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In this work, we study the joint detection and localization of multiple delay- and Doppler-spread targets through an opportunistic radar exploiting mmWave communication signals. The problem is formulated as the identification of an unknown number of active subspaces in a large family of subspaces, accounting for the possible positions of potential targets in the delay-Doppler domain: the resulting testing problem is composite and multi-hypothesis. At first, we derive a solution based on the generalized information criterion (GIC), whose complexity is however prohibitive; then, we propose an approximated form of the GIC-based receiver and derive two iterative data-adaptive strategies, both extracting and eliminating the superimposed back-scattered subspace signals one-by-one. Leveraging the IEEE 802.11ad standard, a short-range low-mobility application is discussed to validate the merits of the proposed procedures in terms of detection and localization capabilities, robustness to multi-target interference, and achievable resolution.

In this paper, we investigate the spectral coexistence between a multiple-input multiple-output (MIMO) communication system and a MIMO radar which inspects multiple range gates with non-homogeneous interference. We consider two formulations for joint resource allocation, aiming at minimizing the inverse of the harmonic mean of the signal-to-interference-plus noise ratio (SINR) across the range gates inspected by the radar under a constraint on the communication mutual information or at minimizing a properly-weighted difference between the inverse of the harmonic mean of the SINR's and the mutual information, respectively. The degrees of freedom for system design are the radar probing signal, the radar receive filters at each range gate, and the communication codebook, under the usual constraints on the transmit power and on the similarity of the radar waveform to a reference signal. We derive a sub-optimal procedure that allows to decompose the non-convex optimizations at the hand into a sequence of simpler sub-problems. Numerical examples are provided to assess the merits of the proposed solutions.

In this work, we exploit the sector level sweep of the IEEE 802.11ad communication standard to implement an opportunistic radar at mmWaves and derive an adaptive procedure for detecting multiple targets (echoes) and estimating their parameters. The proposed detector/estimator extracts the prospective echoes one-by-one from the received signal, after removing the interference caused by the previously detected (stronger) targets. Examples are provided to assess the system performance, also in comparison with the canonical matched-filter peak-detector and the Cram\'er-Rao bounds. Results indicate that the proposed method is robust against the signal spillover and the near-far problem caused by the imperfect auto-correlation of the probing signal and, for the same probability of false alarm, grants detection and localization performances close to those previously obtained in a simplified single-target scenario., Comment: Journal paper submitted to the IEEE Transactions on Wireless Communications on April 19, 2019

In this work, we study the problem of detecting multiple targets (echoes) and estimating their parameters by using the sector level sweep of the IEEE 802.11ad communication standard in order to realize an opportunistic radar at mmWaves. We derive an adaptive detector/estimator which extracts the prospective echoes one-by-one from the received signal, after removing the interference caused by the previously detected (stronger) targets. The numerical analysis indicates that the proposed solution can achieve detection and estimation performances close to those obtained in a single-target scenario.

In this work we consider the IEEE 802.11ad communication standard operating at 60 GHz and exploit the echoes generated during the sector level sweep for target detection and localization. We propose a novel adaptive procedure that extracts the prospective echoes one-by-one from the received signal, after removing the interference caused by the previously detected (stronger) targets. The proposed detector/estimator is robust against the signal spillover and the near-far problem caused by the imperfect auto-correlation of the probing signal. Numerical examples are provided to validate the proposed solution.

A two-step scanning policy for early target detection with agile-beam radars is proposed and analyzed here. In the first step (search) the whole area is scanned, while in the second step (revisit) the ambiguity on returns whose test statistic is close to the threshold is solved. The theoretical analysis, corroborated by experimental results, shows that the additional degrees of freedom provided by the revisit phase results in a reduction of the mean time needed to detect the appearance of a new target in the controlled scene.

We consider the problem of multi-frame target detection in radar systems.. The Detector and Plot Extractor, operating on a scan-by-scan basis, provides a list of candidate detections (or plots) to a Track-before-detect (TBD) Processor, which correlates data over multiple scans (or frames) before taking the final decision as to the target presence. A new dynamic programming algorithm to implement the TBD processor is derived: the algorithm accounts for the fact that the number of plots can be much smaller than the number of resolution elements and efficiently computes the test statistics. Its computational complexity scales linearly with the number of integrated scans and quadratically with the average number of detection in each plot-list. Numerical examples are given to show the merits of the proposed algorithm.

In this paper we address the problem of multi-frame detection (MFD) of a Markov target observed through noisy measurements. To limit the system complexity, we gate the detector with a pre-processing stage which discards unreliable observations in each frame. A novel dynamic programming algorithm is introduced, which applies a track-before-detect (TBD) logic to declare the presence of the target and jointly estimate its position. The closed-form complexity analysis and the numerical examples show that advantageous complexity-performance tradeoff's can be obtained with respect to the single-frame detector and with respect to other strategies already present in the literature.

In this paper, we consider the problem of detecting the presence of a prospective, moving target from a set of noisy measurements. We propose a two-steps approach: The first step discards unreliable measurements (i.e., those whose likelihood ratio falls below a preassigned threshold); The second step, instead, exploits the correlation among observations taken at different time instants and makes the final decision. A novel, computationally efficient, track-before-detect algorithm which exploits the sparse nature of the measurements is proposed, and experimental results to asses the algorithm performance are provided.

We consider a MIMO radar system with widely-spaced transmit/receive antennas and study the problem of designing the signal waveforms transmitted by each source node so as to maximize the detection probability for a fixed false alarm rate under correlated Gaussian clutter. The proposed design procedure relies on a stochastic-gradient search and is general enough to be applied to any statistical target model. Examples are provided to compare the performance of the proposed codes with other known coding schemes.

In this paper, we consider a multiple-input-multiple-output (MIMO) detection problem with M widely spaced transmit antennas and L widely spaced receive antennas, and we study the problem of designing the signal waveforms transmitted by each source node under non-Gaussian target scattering and temporally correlated Gaussian clutter. Two figures of merit are investigated for space-time code (STC) optimization under a semidefinite rank constraint: 1) the lower Chernoff bound (LCB) to the detection probability for fixed probability of false alarm, and 2) the mutual information (MI) between the observations available at the receive nodes and the “channel response” generated by a point-like target, assumed present tout court. Both receive and transmit power constraints are discussed. If the scattering distribution possesses some suitably defined properties of unitary invariance (see ????? ), both MI-optimal and LCB-optimal STCs have a simple canonical structure: the same set of (clutter dependant) temporal codewords are employed at the transmit nodes, the only difference among the many solutions being the amount of power radiated by each antenna. Such a spatial power allocation critically depends upon the adopted figure of merit, the specified power constraint, and the underlying scattering model. Sufficient conditions to determine the optimal power allocation for all design criteria are provided. Asymptotic power distributions are also derived in the limit of vanishingly small and increasingly large signal-to-clutter ratios, proving that assuming Gaussian scattering at the design stage is a robust choice. A case study of relevant practical interest is examined in depth so as to compare the proposed design criteria and to assess the impact of signal non-Gaussianity on the system performances.

In this paper, a multiple-input multiple-output (MIMO) detection problem is considered. At first, we derive the generalized likelihood ratio test (GLRT) for arbitrary transmitted signals and arbitrary time-correlation of the disturbance. Then, we investigate design criteria for the transmitted waveforms in both power-unlimited and power-limited systems and we derive closed-form formulas for the probability of false alarm and the detection probability. Finally, we study the interplay among the rank of the optimized space-time code (i.e., the number of linearly independent transmitted waveforms), the number of transmit diversity paths generated in the signal space, and the amount of energy integrated along each path. The results confirm that there is an inherent tradeoff between diversity and integration, and that no uniformly optimum waveform design strategy exists.