pd=0.7) and for fixed probability of false alarm (Pfa=10-6) and N=12, signal-to-ratio power ration (SNR) which ensure this Pd is equal (18dB) for AND-CFAR, but its equal (18.5dB) for OR-CFAR, this means AND-CFAR is better detection performance than OR-CFAR for different clutter models

An Automatic Ship Detection Method Based on Local Gray-Level Gathering Characteristics in SAR Imagery

This paper proposes an automatic ship detection method based on gray-level gathering characteristics of synthetic aperture radar (SAR) imagery. The method does not require any prior knowledge about ships and background observation. It uses a novel local gray-level gathering degree (LGGD) to characterize the spatial intensity distribution of SAR image, and then an adaptive-like LGGD thresholding and filtering scheme to detect ship targets. Experiments on real SAR images with varying sea clutter backgrounds and multiple targets situation have been conducted. The performance analysis confirms that the proposed method works well in various circumstances with high detection rate, fast detection speed and perfect shape preservation

Some contributions on MIMO radar

Motivated by recent advances in Multiple Input Multiple Output (MIMO) wireless communications, this dissertation aims at exploring the potential of MIMO approaches in the radar context. In communications, MIMO systems combat the fading effects of the multi-path channel with spatial diversity. Further, the scattering environment can be used by such systems to achieve spatial multiplexing. In radar, a complex target consisting of several scatterers takes the place of the multi-path channel of the communication problem. A target\u27s radar cross section (RCS), which determines the amount of returned power, greatly varies with the considered aspect. Those variations significantly impair the detection and estimation performance of conventional radar employing closely spaced arrays on transmit and receive sides. In contrast, by widely separating the transmit and receive elements, MIMO radar systems observe a target simultaneously from different aspects resulting in spatial diversity. This diversity overcomes the fluctuations in received power. Similar to the multiplexing gain in communications, the simultaneous observation of a target from several perspectives enables resolving its features with an accuracy beyond the one supported by the bandwidth. The dissertation studies the MIMO concept in radar in the following manner. First, angle of arrival estimation is explored for a system applying transmit diversity on the transmit side. Due to the target\u27s RCS fluctuations, the notion of ergodic and outage Cramer Rao bounds is introduced. Both bounds are compared with simulation results revealing the diversity potentials of MIMO radar. Afterwards, the detection of targets in white Gaussian noise is discussed including geometric considerations due to the wide separation between the system elements. The detection performance of MIMO radar is then compared to the one achieved by conventional phased array radar systems. The discussion is extended to include returns from homogeneous clutter. A Doppler processing based moving target detector for MIMO radar is developed in this context. Based on this detector, the moving target detection capabilities of MIMO radar are evaluated and compared to the ones of phased array and multi-static radar systems. It is shown, that MIMO radar is capable of reliably detecting targets moving in an arbitrary direction. The advantage of using several transmitters is illustrated and the constant false alarm rate (CFAR) property of adaptive MIMO moving target detectors is demonstrated. Finally, the high resolution capabilities of MIMO radar are explored. As noted above, the several individual scatterers constituting a target result in its fluctuating RCS. The high resolution mode is aimed at resolving those scatterers. With Cramer Rao bounds and simulation results, it is explored how observing a single isotropic scatterer from several aspects enhances the accuracy of estimating the location of this scatterer. In this context a new, two-dimensional ambiguity function is introduced. This ambiguity function is used to illustrate that several scatterers can be resolved within a conventional resolution cell defined by the bandwidth. The effect of different system parameters on this ambiguity function is discussed

Multi-Target Detection Capability of Linear Fusion Approach Under Different Swerling Models of Target Fluctuation

In evolving radar systems, detection is regarded as a fundamental stage in their receiving end. Consequently, detection performance enhancement of a CFAR variant represents the basic requirement of these systems, since the CFAR strategy plays a key role in automatic detection process. Most existing CFAR variants need to estimate the background level before constructing the detection threshold. In a multi-target state, the existence of spurious targets could cause inaccurate estimation of background level. The occurrence of this effect will result in severely degrading the performance of the CFAR algorithm. Lots of research in the CFAR design have been achieved. However, the gap in the previous works is that there is no CFAR technique that can operate in all or most environmental varieties. To overcome this challenge, the linear fusion (LF) architecture, which can operate with the most environmental and target situations, has been presented

The Design of a Low-Cost Traffic Calming Radar - Development of a radar solution intended to demonstrate proof of concept

This study aimed to develop a radar solution that would aid the traffic calming efforts of the CSIR business campus. The Institute of Transportation Engineers defined traffic calming as "The combination of mainly physical measures that reduce the negative effects of motor vehicle use." Radar-based solutions have been proven to help reduce the speeds of motorists in areas with speed restrictions. Unfortunately, these solutions are expensive and difficult to import. Thus, this dissertation's main focus is to produce a detailed blueprint of a radar-based solution, with technical specifications that are similar to those of commercial and experimental systems at relatively low-cost. With the above mindset, the project was initiated with the user requirements being stated. Then a detailed study of current experimental and commercial radar-based traffic calming systems followed. Thereafter, the technical and non-technical requirements were derived from user requirements, and the technical specifications obtained from the literature study. A review of fundamental radar and signal processing principles was initiated to give background knowledge for the design and simulation process. Consequently, a detailed design of the system's functional components was conceptualized, which included the hardware, software, and electrical aspects of the system as well as the enclosure design. With the detailed design in mind, a data-collection system was built. The data-collection system was built to verify whether the technical specifications, which relate to the detection performance and the velocity accuracy of the proposed radar design, were met. This was done to save on buying all the components of the proposed system while proving the design's technical feasibility. The data-collection system consisted of a radar sensor, an Analogue to Digital Converter (ADC), and a laptop computer. The radar sensor was a k-band, Continuous Wave (CW) transceiver, which provided I/Q demodulated data with beat frequencies ranging from DC to 50 kHz. The ADC is an 8-bit Picoscope 2206B portable oscilloscope, capable of sampling frequencies of up to 50 MHz. The target detection and the velocity estimation algorithms were executed on a Samsung Series 7 Chronos laptop. Preliminary experiments enabled the approximation of the noise intensity of the scene in which the radar would be placed. These noise intensity values enabled the relationship between the Signal to Noise Ratio (SNR) and the velocity error to be modelled at specific ranges from the radar, which led to a series of experiments that verified the prototypes' ability to accurately detect and estimate the vehicle speed at distances of up to 40 meters from the radar. The cell-averaging constant false alarm rate (CA-CFAR) detector was chosen as an optimum detector for this application, and parameters that produced the best results were found to be 50 reference cells and 12 guard cells. The detection rate was found to be 100% for all coherent processing intervals (CPIs) tested. The prototype was able to detect vehicle speeds that ranged from 2 km/h up to 60 km/h with an uncertainty of ±0.415 km/h, ±0.276 km/h, and ±0.156 km/h using a CPI of 0.0128 s, 0.256 s, and 0.0512 s respectively. The optimal CPI was found to be 0.0512 s, as it had the lowest mean velocity uncertainty, and it produced the largest first detection SNR of the CPIs tested. These findings were crucial for the feasibility of manufacturing a low-cost traffic calming solution for the South African market

Evaluation of CFAR detectors performance

The operation of coastal and off-shore radars is affected because the targets are surrounded by a background filled with sea clutter. According on the Neyman-Pearson criterion, radar detectors must always try to maintain a constant false alarm probability before trying to improve other system variables. Using the MATLAB mathematic software, the authors evaluated the performance of the CA, OS, MSCA, AND, OR and ISCFAR processors concerning their ability to maintain the constant false alarm probability conceived in the design. After testing the schemes with different test profiles whose samples were Rayleigh distributed, it was concluded that most of the alternatives exhibit problems when facing certain situations that may appear in real environments. Consequently, recommendations on which solution is best to use are offered for guaranteeing a reduced deviation of the operational false alarm probability from the value conceived in the design when processing heterogeneous clutter. La operación de los radares costeros y oceánicos se ve afectada porque los blancos se encuentran embebidos en un fondo de clutter marino. De acuerdo con el criterio de Neyman-Pearson, los detectores de radar siempre buscan garantizar un valor determinado de probabilidad de falsa alarma antes de mejorar otras variables del sistema. Utilizando la herramienta matemática MATLAB, los autores evaluaron el desempeño de los procesadores CA, OS, MSCA, AND, OR e IS-CFAR con respecto al mantenimiento de la probabilidad de falsa alarma concebida a priori en el diseño. Luego de someter los esquemas a diferentes perfiles de prueba con clutter distribuido Rayleigh, se concluyó que la mayoría de las alternativas presentan problemas ante determinadas situaciones que pueden aparecer con relativa frecuencia en ambientes reales. Consecuentemente, se ofrecen recomendaciones sobre cuál es el mejor esquema para emplear y garantizar una desviación reducida de la probabilidad de falsa alarma operacional con respecto a la de diseño cuando se enfrenta clutter heterogéneo

Détection robuste de cibles en imagerie Hyperspectrale.

Hyperspectral imaging (HSI) extends from the fact that for any given material, the amount of emitted radiation varies with wavelength. HSI sensors measure the radiance of the materials within each pixel area at a very large number of contiguous spectral bands and provide image data containing both spatial and spectral information. Classical adaptive detection schemes assume that the background is zero-mean Gaussian or with known mean vector that can be exploited. However, when the mean vector is unknown, as it is the case for hyperspectral imaging, it has to be included in the detection process. We propose in this work an extension of classical detection methods when both covariance matrix and mean vector are unknown.However, the actual multivariate distribution of the background pixels may differ from the generally used Gaussian hypothesis. The class of elliptical distributions has already been popularized for background characterization in HSI. Although these non-Gaussian models have been exploited for background modeling and detection schemes, the parameters estimation (covariance matrix, mean vector) is usually performed using classical Gaussian-based estimators. We analyze here some robust estimation procedures (M-estimators of location and scale) more suitable when non-Gaussian distributions are assumed. Jointly used with M-estimators, these new detectors allow to enhance the target detection performance in non-Gaussian environment while keeping the same performance than the classical detectors in Gaussian environment. Therefore, they provide a unified framework for target detection and anomaly detection in HSI.L'imagerie hyperspectrale (HSI) repose sur le fait que, pour un matériau donné, la quantité de rayonnement émis varie avec la longueur d'onde. Les capteurs HSI mesurent donc le rayonnement des matériaux au sein de chaque pixel pour un très grand nombre de bandes spectrales contiguës et fournissent des images contenant des informations à la fois spatiale et spectrale. Les méthodes classiques de détection adaptative supposent généralement que le fond est gaussien à vecteur moyenne nul ou connu. Cependant, quand le vecteur moyen est inconnu, comme c'est le cas pour l'image hyperspectrale, il doit être inclus dans le processus de détection. Nous proposons dans ce travail d'étendre les méthodes classiques de détection pour lesquelles la matrice de covariance et le vecteur de moyenne sont tous deux inconnus.Cependant, la distribution statistique multivariée des pixels de l'environnement peut s'éloigner de l'hypothèse gaussienne classiquement utilisée. La classe des distributions elliptiques a été déjà popularisée pour la caractérisation de fond pour l’HSI. Bien que ces modèles non gaussiens aient déjà été exploités dans la modélisation du fond et dans la conception de détecteurs, l'estimation des paramètres (matrice de covariance, vecteur moyenne) est encore généralement effectuée en utilisant des estimateurs conventionnels gaussiens. Dans ce contexte, nous analysons de méthodes d’estimation robuste plus appropriées à ces distributions non-gaussiennes : les M-estimateurs. Ces méthodes de détection couplées à ces nouveaux estimateurs permettent d'une part, d'améliorer les performances de détection dans un environment non-gaussien mais d'autre part de garder les mêmes performances que celles des détecteurs conventionnels dans un environnement gaussien. Elles fournissent ainsi un cadre unifié pour la détection de cibles et la détection d'anomalies pour la HSI

The application of digital techniques to an automatic radar track extraction system

'Modern' radar systems have come in for much criticism in recent years, particularly in the aftermath of the Falklands campaign. There have also been notable failures in commercial designs, including the well-publicised 'Nimrod' project which was abandoned due to persistent inability to meet signal processing requirements. There is clearly a need for improvement in radar signal processing techniques as many designs rely on technology dating from the late 1970's, much of which is obsolete by today’s standards. The Durham Radar Automatic Track Extraction System (RATES) is a practical implementation of current microprocessor technology, applied to plot extraction of surveillance radar data. In addition to suggestions for the design of such a system, results are quoted for the predicted performance when compared with a similar product using 1970's design methodology. Suggestions are given for the use of other VLSI techniques in plot extraction, including logic arrays and digital signal processors. In conclusion, there is an illustrated discussion concerning the use of systolic arrays in RATES and a prediction that this will represent the optimum architecture for future high-speed radar signal processors