Accurate and robust sea clutter modeling and parameter estimation are foundational for target detection. Traditional modeling methods rely on measured data, while clutter modeling based on radar settings and oceanographic observations is an alternative. This letter leverages high-resolution sea clutter data from the Sea-Detecting Radar Data-Sharing Program (SDRDSP) to address this challenge. Three distribution types, which are generalized Pareto distribution (GPD), K distribution, and compound-Gaussian model with inverse Gaussian (CGIG), are considered. Using random forest (RF), we identify the most discriminative factors for distribution type classification: range and azimuth resolution cell (RARC), grazing angle, wave speed, wind speed, and significant wave height (SWH). Building on this, a stacking ensemble learning framework is proposed to effectively regress the shape and scale parameters from these optimized input features. Experiments validate the effectiveness of the proposed approach in distribution type classification and parameter estimation.

The direction of arrival (DOA) estimation algorithms for time-modulated arrays (TMAs) with extremely low hardware complexity are attractive. However, due to the time modulation operation performed on each antenna of the TMA to generate the harmonic beams, the rotational invariance is invalid in the harmonic domain. As a result, the estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm cannot be directly applied to the TMA. In this letter, an ESPRIT algorithm for the TMA (TMA-ESPRIT) is proposed for the first time. First, the unidirectional phase center motion (UPCM) modulation is employed and the harmonic domain signal subspace is constructed accordingly. Then, a derived harmonic coefficient matrix (HCM) is performed on the harmonic domain signal subspace to reconstruct the rotational invariance. Numerical simulations demonstrate the superior performance of the TMA-ESPRIT algorithm in terms of the computational complexity and the multi-target resolution.

Adaptive broadband digital beamforming (ABDBF) is an essential topic in the realm of array antenna for radar systems, because the array antenna with ABDBF could obtain wide swath and high azimuth resolution. Performance degradation at low snapshots is a serious problem for ABDBF. In this letter, the broadband beamforming weight generation network (BWGN) is proposed to quickly generate weights for ABDBF under low signal snapshot scenarios. The BWGN leverages the complex convolutional neural network to represent the mapping between input signals and output weights, which avoids the operation of covariance matrix and its inversion, thus speeding up the generation of weights. Compared with the existing neural network-based broadband beamforming method, wideband beamforming prediction network (WBPNet), training BWGN saves 71.45% of the time. Furthermore, progressive learning is introduced to the training process of BWGN, namely PL-BWGN, which further reduces training time of BWGN to 0.7529 h (h: hours). Simulation results demonstrate performance superiority of the proposed method compared with existing beamformers under low snapshot scenarios.

This letter develops a novel transmit beamforming (BF) design for canceling self-interference (SI) in analog in-band full-duplex phased arrays. Our design maximizes transmit BF gain in a desired direction while simultaneously reducing SI power to below a specified threshold on per-antenna basis to avoid saturating receive-chain components, such as LNAs. Core to our approach is that it accounts for real-world phase shifters used in analog phased array systems, whose limited resolution imposes non-convex constraints on BF design. We overcome this by transforming these non-convex constraints into convex polygon constraints, which we then solve through semidefinite relaxation and a rank refinement procedure. Numerical results show that our proposed BF scheme reliably cancels SI to the target power threshold at each receive antenna while sacrificing little in transmit BF gain, even with modest phase shifter resolution.

Sea-surface small target detection is challenging for the reason that the small target with low signal-to-clutter ratio (SCR) would be submerged by strong sea spikes in the complex marine environment. Against this background, a two-stage detection method using multi-feature-fusion-based binary classifier and SCR enhancement is proposed in this paper. In stage 1, the binary classifier extracts multi features in the temporal, frequency and time-frequency domains using convolutional neural networks (CNNs) and performs weighted feature fusion by minimizing cross entropy loss. Afterwards, in stage 2, SCR enhancement is divided into two steps, which are clutter suppression by modified orthogonal projection (MOP) and inter-frame incoherent accumulation by Kalman filter, respectively. Specifically, according to the range cells classified as clutter in stage 1, MOP is performed in the range-Doppler (RD) domain to improve SCR by using the projection operator composed by main clutter components via first-order difference method. Then, Kalman filter combines the predict state after RD compensation and observation state with the gain coefficient to realize target energy accumulation and SCR stabilization. Finally, the final decision is made by the constant false alarm rate (CFAR) detector. The simulation results indicate that the proposed binary classifier attains a significant binary classification performance improvement, laying foundations for effective sea clutter suppression and SCR enhancement so as to improve the final detection performance of target location and Doppler frequency.

Abstract The sum‐difference coarray (SDCA) is the union of the sum coarray (SCA) and difference coarray (DCA), which has higher degrees‐of‐freedom (DOF) than that of the DCA, resulting in a better direction‐of‐arrival (DOA) estimation performance. However, existing passive sparse arrays require spatial and temporal information to construct SDCA. In this study, a mirrored coprime array (MCA) is designed to implement SDCA using only spatial information. First, the SCA and DCA are recovered from the vectorised covariance matrix via the transform matrix. A Tikhonov regularisation method is proposed to reduce the rank‐deficiency effect of the transform matrix. The SCA has the potential to fill the holes in the DCA by adjusting the mirror position since the mirror determines the virtual sensor locations of the SCA. Then, the closed‐form expressions of the mirror position and virtual array aperture are derived for the hole‐free SDCA. The consecutive lags of the optimised SDCA are much larger than those of the DCA, significantly increasing the DOF. Numerical simulations verify that the MCA outperforms the non‐mirrored one with respect to the DOA estimation accuracy and resolution.

A power amplifier’s linearity determines the emission signal’s quality and the efficiency of the system. Nonlinear distortion can result in system bit error, out-of-band radiation, and interference with other channels, which severely influence communication system’s quality and reliability. Starting from the third-order intermodulation point of the milimeter wave (mm-Wave) power amplifiers, the circuit’s nonlinearity is compensated for. The analysis, design, and implementation of linear class AB mm-Wave power amplifiers based on GlobalFoundries 45 nm CMOS silicon-on-insulator (SOI) technology are presented. Three single-ended and differential stacked power amplifiers have been implemented based on cascode cells and triple cascode cells operating in U-band frequencies. According to nonlinear analysis and on-wafer measurements, designs based on triple cascode cells outperform those based on cascode cells. Using single-ended measurements, the differential power amplifier achieves a measured peak power-added efficiency (PAE) of 47.2% and a saturated output power ( P sat ) of 25.2 dBm at 44 GHz. The amplifier achieves a P sat higher than 23 dBm and a maximum PAE higher than 25% in the measured bandwidth from 44 GHz to 50 GHz.

Almost all current beamforming-based designs of self-interference cancellation (SIC) for digital phased array systems neglect the practical issue of the limited dynamic range of analog-to-digital converters (ADCs) and, thus, lack restriction for self-interference (SI) that is incident at each receiving antenna. Other than beamforming, these methods usually require additional SIC techniques to provide a part of isolation, which brings higher system complexity. In this letter, a SIC method is proposed to overcome these issues. By developing a transmit SIC beamformer design that minimizes the power of SI on a per-antenna basis, we can obtain more isolation before the ADCs in each receiving channel and better prevent them all from being saturated. Then, the residual SI can be further suppressed by adaptive receive beamformers to achieve high total isolation performance. The proposed method reduces the need for other SIC techniques than beamforming technology in phased array systems and, thus, facilitates lower system complexity. Numerical results demonstrate the effectiveness of the proposed method.

Unknown array non-ideal characteristics, including mutual coupling between array elements, channel gain/phase errors, and sensor position errors, will degrade the array performance of direction finding and beamforming. In this letter, we propose an improved maximum likelihood (ML) calibration algorithm to estimate all such non-ideal characteristics with lower computational complexity and faster convergence rate. First a modified formulation of the ML function is developed to avoid matrix inversion in each iteration. Then, a damped Newton formula with non-approximate Hession matrix is derived, which greatly improves the convergence rate. The numerical results demonstrate the effectiveness and efficiency of the proposed algorithm.

The scalar sensor array (SSA) is generally assumed insensitive to the polarization of impinging signals, and only diversely polarized arrays, such as the vector (crossed-dipole or tripole) sensor array (VSA), can be used for polarization estimation. However, as shown in this paper, with the mutual coupling effect, the SSA can become partially sensitive to polarization of the impinging signals and therefore can be used for polarization parameter estimation. The polarization sensitivity model of an SSA is first established and then as an example, a dimension-reduction method based on multiple signal classification (MUSIC) is employed to jointly estimate the direction-of-arrival and polarization parameters. Computer simulations based on a planar array of circularly polarized microstrip antennas are provided to demonstrate the performance of the proposed method.

We propose a hop-by-hop relay selection strategy for multi-hop decode-and-forward (DF) underlay cognitive relay networks (CRNs). In this strategy, taking both maximum transmit power and maximum interference constraints into account, one relay in each decoding set is selected to transmit to the next hop. Relay selection at each stage only depends on the channel state information (CSI) of the following hop. We analyze the performance of the proposed strategy in terms of end-to-end outage probability. Numerical results are presented from analysis which closely match those obtained from simulation. The results are compared to those from other hop-by-hop strategies, showing improved outage probability. Moreover, the numerical results show that the outage probability of the proposed method is very close to that of exhaustive strategy which provides a lower bound for outage probability of all relay selection methods.

In this study, a novel multi‐beam pattern synthesis algorithm is proposed. The algorithm is divided into three steps. First, the pattern synthesis is performed for each beam without element excitation amplitude constraints, and form a element excitations amplitude matrix (EEAM). Second, the kernel principal component analysis (KPCA) technique is used to acquire a group of common element excitation amplitudes (CEEA). Finally, the semi‐definite relaxation technique is employed to obtain the element excitation phase of each beam. The KPCA is a kind of principal component extraction method. Compared with the traditional method, the kernel function and the kernel parameter selection criterion used in the proposed study are designed to ensure that the extracted principal component can hold more than 80% of the information of the EEAM, which means that the acquired CEEA can bestly characterise the EEAM, hence resulting in a better synthesised pattern. In addition, the use of KPCA is a quasi‐analytical process, which also speeds up the overall algorithm. Compared to the iterative multi‐beam pattern synthesis algorithm, nearly half of the synthesis time is reduced. Through several sets of synthesised examples, and compared with some classical algorithms, this algorithm proves its superiority in the comprehensive effect and calculation time.

Zadoff-Chu (ZC) sequences have been used as random access sequences in modern wireless communication systems, replacing the conventional pseudo-random-noise (PN) sequences due to their superior autocorrelation properties. An analytical framework quantifying the ZC sequence's performance and its fundamental limitation as a random access sequencein the presence of frequency offset between the transmitter and the receiver is introduced. We show that a ZC sequence's perfect autocorrelation properties can be severely impaired by the frequency offset thereby limiting the overall performance of the random access signals formed from these sequences. First, we derive the autocorrelation function of these random access sequences as a function of the frequency offset. Next, we introduce the concept of critical frequency offsets and the spectrum associated with a ZC sequence set to characterize the frequency offset properties of the random access signals. Finally, we demonstrate that the frequency offset immunity of a ZC sequence set can be controlled by shaping the spectrum of the ZC sequence set.

This paper proposes a unique discovery signal as an enabler of peer-to-peer (P2P) communication which overlays a cellular network and shares its resources. Applying P2P communication to cellular network has two key issues: 1. Conventional ad hoc P2P connections may be unstable since stringent resource and interference coordination is usually difficult to achieve for ad hoc P2P communications; 2. The large overhead required by P2P communication may offset its gain. We solve these two issues by using a special discovery signal to aid cellular network-supervised resource sharing and interference management between cellular and P2P connections. The discovery signal, which facilitates efficient neighbor discovery in a cellular system, consists of un-modulated tones transmitted on a sequence of OFDM symbols. This discovery signal not only possesses the properties of high power efficiency, high interference tolerance, and freedom from near-far effects, but also has minimal overhead. A practical discovery-signal-based P2P in an OFDMA cellular system is also proposed. Numerical results are presented which show the potential of improving local service and edge device performance in a cellular network.