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This paper deals with the synthesis of constant modulus waveforms that optimize radar performance while satisfying multiple spectral compatibility constraints. For each shared band, a precise control is imposed on the injected interference energy. Furthermore, the compliance with amplifiers operating in saturation is ensured at the design stage where phase-only waveforms are considered. To tackle the resulting NP-hard optimization problem, an iterative procedure based on the coordinate descent method is introduced. The overall computational burden of the algorithm is linear with respect to the code length as well as the number of iterations and less then cubic with reference to the number of spectral constraints. Hence, some case studies are reported to highlight the effectiveness of the technique.

This paper studies adaptive radar detection of range distributed targets in the presence of Gaussian interference and possible diffuse multipath returns modeled as independent zero-mean complex circular Gaussian random vectors with unknown covariance matrices. For this problem, an adaptive constrained generalized likelihood ratio (ACGLR) test is devised, where in each range cell of the primary data the covariance matrix (due to both multipath and disturbance echoes) is forced to belong to a neighborhood of the secondary data sample covariance. The size of the uncertainty set is determined adaptively employing jointly a union-intersection test and an expectation likelihood (EL)-based estimate. Besides, an adaptive detector based on the complex parameter Rao test criterion is derived. Remarkably, both the two new architectures possess the desired constant false alarm rate (CFAR) property with respect to the disturbance covariance. Finally, their detection performance is assessed and validated via numerical examples.

Two-dimensional (2-D) spectrum sensing is addressed in the context of a cognitive radar to gather real-time space-frequency electromagnetic awareness. Assuming a sensor equipped with multiple receive antennas, a discrete-time sensing signal model formally accounting for multiple snapshots of observations is introduced. Hence, a new signal processing strategy exploiting the inherent block-sparsity of the overall profile is developed to glean a reliable 2-D occupancy awareness. Specifically, the proposed approach resorts to the regularized maximum likelihood estimation paradigm including a term promoting the block-sparsity of the 2-D profile so as to automatically foster this peculiarity in the profile evaluation. Some illustrative examples (both on simulated and measured data) are provided to compare the novel strategy with a relevant counterpart available in the open literature and highlight the effectiveness of the developed approach.

This paper is focused on the design of phase sequences with good (aperiodic) autocorrelation properties in terms of Peak Sidelobe Level (PSL) and Integrated Sidelobe Level (ISL). The problem is formulated as a bi-objective Pareto optimization forcing either a continuous or a discrete phase constraint at the design stage. An iterative procedure based on the coordinate descent method is introduced to deal with the resulting optimization problems which are non-convex and NP-hard in general. Each iteration of the devised method requires the solution of a non-convex min-max problem. It is handled either through a novel bisection or an FFT-based method for the continuous and the discrete phase constraint, respectively. Additionally, a heuristic approach to initialize the procedures employing the lp-norm minimization technique is proposed. Simulation results illustrate that the proposed methodologies can outperform some counterparts providing sequences with good autocorrelation features especially in the discrete phase/binary case.

This paper considers the design of robust filters for radar pulse-Doppler processing when the interference is a wide sense stationary random process. The figure of merit which is optimized is the signal-to-interference-plus-noise ratio (SINR) at the filter output under a multitude of constraints accounting for Doppler filter sidelobes as well as uncertainties both in the received useful signal component and interference covariance matrix. The design is analytically formulated as a constrained optimization problem whose solvability is thoroughly studied. Precisely, a polynomial-time solution technique to get the optimal filter is proposed exploiting the representation of non-negative trigonometric polynomials via linear matrix inequalities, the spectral factorization theorem, and the duality theory. Last but not least, a detailed analysis of the optimum filter performance is provided showing the tradeoffs involved in the design and the gain achievable over some already known counterparts.

We read with great interest the recently published paper [1] (together with some additional technical details in [2]) dealing with an important topic for the radar signal processing Community.With regret, we noticed that the mentioned reference [1] provides some claims which do not agree with what we proved in paper [3]; contains some technical inconsistencies from the optimization theory point of view. Thus, respectfully, we feel obliged to provide the necessary clarifications and corrections. Specifically, this "comments on" paper has the following technical purposes: a) to rectify some untrue claims by the Authors of [1] about reference [3]; b) to show that the optimization problem arising in [1] (eq. (17)) is a special instance of the more general optimization problem addressed in [3] (eq. (13)); c) to prove that the solution of the waveform design problem in [1] (eq. (25)) given by eq. (36) is not generally correct. [1] P. Setlur and M. Rangaswamy, "Waveform Design for Radar STAP in Signal Dependent Interference," IEEE Transactions on Signal Processing, vol. 64, no. 1, pp. 19-34, Jan. 2016. [2] P. Setlur and M. Rangaswamy, Joint Filter and Waveform Design for Radar STAP in Signal Dependent Interference, Tech. Rep. DTIC, available at : https: // arxiv.org/abs/ 1510. 00055, US Air Force Res. Lab., Sensors Directorate, WPAFB, Dayton, OH, 2014. [3] A. Aubry, A. De Maio, A. Farina, and M. Wicks, "Knowledge-Aided (Potentially Cognitive) Transmit Signal and Receive Filter Design in Signal-Dependent Clutter," IEEE Transactions on Aerospace and Electronic Systems, vol. 49, no. 1, pp. 93-117, Jan. 2013.

In this paper, we study the joint design of Doppler robust transmit sequence and receive filter to improve the performance of an active sensing system dealing with signal-dependent interference. The signal-to-noise-plus- interference (SINR) of the filter output is considered as the performance measure of the system. The design problem is cast as a max-min optimization problem to robustify the system SINR with respect to the unknown Doppler shifts of the targets. To tackle the design problem, which belongs to a class of NP-hard problems, we devise a novel method (which we call DESIDE) to obtain optimized pairs of transmit sequence and receive filter sharing the desired robustness property. The proposed method is based on a cyclic maximization of SINR expressions with relaxed rank-one constraints, and is followed by a novel synthesis stage. We devise synthesis algorithms to obtain high quality pairs of transmit sequence and receive filter that well approximate the behavior of the optimal SINR (of the relaxed problem) with respect to target Doppler shift. Several numerical examples are provided to analyze the performance obtained by DESIDE. © 1991-2012 IEEE.