Junli Liang

Drone swarm array configuration varies with the unmanned aerial vehicle (UAV) positions, and thus provides extra antenna position degree-of-freedom (DOF) comparing to fixed array configuration. In this paper, we utilize the DOFs in both drone swarm waveform and antenna positions to jointly design waveform and array for integrated sensing and interfering (ISI), where a generalized likelihood ratio test (GLRT) is devised to decide if there exists a target at a known location or not; while a range profile-based electromagnetic disguise is carried out for the interference purpose. Then, the matrix inversion lemma is applied to eliminate the challenging fraction term resulted from the GLRT signal-absence and signal-presence hypotheses, and derive simplified detection metric expression. Moreover, to tackle the extremely complicated inversion operator in the projection matrix of the clutter steering matrix, we derive a recursive scheme to reformulate a series of generalized Rayleigh entropy problems. While utilizing the rank-1 and orthogonal properties, the entropy problem is simplified as a constant modulus optimization problem. Numerical results demonstrate the excellent performance of the proposed solutions.

With the recent accelerated advancements in multistatic sensing and localization technologies, elliptic positioning (EP), aimed at locating a signal-reflecting target based on bistatic ranges (BRs), has captured the interest of many researchers in the area. An expanded scenario of EP, termed passive EP (PEP), emerges when the transmitter position is no longer assumed known, but the direct transmitter-to-receiver ranges are available alongside BRs. This contribution studies robust PEP (RPEP) under anomalous range measurements caused by practical operational conditions. To handle outliers, we adopt the concept of sparse representation by exploiting the rare and infrequent characteristics that the deviant range readings usually display and leveraging the $\ell \_{0}$-norm to promote sparsity. We then develop a customized projected gradient descent algorithm to tackle the $\ell \_{0}$-constrained $\ell \_{2}$-minimization problem arising from such a sparsity-driven RPEP formulation. As a theoretical result, we rigorously establish the convergence of the iterates produced to a critical point. Additionally, a two-layer exploratory mechanism is included to more effectively navigate the nonconvex optimization landscape. In the simulations, our proposed technique demonstrates its performance reaching the Cramér–Rao lower bound benchmark under Gaussian noise environments, and exhibits strong potential to outperform existing PEP and RPEP methods in terms of robustness when outliers are introduced.

In this paper, we focus on the problem of joint waveform and array design for direction-of-arrival (DOA) estimation using self-organized unmanned aerial vehicle (UAV) swarm. Exploiting the Cramér-Rao lower bound (CRLB) as the performance metric, we formulate two waveform and array design models. The first applies the minimax criterion to minimize the maximal flight distance for battery saving, while the second maximizes the minimal pair-wise separation (MPS) between the UAV flight traces for collision avoidance. To handle the nonconvex and nonlinear fractional CRLB constraints, we derive two special parametric quadratic matrices with one positive eigenvalue and three non-positive eigenvalues, where each of them is represented as the difference of two positive semidefinite matrices, to convert them into equivalent convex forms. The parametric maximum block improvement method is then developed to tackle the high-order polynomial optimization subproblem with inhomogeneous waveform and antenna position variables encountered in the two models. Especially in the MPS formulation, which cannot be explicitly expressed, we analyze the corresponding Karush-Kuhn-Tucker conditions of the MPS Lagrangian from nine cases to transform it as a solvable and closed-form constraint set. Numerical results demonstrate the excellent performance of our solutions.

Utilizing the dual degrees-of-freedom in the positions of array elements and waveform of drone swarm, range profile (RP)-based electromagnetic disguise is investigated in this paper. Based on the captured transmit waveform (TW) of the hostile target and the desired range profile (DRP) of a user-defined high-valued target (HVT), the swarm transmits the designed waveform toward the hostile, causing its obtained RP (ORP) through matched filtering to be close to the DRP. In doing so, the hostile target is deceived by the disguised HVT via the drones electromagnetically. Maximizing the cosine similarity between the ORP and DRP is formulated as a fractional optimization problem. To address it, a successive factor is first introduced to transform the complex objective function into a polynomial form. While for solving the intricate issue of sum-of-exponent caused by drone coordinates appearing in the steering vector, we introduce the adaptive moment estimate into the block coordinate descent framework for iterative position updates. Furthermore, algorithm robustification for inaccurate TW is conducted. Simulation results demonstrate the effectiveness of the developed algorithms.

In radar space-time adaptive processing (STAP), the sliding window size of the filter is a crucial design parameter that significantly influences system performance. A smaller sliding window size while maintaining performance provides several benefits, particularly in terms of faster response and lower range migration probability on the receiver side. In this paper, we tackle the problem of space-time feedback beampattern synthesis (FBS), which reduces the sliding window size by reducing the required number of pulses while maintaining performance. This is achieved through the formulation of two novel models. Motivated by the domino effect, which creates a chain reaction of falling dominoes when the first one is knocked down, the first formulation introduces a novel domino-group-sparsity (DGS) scheme to achieve domino-sparse-pulses (DSP),and its adaptive version is also provided. While the second formulation minimizes the ratio of maximal sidelobe level to the minimal mainlobe level in the FBS to attain performance comparable to the former but with even fewer pulses. Especially, for the latter, we solve the resultant nested-fractional program by decoupling the fractions layer by layer. Simulation results are provided to demonstrate the effectiveness of the proposed methods.