Achievable Detection Performance Research Articles

Entropy-based multipath detection model for MIMO radar

An optimized detection model based on weighted entropy for multiple input multiple output (MIMO) radar in multipath environment is presented. After defining the multipath distance difference (MDD), the multipath received signal model with four paths is built systematically. Both the variance and correlation coefficient of multipath scattering coefficient with MDD are analyzed, which indicates that the multipath variable can decrease the detection performance by reducing the echo power. By making use of the likelihood ratio test (LRT), a new method based on weighted entropy is introduced to use the positive multipath echo power and suppress the negative echo power, which results in better performance. Simulation results show that, compared with non-multipath environment or other recently developed methods, the proposed method can achieve detection performance improvement with the increase of sensors.

Automated misspelling detection and correction in clinical free-text records

Accurate electronic health records are important for clinical care and research as well as ensuring patient safety. It is crucial for misspelled words to be corrected in order to ensure that medical records are interpreted correctly. This paper describes the development of a spelling correction system for medical text. Our spell checker is based on Shannon’s noisy channel model, and uses an extensive dictionary compiled from many sources. We also use named entity recognition, so that names are not wrongly corrected as misspellings. We apply our spell checker to three different types of free-text data: clinical notes, allergy entries, and medication orders; and evaluate its performance on both misspelling detection and correction. Our spell checker achieves detection performance of up to 94.4% and correction accuracy of up to 88.2%. We show that high-performance spelling correction is possible on a variety of clinical documents.

Signal detection with noisy reference for passive sensing

In many detection applications, the signal to be detected, referred to as target signal, is not directly available. A reference channel (RC) is often deployed to collect a noise-contaminated version of the target signal to serve as a reference, which is then used to assist detecting the presence/absence of the target signal in a test channel (TC). A standard approach is to cross-correlate (CC) the signals received in the TC and RC, respectively. When the signal-to-noise ratio (SNR) in the RC is high, the CC behaves like the optimum matched filter. However, when the SNR in the RC is low, the CC detector suffers significant degradation. This paper considers the above detection problem with a noisy reference signal. We propose four detectors based on the generalized likelihood ratio test principle, by treating the unknown target signal to be deterministic or stochastic and under conditions whether the noise variance is known or unknown. Our results demonstrate that the noise in the RC has an impact on the achievable detection performance. However, when the reference signal is noisy, three of the proposed detectors offer substantial improvements in detection performance over the CC detector.

LPI Optimization Framework for Target Tracking in Radar Network Architectures Using Information-Theoretic Criteria

Widely distributed radar network architectures can provide significant performance improvement for target detection and localization. For a fixed radar network, the achievable target detection performance may go beyond a predetermined threshold with full transmitted power allocation, which is extremely vulnerable in modern electronic warfare. In this paper, we study the problem of low probability of intercept (LPI) design for radar network and propose two novel LPI optimization schemes based on information-theoretic criteria. For a predefined threshold of target detection, Schleher intercept factor is minimized by optimizing transmission power allocation among netted radars in the network. Due to the lack of analytical closed-form expression for receiver operation characteristics (ROC), we employ two information-theoretic criteria, namely, Bhattacharyya distance and J-divergence as the metrics for target detection performance. The resulting nonconvex and nonlinear LPI optimization problems associated with different information-theoretic criteria are cast under a unified framework, and the nonlinear programming based genetic algorithm (NPGA) is used to tackle the optimization problems in the framework. Numerical simulations demonstrate that our proposed LPI strategies are effective in enhancing the LPI performance for radar network.

Genetic Algorithm Parameter Requirements for Detection in MIMO Fading Channels

For multiple-input/multiple-output (MIMO) wireless communications systems employing spatial multiplexing transmission, we evaluate by simulation the parameter (i.e., population size, generation number) value requirements for detection based on genetic algorithm (GA) at the receiver. We assume transmit-correlated Rayleigh or Rician fading with realistic Laplacian power azimuth spectrum as well as azimuth spread (AS) and Rician K-factor selected according to the measurement-based WINNER II channel model, for several relevant scenarios. We first confirm that a GA whose parameters are suitably set converges to maximum-likelihood (ML)-like performance. Then, we study the effects of the number of antennas, modulation constellation size, scenario (i.e., AS and K values), and rank of the deterministic component of the channel matrix on parameter values required for GA in order to converge to ML-like performance. We find that, for poorer channel fading conditions, i.e., poorer achievable MIMO detection performance, the GA converges faster and for smaller population size. Therefore, selecting the GA parameter values according to the channel features may help achieve ML-like performance for lower complexity.

Multiuser Detection Assisted Time- and Frequency-Domain Spread Multicarrier Code-Division Multiple-Access

In this contribution, we study a reduced-complexity multiuser detection aided multicarrier direct-sequence code-division multiple-access (MC DS-CDMA) scheme, which employs both time (T)-domain and frequency (F)-domain spreading. We investigate the achievable detection performance in the context of synchronous TF-domain spread MC DS-CDMA when communicating over an additive white Gaussian noise (AWGN) channel. Five detection schemes are investigated, which include the single-user correlation based detector, the joint TF-domain decorrelating multiuser detector (MUD), the joint TF-domain MMSE MUD, the separate TF-domain decorrelating/MMSE MUD, and the separate TF-domain MMSE/decorrelating MUD. Our simulation results show that the separate TF-domain MUD schemes are capable of achieving a similar bit error rate (BER) performance to that of the significantly more complex joint TF-domain MUD schemes.

The use of fuzzy spaces in signal detection

The fuzzy constant false alarm rate (CFAR) detector, which is based on the M-out-of- N binary detector, is characterized and compared with the optimal Neyman–Pearson detector. It replaces the crisp M-out-of- N binary threshold with a soft, continuous threshold, implemented as a membership function. This function is chosen so that the output is equal to the false alarm rate of the binary detector, and therefore maps the observation set to a false alarm space corresponding to the false alarm rate, P FA . An analogous membership function is also developed mapping observations to a detection space which corresponds to the detection rate, P D . These two spaces allow different detectors to be compared directly with respect to the two important detection performance indices, P FA and P D . Comparison of the false alarm space and detection space indicates that the fuzzy CFAR detector and Neyman–Pearson detector detect signals in a different manner and have different detection properties. Nevertheless, performance results illustrate that the fuzzy CFAR detector achieves detection performance comparable to the optimal Neyman–Pearson detector.

Signal Detection Analysis of the Ideal Group

Theorems derived from the Theory of Signal Detectability are applied to the group decision problem. These theorems specify the behavior of the Ideal Group - the group having the highest achievable detection performance given an array of members with specified individual indices of detectability. Equations are provided for relating group performance to (1) the size of the group, (2) the detectability indices of the individual members, and (3) the correlation between member observations of the stimulus. The efficiency of real groups, such as juries and committees, is briefly discussed.