Probability Of False Alarm Rate Research Articles

Peaks Detector Algorithm after CFAR for Multiple Targets Detection

The constant false alarm rate (CFAR) algorithm is a strong technique to detect and track dynamic targets in an environment of an unknown noise floor. Multiple reflections of a pulse from a target and different signal processing techniques applied to the received pulse, make it spread along the range and/or Doppler axis. Spreading of a pulse results in a cluster of targets detection for a single target when the CFAR technique is applied to it. This causes difficulties in calculating those target’s parameters which require only a single maximum peak for a target, such as Radar cross-section (RCS), relative phase, etc. This manuscript proposes a solution, which extracts a single independent peak for a target that had clusters of peaks after CFAR. The novelty of the algorithm is that it works well to extract a single peak for each of all targets in the multiple targets environment, as compared to the conventional global maxima finding techniques which outputs only one target of the maximum amplitude while suppressing the rest of the small targets. The algorithm is basically a local maxima finder algorithm termed as peaks detector algorithm. An attractive feature of this algorithm is that it neither disturbs the Probability of false alarm rate (Pfa) of CFAR nor it affects the probability of detection (Pd) of a target. The algorithm is tested and its performance is evaluated in a multiple targets environment on the output of 1D and 2D CFAR.

Accumulatively Increasing Sensitivity of Ultrawide Instantaneous Bandwidth Digital Receiver with Fine Time and Frequency Resolution for Weak Signal Detection

It is always an interesting research topic for digital receiver (DRX) designers to develop a DRX with (1) ultrawide instantaneous bandwidth (IBW), (2) high sensitivity, (3) fine time-of-arrival-measurement resolution (TMR), and (4) fine frequency-measurement resolution (FMR) for weak signal detection. This is because designers always want their receivers to have the widest possible IBW to detect far away and/or weak signals. As the analog-to-digital converter (ADC) rate increasing, the modern DRX IBW increases continuously. To improve the signal detection based on blocking FFT (BFFT) method, this paper introduces the new concept of accumulatively increasing receiver sensitivity (AIRS) for DRX design. In AIRS, a very large number of frequency-bins can be used for a given IBW in the time-to-frequency transform (TTFT), and the DRX sensitivity is cumulatively increased, when more samples are available from high-speed ADC. Unlike traditional FFT-based TTFT, the AIRS can have both fine TMR and fine FMR simultaneously. It also inherits all the merits of the BFFT, which can be implemented in an embedded system. This study shows that AIRS-based DRX is more efficient than normal FFT-based DRX in terms of using time-domain samples. For example, with a probability of false alarm rate of 10−7, for N=220 frequency-bins with TMR = 50 nSec, FMR = 2.4414 KHz, IBW > 1 GHz and ADC rate at 2.56 GHz, AIRS-based DRX detects narrow-band signals at about −42 dB of input signal-to-noise ratio (Input-SNR), and just uses a little less than N/2 real-samples. However, FFT-based DRX have to use all N samples. Simulation results also show that AIRS-based DRX can detect frequency-modulated continuous wave signals with ±0.1, ±1, ±10 and ±100 MHz bandwidths at about −39.4, −35.1, −30.2, and −25.5 dB of Input-SNR using about 264.6 K, 104.7 K, 40.2 K and 18.3 K real-samples, respectively, in 220 frequency-bins for TTFT.

Cat-Inspired Deep Convolutional Neural Network for Bone Marrow Cancer Cells Detection

Bone marrow cancer is considered to be the most complex and dangerous disease which results due to an uncontrolled growth of white blood cells called leukocytes. Acute Lymphoblastic Leukemia (ALL) and Multiple Myeloma (MM) are considered to be important categories of bone cancers, which induces a larger number of cancer cells in the bone marrow, results in preventing the production of healthy blood cells. The advent of Artificial Intelligence, especially machine and deep learning, has expanded humanity’s capacity to analyze and detect these increasingly complex diseases. But, accurate detection of cancer cells and reducing the probability of false alarm rates remain on the darker side of the research. This paper proposes novel deep convolutional neural networks in which the hyper parameters are optimized by adaptive Multi-objective CAT algorithms. The proposed model is trained on the preprocessed cell images followed by training with Optimized Convolutional Neural Network (OCNN) and finally detecting the category of cancer cells present in the bone marrow. The extensive experimentations have been carried out using SN-AM datasets and various performance metrics such as accuracy, precision, recall, specificity, and F1-score are calculated and analyzed. The overall accuracy was found to be 99.45% in predicting different categories of cancer and it outperforms the pre-trained deep learning models such as Alexnets, VGG-16 nets, and U-nets. To establish the superiority of the proposed model, we have compared the performance of the proposed model with other machine learning models such as Support Vector Machines, Random Forest, Naïve Bayes, and Artificial Neural Networks. From the above intensive studies, it is clear that the proposed model was able to produce brighter performances than the other learning models in detecting the category of cancer cells.

Robust CFAR Ship Detector Based on Bilateral-Trimmed-Statistics of Complex Ocean Scenes in SAR Imagery: A Closed-Form Solution

A robust constant false alarm rate (RCFAR) detector based on bilateral-trimmed-statistics (BTS-RCFAR) with a closed-form solution is proposed. BTS-RCFAR aims at improving the detection performance in complex ocean scenes such as the multiple-target environment, off-shore, oil-spilled ocean area, etc. In these circumstances, the clutter samples are often contaminated by the outliers. Consequently, the estimated parameters are biased, and the probability density function modeling of the clutter is not accurate. Detection performance deteriorates with either decrease of the detection rate or increase of the false alarm rate. Inspired by Sigma filter, BTS-RCFAR proposes a bilateral-thresholds-based strategy to automatically trim the samples in the local reference window, both the high-intensity and the low-intensity outliers are eliminated. Furthermore, the trimming depth is adaptively derived according to the homogeneity of the clutter backgrounds, where the outliers are completely removed and the real clutter samples can be greatly sustained. Maximum-likelihood-estimator with a closed-form solution is used for parameter estimation using the bilateral-trimmed samples, and log-normal model of the sea clutter can be accurately established. Finally, the test cell is detected given the specified probability of false alarm rate. BTS-RCFAR improves the detection performance in complex ocean scenes by elevating the detection rate and reducing the false alarm rate. Both simulated data and real data are used for validation.

Long-wave infrared multi-wavelength IPDA lidar for standoff detection of chemical warfare agents: theoretical study.

We discuss and evaluate the expected performance of a tunable multi-wavelength integrated-path differential absorption lidar operating in the long-wave infrared between 7.5 and 11 µm, for standoff measurement of chemical agents. Interference issues with natural gas compounds throughout the entire 7.5-11 µm band are first discussed. Then, the study focuses on four interest species, three warfare agents, and a simulant. A performance model is derived and exploited to assess the expectable measurement precision of the lidar for these four species in the integrated-path mode within a 2 min alert time and seventeen emitted wavelengths. Measurement precisions better than the targeted sensitivity levels look reachable at the kilometer range with laser power below 100 mW. Performance optimization strategies are discussed, either by adjusting the pulse energy/pulse repetition rate for a given laser power and lidar range or by reducing the wavelength sequence in an optimal way. Finally the system's receiving operating characteristic curves are derived to describe the expected detection performance in terms of probability of false alarm rate and probability of detection.

Single Acoustic Sensor-Based Time–Frequency Spectrum Sensing Approach for Land Vehicle Detection

This paper introduces a time-frequency spectrum sensing (TFSS) approach based on the short-time Fourier transform (STFT) for vehicle detection using a single acoustic sensor. Spectrum sensing on time-frequency plane offers various applications including vehicle detection, tracking, and classification. The proposed technique estimates the threshold from the power spectral density (PSD) distribution of white Gaussian noise (WGN) which is fitted with Chi-square distribution. This threshold is further used to extract the dominating frequency harmonics and detect the presence of a vehicle from the PSD of the signal on the spectrogram plane. Various performance measures are computed on experimentally generated datasets of the moving vehicles for evaluation of the classical amplitude-based energy detector (ED) and proposed algorithm. The main challenging issue for vehicle monitoring system is, to detect the moving vehicle with a minimum probability of false alarm rate at a low signal-to-noise ratio (SNR). The TFSS can detect the signal at a low SNR level up to −11dB with reduced false alarm rate by 50% as compared to ED. It also provides satisfactory performance measures and receiver operating characteristic curve.

A hybrid model for energy efficient spectrum sensing in cognitive radio

PurposeCognitive radio (CR) plays a very important role in enabling spectral efficiency in wireless communication networks, where the secondary user (SU) allows the licensed primary users (PUs). The purpose of this paper is to develop a prediction model for spectrum sensing in CR.Design/methodology/approachThis paper proposes a hybrid prediction model, called krill-herd whale optimization-based actor critic neural network and hidden Markov model (KHWO-ACNN-HMM). The spectral bands are determined optimally using the proposed hybrid prediction model for allocating the spectrum bands to the PUs. For better sensing, the eigenvalue based on cooperative sensing used in CR. Finally, a hybrid model is designed by hybridizing KHWO-ACNN and HMM to enhance the accuracy of sensing. The predicted results of KHWO-ACNN and HMM are combined by a fusion model, for which a weighted entropy fusion is employed to determine the free spectrum available in CRs.FindingsThe performance of the prediction model is evaluated based on metrics, such as probability of detection, probability of false alarm, throughput and sensing time. The proposed spectrum sensing method achieves maximum probability of detection of 0.9696, minimum probability of false alarm rate as 0.78, minimum throughput of 0.0303 and the maximum sensing time of 650.08 s.Research implicationsThe proposed method is useful in various applications, including authentication applications, wireless medical networks and so on.Originality/valueA hybrid prediction model is introduced for energy efficient spectrum sensing in CR and the performance of the proposed model is evaluated with the existing models. The proposed hybrid model outperformed the other techniques.

Generalized Energy Detector with Iterative Processing of Narrowband Signals in Frequency Domain

The article is devoted to improvement and study of efficiency of the energy detector of narrow-band signals in background of additive noise of unknown power. Analytical expressions describing the probability density distribution of samples of the generalized energy spectrum of noise are obtained. It is shown that the obtained distributions describe well the broadband noise, which differs from the Gaussian one. For the separation of signal and noise samples in the frequency domain, was proposed the decisive statistics in the form of the standard deviation of generalized power spectral density. The threshold value of the decisive statistics for a given probability of false alarm rate in frequency domain was obtained numerically. An iterative algorithm for detecting narrow-band signals in the frequency domain was proposed. A distinctive feature of the developed algorithm is the normalization of the vector of frequency samples to the sum of its elements after each iteration of processing, which consists of recursively calculating the value of decisive statistics, comparing it with the threshold and, if the threshold is exceeded, discarding the maximum frequency sample from the vector. Each dropped sample is signal sample. This approach will allow to detect narrow-band signals in a dynamic range, which is limited only by the maximum level of side lobes of the window function. During the study of the algorithm, it was found that the highest detection quality indicators are achieved when the value of the exponent to which the frequency samples is about 3. The type of window function has little effect on the probability of detection, and this effect decreases with increasing the load on the analysis frequency band. At the same time, the proposed detector remains operable when the analysis frequency band is loaded up to 20%, and its performance is not worse than for the case of a known noise level. If the value of the exponent deviates from 3, the algorithm will be operational with a smaller bandwidth load.

Performance of Threshold Detection in Cognitive Radio with Improved Otsu’s and Recursive One-Sided Hypothesis Testing Technique

Cognitive radio (CR) is a new technology proposed to enhance spectrum efficiency by enabling unlicensed secondary users to access the licensed frequency bands without getting involved with the primary users licensed. Although considered optimal, in order to calculate the signal threshold, this approach requires prior noise statistics information. Even though considered optimal, in order to calculate the signal threshold, this approach requires prior noise statistics information. A prominent example of an Adaptive Threshold Estimation Technique (ATT) for energy detection in Cognitive Radio (CR) is the Recursive One-sided Hypothesis Testing Technique (ROHT). Accurate threshold values are known to be calculated based on the correct choice of their parameter values, which include the standard deviation coefficient and the stop criteria. In this paper, for efficient threshold estimation, the improved Otsu and ROHT are combined for estimating threshold even in the presence of noise floor without need of prior knowledge. The proposed methodology for enactment in cognitive radio sensor networks (CRSN) system based on the adaptive threshold energy detection model with noise variance estimation. The simulation is carried out with the help of Matlab 2017a with the improved Otsu and ROHT techniques. The results obtained shows that improved Otsu and ROHT techniques outperforms that of fixed threshold energy detection in terms of different probability of false alarm rates and miss detections

Hybrid algorithm for two-objective software defect prediction problem

Static software defect prediction problem is one crucial problem in software test, to measure the performance, several indexes are introduced. In this paper, a two-objective software defect prediction model is employed, while probability of false alarm rate and probability of detection are taken as two objectives. To solve this model, one hybrid algorithm combined with support vector machine (SVM) and cuckoo search algorithm is designed. SVM is one general tool for this problem, and the performance is significantly influenced by two parameters. To provide a good classification results, one multi-objective cuckoo search algorithm is designed to optimise these two parameters. In this algorithm, the global best position is extended to be one collection including all non-dominated solutions, and the local search manner is changed to increase the local search speed. Simulation results show our hybrid algorithm is effective.

Hybrid algorithm for two-objective software defect prediction problem

Static software defect prediction problem is one crucial problem in software test, to measure the performance, several indexes are introduced. In this paper, a two-objective software defect prediction model is employed, while probability of false alarm rate and probability of detection are taken as two objectives. To solve this model, one hybrid algorithm combined with support vector machine (SVM) and cuckoo search algorithm is designed. SVM is one general tool for this problem, and the performance is significantly influenced by two parameters. To provide a good classification results, one multi-objective cuckoo search algorithm is designed to optimise these two parameters. In this algorithm, the global best position is extended to be one collection including all non-dominated solutions, and the local search manner is changed to increase the local search speed. Simulation results show our hybrid algorithm is effective.

A COMPARITIVE STUDY OF W-CDMA CELL SEARCH DESIGNS

In this letter an Improved Cell Search Design using cyclic codes (Improved CSD) is compared with the 3GPP Cell Search Design using comma free codes (3GPP-comma free CSD) in terms of (1) hardware utilization on a field programmable gate array and (2) acquisition time measures for different probabilities of false alarm rates. Our results indicate that for an additive white Gaussian noise channel in a high signal-to-noise ratio the Improved CSD achieves faster synchronization with the base station and has lower hardware utilization when compared with the 3GPP-comma free CSD scheme under the same design constraints.