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Conventional methods such as matched filtering, fractional lower order statistics cross ambiguity function, and recent methods such as compressed sensing and track-before-detect are used for target detection by passive radars. Target detection using these algorithms usually assumes that the background noise is Gaussian. However, non-Gaussian impuls...
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... the creation of the confusion matrix, metrics such as PoD, false alarm rate, threat score can be obtained to compare the detection performance of the algorithms. The structure of the confusion matrix used in this study is presented in Figure 7. Two maps are compared to define the confusion matrix. ...
Citations
... Professor Nikias's team and the related researchers who continue to study this situation defined fractional lower-order statistics (FLOS), which can replace the SOS used in the conventional signal processing methods, including robust covariation (ROC) [12], fractional lower-order moment (FLOM) [13,14], and phased fractional lower-order moment (PFLOM) [15]. In this context, the TDE methods based on FLOS were proposed and analyzed in [16][17][18]. Although the TDE methods based on FLOS are robust to impulsive noise, the fractional lower-order parameter of FLOS is determined by the characteristic exponent of α-stable distribution, which is difficult to estimate in practice. ...
In this paper, a generalized logarithmic hyperbolic secant (GLHS) function is introduced that can effectively suppress impulsive noise while guarding the signal of interest from damage. Also, an analysis of the optimal scaling parameter choices for the GLHS function was studied. Then, in order to address the performance drawbacks of the traditional time delay estimation methods based on correlation under an impulsive noise environment, a novel GLHS-based correlation (GLHSC) is further developed, and the reliable time delay estimation result is obtained by finding the peak of GLHSC. The comprehensive Monte Carlo simulation results demonstrate that the performance of the method based on GLHSC is better than other robust competitive methods based on correlation in terms of probability of resolution and estimation accuracy, especially in a heavy-tailed noise environment.
... So, the optimal solution v * is the eigenvector of the smallest generalized eigenvalue of (K T DK, C). Figure 2 gives a flowchart to depict the whole process of the IGED algorithm. The algorithm starts from the initialization procedure, where v 0 is set as [t T PLE , −1] T andt PLE is obtained from (23). Given p and q, matrices C and D can be computed, respectively. ...
This paper considers the problem of robust bearing-only source localization in impulsive noise with symmetric α-stable distribution based on the Lp-norm minimization criterion. The existing Iteratively Reweighted Pseudolinear Least-Squares (IRPLS) method can be used to solve the least LP-norm optimization problem. However, the IRPLS algorithm cannot reduce the bias attributed to the correlation between system matrices and noise vectors. To reduce this kind of bias, a Total Lp-norm Optimization (TLPO) method is proposed by minimizing the errors in all elements of system matrix and data vector based on the minimum dispersion criterion. Subsequently, an equivalent form of TLPO is obtained, and two algorithms are developed to solve the TLPO problem by using Iterative Generalized Eigenvalue Decomposition (IGED) and Generalized Lagrange Multiplier (GLM), respectively. Numerical examples demonstrate the performance advantage of the IGED and GLM algorithms over the IRPLS algorithm.
... Digital Object Identifier (DOI): 10.24138/jcomss.v17i1.1110 [1,4,5], suppression of the interference affecting the surveillance channel [6,7], improving the detection of targets' echoes signals [6,8,9], and estimation of targets' parameters (e.g. velocity and coordinates) [10]- [16]. ...
... Digital Object Identifier (DOI): 10.24138/jcomss.v17i1.1110 [1,4,5], suppression of the interference affecting the surveillance channel [6,7], improving the detection of targets' echoes signals [6,8,9], and estimation of targets' parameters (e.g. velocity and coordinates) [10]- [16]. ...
In a passive radar system, localizing a target in Cartesian space is achieved by using one of the following bistatic geometries: multiple non-cooperative transmitters with one receiver, one non-cooperative transmitter with multiple receivers, or one non-cooperative transmitter with one receiver. In this paper, we propose a new method for localizing a target in Cartesian space by passive radar having the bistatic geometry “one non-cooperative transmitter and one receiver”. This method depends on using two consecutive particle filters for estimating and analyzing the Doppler frequency and time delay of the target’s echo signal. The theoretical analysis of the proposed method is presented, and its efficiency is verified by simulating the passive radar system with a Digital Video Broadcasting-Terrestrial (DVB-T) transmitter.
... The direct signal is reconstructed to detect targets' echoes [3]- [5]. Many researches have been conducted studying this radar, such as studying and analyzing of signals of non-cooperative transmitters (e.g., Frequency Modulation (FM) radio, Global System for Mobile communication (GSM), Digital Video Broadcasting-Terrestrial (DVB-T), and Digital Audio Broadcasting (DAB)) [1], [2], [6]- [9], studying of the interference of the direct signal on the surveillance channel [10], [11], detection of "maneuvering/non-maneuvering" targets [12]- [14], and estimation of their parameters (e.g., Manuscript Doppler frequency, velocity, acceleration, and coordinates) [2], [15]- [19]. ...
In this paper, we estimate Doppler frequency of a maneuvering target being tracked by passive radar using two types of particle filter, the first is “Maximum Likelihood Particle Filter” (MLPF) and the second is “Minimum Variance Particle filter” (MVPF). By simulating the passive radar system that has the bistatic geometry “Digital Video Broadcasting-Terrestrial (DVB-T) transmitter / receiver” with these two types, we can estimate the Doppler frequency of the maneuvering target and compare the simulation results for deciding which type gives better performance.
... Since the performance of conventional communication systems is severely degraded in such an environment, detecting the presence of impulsive noise is an important task in most communication systems [5]. Upon the detection of impulsive noise, a receiver can execute a special filter, such as a non-linearity pre-processor [6,7] which is designed based on the characteristics of impulsive noise, to circumvent the impulsiveness of the noise and further achieve the optimal performance in an impulsive noise environment [8,9]. ...
This paper proposes a new test method of detecting the presence of impulsive noise based on a complementary cumulative density function (CCDF). Impulsive noise severely degrades performance of communication systems and the conventional Kolmogorov–Smirnov (K–S) test may not perform well, because the test does not consider the characteristics of impulsive noise. In order to detect the presence of impulsive noise reliably, the CCDF of measurement samples is analyzed and compared with the CCDF of additive white Gaussian noise to find the difference between those CCDFs. Due to the nature of heavy-tails in impulsive noise, only the maximum difference may not be sufficient for the accurate detection of impulsive noise. Therefore, the proposed method applies the test hypothesis using the weighted sum of all the differences between those CCDFs. Simulation results justify that the proposed test is more robust and provides lower miss detection probability than the K–S test in the presence of impulsive noise.
