Active reconfigurable intelligent surfaces (RISs) can mitigate severe multiplicative fading through signal amplification, but fully-connected architectures consume substantial hardware power because each reflecting element requires a dedicated power amplifier (PA). This paper investigates a sub-connected active RIS (SC-ARIS)-assisted integrated sensing and communication (ISAC) system, where multiple reflecting elements share one PA, reducing active-hardware power consumption at the cost of lower amplification-control flexibility. We formulate a highly non-convex energy efficiency (EE) maximization problem by jointly optimizing the base station transmit beamforming, radar receive filter, and SC-ARIS reflection coefficients, subject to a minimum radar signal-to-noise ratio (SNR) requirement, power budgets, and hardware constraints. To solve it, we develop an alternating optimization framework combining Dinkelbach’s method and fractional programming. By exploiting the phase freedom of the radar receive filter, the non-convex radar SNR constraint is transformed into a linear constraint on the transmit beamformer for a fixed receive filter, while projection-based recovery yields hardware-feasible SC-ARIS coefficients. Simulations show that the proposed design achieves higher EE than fully-connected and random-phase baselines, although the fully-connected benchmark attains a higher communication sum-rate. These results demonstrate a favorable trade-off among EE, ISAC performance, and active-hardware complexity.

In this paper, a meter-wave radar height estimation algorithm founded on sparse Bayesian learning under refined reflection model is proposed, addressing the challenge of height estimation in multipath environments. Since the reflective surface of the multipath signal is considered as a reflective point under the classic model, but it is actually an area, the multipath signals received by different array elements are modulated differently by reflective surface of different media and heights. Therefore, model mismatch can occur under complex terrain, which affects the estimation results. On this basis, we derive the refined reflection model, which fully considers the modulation effect of reflective surface medium and height on the multipath signal. Taking advantage of the sparsity of the airspace signal, we develop an iterative optimization algorithm, with its theoretical foundation firmly established in the off-grid refined model from the Bayesian learning method, which enhances the accuracy of estimation and mitigates the effects of model mismatch and off-grid error under the complex terrain to a certain extent. Under complex terrain, both measured data validation and simulation analysis indicate that the proposed method in this paper has good performance in low-elevation target height estimation.

In this letter, we propose a two-step approach to jointly design the aperture share and transmit beamforming for dual-functional radar-communication (DFRC) systems. In particular, by sharing the array aperture, the communication and radar beamforming are respectively designed with different antenna power constraints. A two-step method is proposed, in which the aperture share vector and communication beamforming are first designed. For the formulated nonconvex problem, a successive convex approximation and semidefinite relaxation (SCA-SDR) based method is proposed to iteratively solve the problem. Then, in the second step, two different approaches are presented to design the radar beamforming. For the formulated problems, the solutions are obtained in closed form or the alternating direction method of multipliers (ADMM) with easy-to-solve subproblems.

The massive multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) with large-scale user access enables both high-spectral-efficiency reliable communication and high-resolution omnidirectional sensing while significantly improving the reuse efficiency of hardware and spectrum resources, especially for the ultra-massive machine-type communications. However, this massive MIMO-ISAC system benefiting from the vast spatial degrees of freedom raises two critical challenges in acquiring both accurate channel state information and target parameter information. On one hand, in massive MIMO-ISAC systems with large-scale antenna arrays, the spatial distribution of propagation paths induces a frequency-dependent phase progression across the array aperture. The segment-based training pattern is presented to solve channel and target parameter estimation in the scenario with beam squint effect. On the other hand, a shared training pattern with the same time-frequency resource is adopted to formulate the unified tensor-based framework, while resulting in the coupling problem of equivalent path parameters. The tucker decomposition based on a tensor train (TT) is proposed to eliminate the coupling of communication and sensing parameters using its remarkable specific structure. The simulation results demonstrate that the proposed method provides superior performance compared to the-art-of-state methods with respect to channel estimation and radar sensing.

Due to the unique anti-capture capability and absence of range blind zones, linear frequency modulated continuous wave (LFMCW) radar has been widely used in various fields. The range estimation for LFMCW radar typically employs the discrete Fourier transform (DFT) technique. However, when a weak target is adjacent to the strong target, the DFT exhibits poor Doppler filtering performance. In this paper, we propose a weak target detection method for LFMCW radar with temporal finite impulse response (FIR) filter. To solve the missed detection caused by the strong clutter, we design a time-domain FIR filter tailored to the clutter environment, which performs the time-domain filtering on the beat signal to extract the target’s range. Similarly, when the weak target is masked by an adjacent strong target, we extract the characteristics of the strong target to reconstruct it. Then, the FIR filter is developed to suppress the strong target to accurately locate the weak target. Simulation results demonstrate that the proposed algorithms improve the detection probability of weak targets while ensuring the accuracy and robustness of target estimation.

The trajectory probability hypothesis density and trajectory cardinality probability hypothesis density (TPHD and TCPHD) filters are capable of producing tracks without adding labels. Multi-trajectory densities are recursively propagated using random finites sets of trajectories. In this paper, the proposed filters extend the TPHD and TCPHD to mitigate the impact of minimum detectable velocity (MDV) for the Doppler radar-based detection. The continuous missed detections occur due to the MDV in the Doppler radar. To address this issue, we incorporate the MDV into the state-dependent detection probability model to keep track continuity. In the framework of trajectory random finite set (RFS), the observations, including position and Doppler, are considered in the filter recursion to improve the estimation precision. Besides, we perform the measurement-driven adaptive track initiation instead of fixed track initiation in the proposed filters. In pursuit of computational efficiency for both filters, the analytical solutions of sequential update, including the version of L-scan approximation, are derived. Experimental simulations demonstrate the proposed algorithms achieve reliable trajectory estimates at the expense of a slight rise in false tracks.'

Subarray partitioning in phased multiple-input multiple-output (PMIMO) radar shapes the sidelobe behavior of the overall transmit-receive beampattern. Under a uniform-overlap constraint, the optimal partition is defined as the one that minimizes the peak sidelobe level (PSLL) of the overall transmit-receive beampattern. Rather than using numerical or iterative optimization, an analytical alignment-based rule is developed: the transmit and receive beampatterns are angularly aligned, and sidelobes are reduced via pattern multiplication. Unified closed-form solutions are derived for both an ideal case (identical complex-valued coherent gains after phase normalization) and a practical case (with inter-subarray phase offsets), applicable to arrays with either odd or even numbers of elements. Moreover, an "approximate parameter swap-invariance" is proved to hold only in the ideal case and to break down under practical inter-subarray phase offsets. The impact of the number of receiving elements on sidelobe characteristics is then quantified. Simulations show that, compared with transmit-only designs, accounting for the number of receiving elements improves PSLL by approximately 1.87 similar to 3.3

In this paper, we consider the joint DOD and DOA estimation for transmit energy focusing-based (TEF) bistatic MIMO radar. We devise the optimization problem by minimizing the matching error between the designed transmit beampattern and the desired beampattern and using a re-weighted group sparsity norm to control the row sparsity of the transmit beamspace matrix. To solve the resultant nonconvex problem, the alternating direction method of multipliers (ADMM) framework is used to transform this nonconvex problem into multiple convex subproblems by introducing an auxiliary variables. To solve the non-differentiable of the re-weighted group sparse norm of the transmit beamspace matrix, we use the idea of proximal gradient (PG) to derive an iteration method to obtain the optimal solution of this subproblem. To exploit the structure inherent in multi-dimensional measurement data, we establish the tensor-based HOSVD model to jointly estimate DOD and DOA. The backward-forward averaging and spatial smoothing schemes are extended to tensor model mapping the centro-Hermitian matrices to real-valued ones with the same size. The unitary HOSVD algorithm in TEF-based bistatic MIMO radar is proposed to jointly estimate DOD and DOA of targets. Moreover, the proposed algorithm is suitable for correlated and closely-spaced targets. The simulation results validate the effectiveness of the proposed algorithm with respect to joint DOD and DOA estimation.

Tracing the trajectories of cells alongside their lineage or ancestral details in real-time experiments is crucial for unraveling the intricacies of cellular behavior and proliferative processes. In this paper, we consider the challenging problem of tracking multiple targets and their lineages by building a flexible modeling framework based on outer probability measures (OPMs) theory. The possibility generalized labeled multi-Bernoulli (GLMB) filter was previously developed to address the multi-target tracking problem under epistemic uncertainty, focusing solely on survival and birth processes. However, it falls short in capturing spawned trackers and their lineages due to the absence of a spawning model. To address this problem, we introduce a possibility GLMB filter that formally integrates a spawning model for tracking targets and their lineages in scenarios of uncertainty or incomplete knowledge. More importantly, the labeled GLMB uncertainty finite set (UFS) approximation is applied in the prediction for establishing multi-target tracking Bayesian recursion. Simulation results and real-measured datas demonstrate that proposed possibility GLMB filter with target spawning outperforms the state-of-the-art methods including probability GLMB version and cardinalized probability hypothesis density (CPHD) version, particularly in realistic applications.

This paper considers the challenging problem of hybrid analog-digital beamforming design in mmWave MIMO integrated sensing and communication (ISAC) system to balance hardware complexity and system performance. In this paper, the hybrid analog-digital beamforming design problem is formulated into a nonconvex optimization, with the objective of minimizing a weighted summation of overall spectral efficiency and spatial spectrum matching error. To handle the resulting optimization problem, a novel Riemannian alternating minimization algorithm is proposed to computationally-effectively solve the high nonconvex constraints and coupled variables. Specifically, our approach begins by defining a complex ellipsoid manifold, accompanied by tailored projection and retraction operations. Leveraging this manifold, a Riemannian optimization algorithm is employed to optimize the digital beamformer, offering computational advantages over traditional semidefinite relaxation methods. Subsequently, the Alternating Direction Method of Multipliers (ADMM) is adopted to update analog beamformer via introducing an auxiliary variable and reformulating the problem. Numerical experiments validate the efficacy of our proposed methodology, demonstrating its potential in enhancing the sensing performance and spectral efficiency of mmWave ISAC systems.

This paper considers the challenging problem of heterogenous multi-sensor fusion using information geometry theory. A novel distributed heterogenous fusion method with labeled random finite set (RFS) multi-target densities is proposed to address multi-sensor multi-target tracking problem. The Fisher information distance (FID) is used to characterize the information acquisition and sensing capability of distributed sensor nodes by analyzing measurement model of sensor nodes via geodesic computation on statistical manifold. A scalar fusion weight is assigned to each local multi-target density in order to characterize its relative information confidence under the different architecture of sensor nodes. The fusion weights are adjusted according to the contribution of each sensor node to global multi-target density. Thus, the proposed distributed heterogenous fusion serves as an adaptive algorithm for multi-sensor multi-target tracking within the monitored area. Aiming to achieve multi-target tracking under the limited sensing capabilities of sensor nodes, we firstly formulate the general framework of distributed heterogenous fusion strategies based on sensor information accumulation. Secondly, we present both numerical and approximate solutions to computing the geodesic on the manifold for FID calculations, furtherly for determining locally tailored fusion weights for different sensor nodes. Finally, we present several simulation results to validate the efficacy of our proposed distributed heterogenous fusion algorithm for multi-target tracking. These simulations involve three typical types of sensor nodes where each sensor agent performs a multi-scan generalized labeled multi-Bernoulli (GLMB) smoothing algorithm, demonstrating superior performance in multi-target state estimation.

With the increasing research depth in integrated sensing and communication (ISAC) systems, the number of users and power consumption in future network systems are expected to continue growing. To address this challenge, this paper proposes a novel energy-efficient beamforming design for ISAC systems based on non-orthogonal multiple access (NOMA). The proposed scheme aims to maximize the energy efficiency (EE) of NOMA-ISAC systems while satisfying the minimum communication rate and successive interference cancellation (SIC) requirements for NOMA users, as well as the beampattern gain and cross-correlation requirements. The original problem poses significant challenges due to its complex fractional objective function and non-convex NOMA rate expressions. We first transform the fractional objective function into a more tractable form, then develop an efficient iterative algorithm by combining successive convex approximation (SCA) and semidefinite relaxation (SDR) techniques to solve the reformulated optimization problem, and rigorously demonstrate the tightness of the introduced relaxation. Simulation results demonstrate that under user overload scenarios, compared with existing schemes, the proposed NOMA-based energy-efficient beamforming scheme can significantly improve the EE of multi-user ISAC systems while ensuring sensing robustness.

The multiple-input multiple-output (MIMO) radar has attracted much attention due to its superior sensing functions. However, it suffers from huge computational complexity for its sensing application. To tackle this challenge, this paper proposes a novel transmit waveform design method grounded in an atomic 80-norm minimization. This technique capitalizes on signal sparsity and operates directly on continuous samples, thereby fully addressing the grid mismatch issue prevalent in prior compressed sensing approaches. Combining with multi-rank transmit beamformer (MRTBF), the transmit waveform design problem is formulated into convex relaxation of an atomic 80-norm minimization optimization problem. The MRTBF can be acquired by computational-efficiently solving the semi-definite programming (SDP) optimization problem via alternating direction method of multipliers (ADMM) algorithm, with enhanced computational efficacy. Finally, the transmit waveform can be designed resorting to resulting MRTBF and predefined orthogonal waveforms. Theoretical analysis and simulation results demonstrate that the proposed waveform design algorithm based on an atomic 80-norm offers comparable matching performance while significantly reducing computational complexity. Compared to state-of-the-art waveform design methods, the proposed algorithm achieves a better tradeoff between matching accuracy and computational efficiency. Additionally, the transmit waveform generated by the proposed method exhibits a lower Peak-to-Average Power Ratio (PAPR), substantially satisfying the uniform element power constraint.

This paper discusses the problem of low-elevation target height estimation for multiple-input multiple-output (MIMO) radar in multipath environments. The beamspace compresses the data and is ideal for reducing the computational burden of elevation estimation. To obtain the height parameter of the target accurately, we propose a height estimation method based on a beamspace joint alternating iterative (BJAI) algorithm in MIMO radar. This method mainly converts the reduced-dimensional MIMO radar element space data into beamspace data and whitens them to improve the reliability. Then, a simplified model is used to obtain the initial value of the elevation, and we combine the reflection coefficient and the target elevation angle for alternate estimation. Finally, we calculate the target height using the obtained elevation information. Simulation results verify that the proposed algorithm has high estimation accuracy and strong robustness.

This paper introduces a novel waveform agile MIMO radar waveform design method based on random arrangement of pulse slices (RAPS), enhancing the conventional Frequency diversity LFM (FD-LFM) transmit beampattern synthesis. We conduct a pioneering analysis of the auto-correlation function characteristics of the existing FD-LFM, revealing theoretical insights into its low range resolution and associated factors. The proposed method significantly enhances range resolution and effectively minimizes range sidelobes by combining RAPS and waveform agility between pulses. The efficacy of this approach is corroborated through comprehensive simulation experiments.

Target low-elevation estimation is of great significance in the field of radar detection. However, as the current electromagnetic environment grows increasingly complex, interference significantly degrade the performance of radar angle estimation. To address this issue, this paper proposes a low-elevation estimation method for meter-wave array radar in interference environments: firstly, the multipath signals of both target and interference are fully considered and a refined model is constructed; secondly, based on the minimum variance distortionless response, interference samples are combined with the target’s composite steering vector to derive a weight vector that integrates both interference suppression and elevation estimation functions; finally, the target elevation is estimated using the idea of maximum likelihood estimation. Simulation results demonstrate the effectiveness of the proposed method.

The energy efficiency (EE) and security of integrated sensing and communication (ISAC) systems are of significant importance in practical applications, but relatively few current design schemes have considered both simultaneously. In this paper, we propose a secure and energy-efficient beamforming design for sensing-centric ISAC systems. Our objective is to maximize the EE of the sensing-centric system subject to constraints on the maximum permissible Cramér-Rao Bound (CRB) for extended target estimation, the minimum required signal-to-interference-plus-noise ratio (SINR) for legitimate users (LUs), the minimum outage probability for a potential passive eavesdropper (PE), and the maximum transmit power. To solve this non-convex fractional optimization problem, successive convex approximation (SCA) and semidefinite relaxation (SDR) techniques are employed after equivalent transformations. Simulation results demonstrate that, compared to conventional energy-efficient beamforming algorithms, the proposed beamforming algorithm achieves a higher secrecy rate, significantly enhances physical layer security (PLS), and exhibits applicability in more complex environments.

Recently, iterative adaptive approach (IAA) has received enthusiastic attention as a super-resolution direction of arrival (DOA) estimation algorithm based on the weighted least squares (WLS). However, this algorithm is computationally intensive and can only estimate the targets located on the predefined discrete angular grids. As a result, this paper proposes an alternating descent weighted least squares (ADWLS) algorithm to estimate the angles of off-grid targets. Under the assumption of spatial sparsity, this algorithm utilizes Taylor expansion to approximate the real steering vectors of the offgrid targets. Subsequently, using the weighted least squares as the cost function, the sparse offsets of the targets and the reconstructed dictionary matrix are alternately updated at each iteration. Afterwards, the real angles of the on-grid or off-grid targets are accurately estimated by several iterations until convergence. Compared with IAA, the novel proposed algorithm ADWLS has less computational complexity and can accurately estimate the DOAs that mismatch the predefined discrete angular grids. The simulation results show that the estimation performance of the proposed algorithm is better under certain circumstances, especially compelling in the cases of single snapshot and correlated sources.

Compared with the traditional array, the sparse array can offer a larger array aperture with the same number of physical sensors. In this paper, an efficient method to estimate direction of arrivals (DOAs) of two targets based on robust generalized Chinese remainder theorem (RGCRT) for monostatic MIMO radar in sparse array is proposed. Firstly, we employ the all-phase time-shifting phase difference correcting method (AP-TSPDC) to determine the wrapped phases of two targets in the noise environments. And then the improved RGCRT is utilized for the phase unwrapping, enabling accurate DOA estimation for both targets. Besides, the proposed algorithm displays high applicability due to no strict constraint on the array aperture. The future research will extend the proposed algorithm to bistatic radar models. The proposed algorithm is able to achieve high DOA estimation precision with low computational complexity. Computer simulation results confirm the effectiveness and stability of the proposed algorithm.