The growing use of AI applications among freelance workers is reshaping trust and relationships with clients. This paper investigates how both workers and clients perceive AI use and disclosure in the freelance economy through a three-stage study: interviews with workers and two survey studies with workers and clients. Findings first reveal a key expectation gap around disclosure: Workers often adopt passive disclosure practices, revealing AI use only when asked, as they assume clients can already detect it. Clients, however, are far less confident in recognizing AI-assisted work and prefer proactive disclosure. A second finding highlights the role of unclear or absent client AI policies, which leave workers consistently misinterpreting clients’ expectations for AI use and disclosure. Together, these gaps point to the need for clearer guidelines and practices for AI disclosure. Insights extend beyond freelancing, offering implications for trust, accountability, and policy design in other AI-mediated work domains.

Direction of Arrival (DOA) estimation has been widely studied and applied in real-life systems. However, little attention has been given to the risk posed by attackers trying to hide real targets or create fake ones. This poses a serious security and safety concern, as such attacks could lead to system failures or accidents. In this paper, we proposeDOA spoofing, the first physical layer attack that exploits the semantic gap between DOA estimation outputs and the existing physical location of targets to spoof the perceived sources’ location. We consider two scenarios:i)the attacker wants to create ghost targets at desired locations while being undetected, andii)the attacker wants to change the DOA estimated at the receiver for a target victim. To evaluate the effectiveness of DOA spoofing, we test it against several widely used and well-established DOA estimation algorithms. Via extensive numerical simulations, we demonstrate that in both scenarios the attacker can spoof DOA estimates at the receiver while not being detected among the targets. To the best of our knowledge, this paper presents the first study to examine the impact of DOA attacks on the behavior of radar perception models. Therefore, although not providing a real-life testbed due to the challenges of implementing a non-commercial multi-antenna system, our contribution exposes novel threats thanks to the use of algorithms and models commonly used in real-life DOA estimation.

Blocking jamming and deceptive jamming pose significant threats to synthetic aperture radar (SAR) systems in practical combat scenarios, as they can heavily obscure the scene and confuse the detection of practical targets. To effectively suppress this hybrid jamming, this article proposes a novel hybrid jamming suppression method for multichannel SAR based on group iterative separation. In this method, multichannel SAR signals with hybrid jammings are suppressed by using grouped iterative separation. To enhance the operational efficiency and determine the number of signal sources, the proposed algorithm initially preprocesses the mixed signals received by the SAR. Then, the mixed signals are divided into two groups to separate the multichannel signals by eliminating the jumping parts of the jamming and iteratively solving an optimization problem. Thus, this approach enables more efficient and accurate separated signals. Finally, the target echo signal is identified by the peak-to-average power ratio in the time domain. Simulation results and experiments using measured data demonstrate that the proposed algorithm effectively separates signals with a high degree of similarity and performs well in terms of hybrid jamming suppression.

Appropriate modeling and processing of echoes are the foundations for high-precision frequency-domain synthetic aperture radar (SAR) imaging. The complex geometry makes it challenging to accurately characterize and cope with the 2-D spatial variation of Doppler modulation in maneuvering-platform translational-variant bistatic forward-looking SAR (MTV-BFSAR), resulting in that the azimuth processing method by means of setting reference points on the Cartesian coordinate axis significantly impairs the performance in terms of robustness, accuracy, and efficiency. This article proposes a comprehensive MTV-BFSAR imaging algorithm based on universal frequency-domain azimuth signal modeling (UFDASM). The presented methodology utilizes the bistatic bisector to form the azimuth reference line (ARL) and develops two expeditious ARL and range isoline (RIL) coordinates’ solving techniques, which substantially augments the reliability and efficiency of the space-variant Doppler modulation coefficient (DMC) representation, reduces the order, and improves the robustness of the entire algorithm. Subsequently, thanks to UFDASM, a modified nonlinear chirp scaling (NLCS) method with orthogonal impulse response function (IRF) is derived to eliminate the spatial variation of the quadratic DMC in the range-Doppler domain while omitting that of the cubic one. Furthermore, one may find that the equalization of the second-order spatial variation introduces a cubic phase error (CPE) term. However, boundary analyses manifest that this error is small enough not to affect the imaging performance in MTV-BFSAR. Finally, the accuracy, robustness, and efficiency of the approach are validated through numerical simulation and raw data processing.

In this work, we propose a joint collaboration-compression framework for the random signal detection problem in a resource constrained wireless sensor network (WSN). Specifically, we propose a framework where the local sensors first collaborate (via a linear collaboration matrix) with each other. Then a subset of sensors linearly compress their aggregated information before communicating with the fusion center (FC). We propose a novel metric called generalized deflection coefficient (GDC) for evaluating the detection performance which is shown to be tightly upper bounded by the Kullback-Leibler divergence for Gaussian observations. We jointly design the linear collaboration and compression strategies under power constraints via alternating maximization of the proposed GDC metric. Finally, numerical results are provided to demonstrate the effectiveness of the proposed framework.

In this study, a novel method is proposed to deal with the problem of altitude measurement of a low‐elevation target for very high frequency radars in complex terrains. The problem typically concerns about the direction‐of‐arrival (DOA) estimation method for closely spaced and correlated signals arose by multipath propagation, in which the multipath signal would be modulated by the rough and irregular reflecting surface, result in amplitude and phase perturbation and energy fluctuations of the receiving signals. A perturbation multipath propagation model is derived where the influence of irregular reflecting in complex terrain is taken as a random perturbation of the multipath signal. The spatial sparsity of the receiving signal is then exploited, and a weighted sparse Bayesian learning method is proposed to estimate both the random perturbation coefficients and DOA of the incident signal with high precision. Both the computer simulations and real data analysis indicate the efficiency and superior performance of the proposed method in dealing with altitude measurement in complex terrain.

We propose anew antenna array design approach for a multiple-input and multiple-output (MIMO) radar, which has closed-form expressions for the sensor locations and the number of achievable degrees of freedom (DOFs). This new approach utilizes the nested array as transmitting and receiving arrays. We employ the difference coarray of the sum coarray (DCSC) of the MIMO radar to obtain more DOFs for direction-of-arrival (DOA) estimation. Via properly designing the interelement spacings of the transmitting and receiving arrays, we can obtain a hole-free DCSC. The characteristics of array geometries are analyzed and the optimal numbers of sensors in transmitting/receiving antenna array are derived when given the total number of physical sensors. Simulations are conducted to demonstrate the advantages of the proposed array in terms of the number of DOFs, the number of resolvable sources, and the DOA estimation performance over the coprime MIMO array.

In a low angle target parameter estimation scenario, the backscattered signals from targets are distorted by clutter and multipath, which degrades the performance of direction-of-arrival (DOA) estimator significantly. This paper presents a novel method using time reversal (TR) technique and coherent signal-subspace method (CSM) for DOA estimation in a low angle scenario. The TR method exploits target information contained in the return echoes due to multipath and adaptively adjusts TR probing waveforms to increase the signal-to-noise ratio (SNR). Furthermore, CSM is adopted to focus the energy of the multipath signal in a predefined subspace so as to exploit the full time-bandwidth product of the target source. We analyze the performance of the proposed new DOA algorithm. Numerical simulations demonstrate its superior performance compared with the conventional DOA estimators.

This work presents a new array geometry, which is capable of providing O(M2N2) degrees of freedom (DOF) using only MN physical sensors via utilizing the second-order statistics of the received data. This new array is composed of multiple, identical minimum redundancy subarrays, whose positions follow a minimum redundancy configuration. Thus the new array is a minimum redundancy array (MRA) of MRA subarrays, and is termed as nested MRA. The sensor positions, aperture length, and the number of DOF of the new array can be predicted if these parameters of MRA subarrays are given. Numerical simulations demonstrate the superiorities of the proposed array geometry in resolving more sources than sensors and DOA estimation.

Conventional antenna array 3-D inverse synthetic aperture radar (ISAR) imaging method does imaging by first carrying out ISAR imaging for all the different antennas and then using array direction of arrival (DOA) estimation on every scatterer to get the position of that scatterer. The aperture formed by the target's motion is used to separate the scatterers, and the antenna array aperture is used for position measurement. In this letter, a 3-D imaging method based on a combination of sparse array beamforming and ISAR imaging is proposed, where 3-D images are formed by space-time match filtering. The estimation of the target's rotation parameters is also given. Simulation results have shown the effectiveness of the proposed methods.