This paper deals with the suppression problem of mainlobe digital radio frequency memory (DRFM) jamming especially for intra-pulse slice forwarding jamming for phased array radar. A novel mainlobe DRFM jamming suppression method resorting to blind source separation based on tensor decomposition (TD-BSS) is proposed to separate the target echo signal and jamming signal. Specifically, the received signals are reconstructed into higher-order tensor form. Further, the separating matrix optimization problem relying on tensor Canonical Polyadic (CP) decomposition principle is formulated and then solved by an enhanced line search alternating least square (ELS-ALS) algorithm. Finally, the performance of the proposed method is demonstrated through numerical simulations, showing its superior to suppress mainlobe DRFM jamming over the exiting method in terms of interrupted sampling and cyclic repeater jamming (ISCRJ) and chopping and interval (C&I) jamming.

This paper deals with the problem of mainlobe jamming suppression for phased-array radar system via a joint polarization-space-time processing method resorting to tensor decomposition. Firstly, a dual-polarization receiving array model is established. Then, base on the polarization-space-time characteristics, CANDECOMP/PARAFAC (CP) decomposition is leveraged to recast the received signals as a third-order tensor consisting of three factor matrices. Further, the target echo and the jamming signal can be obtained via minimizing the Frobenius norm of the approximation error between the factor matrices and their iteration forms. Finally, the effectiveness of the proposed method by numerical simulation is verified, showing its capability to resist mainlobe jamming compared with the art competing method.

In this paper, we propose a new Laurent decomposition based low complexity detection scheme for M-ary continuous phase modulation (CPM) signals. By introducing per survivor processing (PSP) algorithm in the demodulation of Laurent-based CPM signals, the proposed scheme simplifies the trellis structure, which reduces the detection complexity of CPM signals. Numerical results reveal that the proposed algorithm can significantly reduce the detection complexity at the price of slight bit-error-rate (BER) disgrace as compared with the conventional detection schemes.

The authors consider an optimisation problem of multistatic radar system (MSRS) deployment, which includes both the antenna placement and the transmitted power allocation. To improve the surveillance performance of MSRS that equipped with different detection methods, they mainly aim at two goals: (i) to improve the coverage ratio of a surveillance region; (ii) to get an even distribution of signal energy in the surveillance region. Through introducing two objective functions for the above two mentioned goals, respectively, they formulate a multi-objective optimisation problem. To overcome drawbacks caused by the significant difference between the values of these two objective functions, they propose a multi-objective particle swarm optimisation (MOPSO) algorithm with non-dominated relative crowding distance (MOPSO-NRCD). Specifically, through the non-dominative relationship comparison and normalisation of crowding distance, a novel method is proposed to select the global best solution. Moreover, a multi-swarm structure is applied to labour division of all particles. Comparing with the MOPSO with the traditional crowding distance, simulation results show that the MOPSO-NRCD can provide better candidate schemes, which are capable to satisfy more stringent performance requirements.

We address the performance prediction of the noncoherent detector (i.e., squared-law plus integrator) for independent but possibly nonidentically distributed (non-id) noncentral Gamma (NCG) fluctuating target model, which is more general and can describe more complex targets. Initially, we obtain the probability density function (pdf) of the sum of independent and non-id NCG random variables in terms of an infinite sum of Gamma pdfs. Furthermore, we develop an analytic expression of the detection probability in terms of converging series based on the generalized Marcum Q function. Finally, at the analysis stage, we first study the truncation error of the derived expression and then evaluate the impacts on the detection performance of the target fluctuating parameters via numerical simulations.

We consider the suppression problem of rangevelocity deception jamming based on adaptive iterative filtering algorithm for pulse Doppler (PD) radar. First, we present the real target signal and false target signal model based on digital radio frequency memory (DRFM). Then, we suppress rangevelocity jamming by adaptive iterative filtering algorithm in range dimension processing and Doppler dimension processing for target signal and jamming respectively. At the stage of analysis, we consider multiple real targets and false targets in range-Doppler plane, and evaluate the suppression performance of the proposed algorithm by simulation. The results highlight that the proposed algorithm will converge fast and yield an excellent suppression performance.

This work concentrates on the suppression of deceptive jamming generated by digital radio frequency memory (DRFM) in multistatic radar system. First, we model the received signal model in the presence of deceptive jamming based on the properties of the radar target and the deceptive jamming in time and frequency domains. Then, we propose the suppression approach of the deceptive jamming based on three procedures, i.e., the false target alignment, the coherent cancelling and the secondary rejection. Finally, we demonstrate the validity and capacity of the proposed method via numerical simulations.

We address the performance prediction of the ordered-statistic constant false alarm rate (OS-CFAR) detector for arbitrarily correlated and possibly nonidentically distributed target echoes in the presence of nonhomogeneous background. Accounting for the generalized Swerling-Chi fluctuating model, including the well-known Swerling models as special cases, we develop the analytic expressions of detection probability in terms of converging series in nonhomogeneous background involving clutter edges and possibly isolated point-like interference. At the analysis stage, we study the truncation error of the derived expressions and then evaluate the impacts on detection performance of the target parameters via numerical simulations.

This study deals with the performance prediction of constant false alarm rate (CFAR) detector for non-independent and possibly non-independent and non-identically distributed (non-IID) gamma fluctuating targets in the presence of homogeneous background. Accounting for this target model, including the well-known Swerling models as special cases, the authors develop the analytic expressions of detection probability of the cell averaging CFAR (CA-CFAR) and the ordered-statistic CFAR (OS-CFAR) in terms of converging series. At the analysis stage, they study the truncation error and the convergence rate of the derived expressions. Finally, they assess the impacts on detection performance of the target parameters and compare the detection performance between CA-CFAR and OS-CFAR for the non-IID gamma fluctuating targets via numerical simulations.

The α-μ distribution, a generalization of the Gamma distribution, includes the Chi-squared, Swerling-Chi, Exponential, Weibull and Gamma distributions as special cases, and is adopted to model the fluctuation of the target radar cross section (RCS) in this paper. We proof the capacity of the new target model using the real data collected by the McMaster IPIX radar in the sense of minimum of Cramer-Von Mises distance. The results highlight that the α-μ distribution can describe the real data better than conventional models.

In this paper, we adopt the α-μ distribution to approximate the statistic distribution of the sum of independent and possibly non-identically distributed lognormal variables, and obtain the shape and scale parameters using both the moment matching method and Non-linear Least Square Method. Finally, we evaluate the performance via numerical simulations, the results illustrate that the α-μ approximation fits well the sum of the lognormal variables.

This paper considers the statistical distribution of the sum of independent and possibly non-identically distributed lognormal random variables (RVs). The α–μ distribution is employed to approximate the probability of density function (PDF) of the lognormal sum. Two approaches, e.g., the moment-based and non-linear least square based (LS-based) methods, are used to obtain the parameters of α–μ distribution. Accounting for maximal-ratio combining (MRC) receiver, an analytical expression of the average bit error probability (ABEP) of the M-ary noncoherent frequency-shift keying (M-NFSK) modulation is derived. Finally, the performances of the α–μ approximation and the ABEP are evaluated via numerical experiments.

In this paper, we adopt the alpha-beta distribution to approximate the statistic distribution of the sum of independent and possibly non-identically distributed lognormal variables, and obtain the shape and scale parameters using both the moment matching method and Non-linear Least Square Method. Finally, we evaluate the performance via numerical simulations, the results illustrate that the alpha-beta approximation fits well the sum of the lognormal variables.

We deal with the performance prediction of the incoherent radar receiver (i.e. squared-law plus integrator) in the presence of independent and possibly non-identically distributed target backscattered echoes. Specifically, we first approximate the lognormal sums with a new lognormal probability density function (pdf). Based on the approximation, we develop an approximate expression of the detection probability. Finally, we assess the detection performance for different target models via several numerical simulations.

We deal with the performance prediction of the conventional cell-averaging constant false alarm rate (CA-CFAR) detector in the presence of non-independent identically distributed (non-iid) target backscattered echoes. Accounting for the generalized Swerling-Chi fluctuation target model, we develop the analytical expressions for the target detection probability for CA-CFAR detector. Finally, we assess the detection performance with the derived expression as well as Monte Carlo simulations.