New CFAR algorithm and circuit development for radar receiver

Automatic target detection radar requires adaptive thresholding achieved by the Constant False Alarm Rate (CFAR) circuit to control the false alarm caused by variations in the background clutter. This thesis deal with the problem that happened when an abrupt variation in background clutter merged with a multi-interfering target, and when the clutter cloud itself centered with multi-interfering targets. To detect targets in such environments, it needs a robust CFAR algorithm that excises the target spikes and clutter edges from the CFAR window to give the best possible estimation of the noise background. The Maximum Spike Subtraction MSS-CFAR family that uses two lock circuits to find two maximum spikes in the CFAR window that subtracted from sample summing to make better background noise estimation that used to construct an adaptive threshold. The MSS-CFAR family is MSS-CA�CFAR, MSS-GO-CFAR, and MSS-SO-CFAR, MSS-CFAR family in addition to two core algorithms were studied which are cell averaged CA-CFAR family that includes the greatest of GO-CFAR and smallest of SO-CFAR and ordered statistics OS-CFAR family that include greatest of ordered statistics OSGO-CFAR and the smallest of ordered statistics OSSO-CFAR. All these algorithms are simulated using MATLAB and applied them to three different clutter models that represent different environment cases. The CA-CFAR family failed to handle models two and three also OS-CFAR family except for OS-CFAR that handle all models successfully. For the MSS-CFAR family, MSS-CA-CFAR could handle all models successfully, and comparing with OS-CFAR, the MSS-CA-CFAR need less hardware and processing time because it did not need a sorting process that is essential for OS-CFAR. Therefore, the MSS-CA-CFAR is chosen to implement by practical digital circuit and there is another important feature in the MSS-CFAR algorithm that is parallel processing since the spike selection process is done at the same time with summing of samples process that makes this algorithm much less in processing time from any other algorithm using the same environment. The last MATLAB test for MSS-CA- vi CFAR with a spiky exponential model shown in Table 4.3 in chapter four shows clearly that MSS-CA-CFAR detects nine targets from ten that means the efficiency of detection of the proposed method is 90%. The field-programmable gate array FPGA chip that is used to implement the MSS-CA-CFAR algorithm needs only three signals from the radar receiver to match with the receiver circuit correctly which are time base clock signal period reset trigger signal and the pulse duration time

Differences a Day Can Make: Exploring the Effects of Abbreviated Intervention on Improving Financial Management for Youth-serving Organizations

This report by the management consulting firm CFAR examines the effectiveness of a one-day workshop and series of webinars offered to nonprofits by the consulting firm FMA as part of a Wallace Foundation initiative to strengthen the financial management of afterschool nonprofits. CFAR provides suggestions for the development of future training events to help nonprofits improve their financial stability and planning

Efficient Approach for OS-CFAR 2D Technique Using Distributive Histograms and Breakdown Point Optimal Concept applied to Acoustic Images

In this work, a new approach to improve the algorithmic efficiency of the Order Statistic-Constant False Alarm Rate (OS-CFAR) applied in two dimensions (2D) is presented. OS-CFAR is widely used in radar technology for detecting moving objects as well as in sonar technology for the relevant areas of segmentation and multi-target detection on the seafloor. OS-CFAR rank orders the samples obtained from a sliding window around a test cell to select a representative sample that is used to calculate an adaptive detection threshold maintaining a false alarm probability. Then, the test cell is evaluated to determine the presence or absence of a target based on the calculated threshold. The rank orders allows that OS-CFAR technique to be more robust in multi-target situations and less sensitive than other methods to the presence of the speckle noise, but requires higher computational effort. This is the bottleneck of the technique. Consequently, the contribution of this work is to improve the OS-CFAR 2D with the distributive histograms and the optimal breakdown point optimal concept, mainly from the standpoint of efficient computation. In this way, the OS-CFAR 2D on-line computation was improved, by means of speeding up the samples sorting problem through the improvement in the calculus of the statistics order. The theoretical algorithm analysis is presented to demonstrate the improvement of this approach. Also, this novel efficient OS-CFAR 2D was contrasted experimentally on acoustic images.Fil: Villar, Sebastian Aldo. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ingeniería Olavarría. Departamento de Electromecánica. Grupo INTELYMEC; ArgentinaFil: Menna, Bruno Victorio. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ingeniería Olavarría. Departamento de Electromecánica. Grupo INTELYMEC; ArgentinaFil: Torcida, Sebastián. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Departamento de Matemática; ArgentinaFil: Acosta, Gerardo Gabriel. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ingeniería Olavarría. Departamento de Electromecánica. Grupo INTELYMEC; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; Argentin

No double trouble: How to reopen the economy.

This policy introduces a measure of choice, consonant with our culture. Those younger than 65 can make their own personal tradeoffs between heath and livelihood, while older people, knowing that the virus will be spreading more quickly through the population will be even more cautious, thus preventing their early deaths. We return decisions to people while ensuring that the sum total of decisions does not overwhelm our hospitals. One felicitous result of this policy is that the virus will spread more quickly through the healthier population. This means that when the elderly re-engage in social life they will encounter fewer rather than more infected people reducing the likelihood that they will become sick and die themselves. Ironically, the best way to protect seniors is to let the virus spread in a controlled fashion among those who are not

CFAR matched direction detector

In a previously published paper by Besson et al., we considered the problem of detecting a signal whose associated spatial signature is known to lie in a given linear subspace, in the presence of subspace interference and broadband noise of known level. We extend these results to the case of unknown noise level. More precisely, we derive the generalized-likelihood ratio test (GLRT) for this problem, which provides a constant false-alarm rate (CFAR) detector. It is shown that the GLRT involves the largest eigenvalue and the trace of complex Wishart matrices. The distribution of the GLRT is derived under the hypothesis. Numerical simulations illustrate its performance and provide a comparison with the GLRT when the noise level is known

Adaptive detection of a signal known only to lie on a line in a known subspace, when primary and secondary data are partially homogeneous

This paper deals with the problem of detecting a signal, known only to lie on a line in a subspace, in the presence of unknown noise, using multiple snapshots in the primary data. To account for uncertainties about a signal's signature, we assume that the steering vector belongs to a known linear subspace. Furthermore, we consider the partially homogeneous case, for which the covariance matrix of the primary and the secondary data have the same structure but possibly different levels. This provides an extension to the framework considered by Bose and Steinhardt. The natural invariances of the detection problem are studied, which leads to the derivation of the maximal invariant. Then, a detector is proposed that proceeds in two steps. First, assuming that the noise covariance matrix is known, the generalized-likelihood ratio test (GLRT) is formulated. Then, the noise covariance matrix is replaced by its sample estimate based on the secondary data to yield the final detector. The latter is compared with a similar detector that assumes the steering vector to be known