In this work, we consider a backscatter communication system wherein multiple asynchronous sources (tags) exploit the reverberation generated by a nearby radar transmitter as an ambient carrier to deliver a message to a common destination (reader) through a number of available subchannels. We propose a new encoding strategy wherein each tag transmits both pilot and data symbols on each subchannel and repeats some of the data symbols on multiple subchannels. We then exploit this signal structure to derive two semi-blind iterative algorithms for joint estimation of the data symbols and the subchannel responses that are also able to handle some missing measurements. The proposed encoding/decoding strategies are scalable with the number of tags and their payload and can achieve different tradeoffs in terms of transmission and error rates. Some numerical examples are provided to illustrate the merits of the proposed solutions.

This paper considers the problem of integrated sensing and communications in a user-centric cell-free massive MIMO system employing OFDM modulation. It is assumed that a large number of access points is using the same frequency band in order to communicate with a certain number of mobile users and to perform surveillance against potential targets in the surrounding area. The paper develops all the needed transceiver signal processing needed to implement both the communication and radar functionality, and provides expressions for the generic user downlink SINR and for the radar SNR. Numerical results are finally presented to show the effectiveness of the proposed system and to highlight the impact on the system performance of the different power split between the communication and radar functionalities, as well as of the access points and users density in the considered area.

In this paper, we consider a monostatic radar and study the joint detection and localization of a prospective target when the receiver is assisted by a reconfigurable intelligent surface (RIS). To mitigate the multiplicative path loss in the target-RIS-radar hops, we resort to an active RIS, which allows both redirecting and amplifying the incident signal. Upon choosing the array gain factor of the radar transmitter and the RIS to uniformly cover the inspected region, the joint target detection and localization is formulated as a composite hypothesis testing problem, which is solved via a generalized likelihood ratio test (GLRT). Numerical examples are provided to show the merits of the proposed architecture.

In this work, we exploit the radar clutter (i.e., the ensemble of echoes generated by the terrain and/or the surrounding objects in response to the signal emitted by a radar transmitter) as a carrier signal to enable an ambient backscatter communication from a source (tag) to a destination (reader). The proposed idea relies on the fact that, since the radar excitation is periodic, the radar clutter is itself periodic over time scales shorter than the coherence time of the environment. Upon deriving a convenient signal model, we propose two encoding/decoding schemes that do not require any coordination with the radar transmitter or knowledge of the radar waveform. Different tradeoffs in terms of transmission rate and error probability can be obtained upon changing the control signal driving the tag switch or the adopted encoding rule; also, multiple tags can be accommodated with either a sourced or an unsourced multiple access strategy. Some illustrative examples are provided.

This paper considers a monostatic multiple-input multiple-output (MIMO) radar aided by a reconfigurable intelligent surface (RIS). After modeling the received target signal as the superposition of four echoes, which may involve up to two bounces off the RIS, the phases of the reflecting elements and the radar receive filter are chosen to maximize the signal-to-noise ratio (SNR). Some examples are provided to assess the performance of this novel architecture.

In this work we consider a multiple-input multiple-output (MIMO) dual-function radar-communication (DFRC) system, which senses multiple spatial directions and serves multiple users. Upon resorting to an orthogonal frequency division multiplexing (OFDM) transmission format and a differential phase shift keying (DPSK) modulation, we study the design of the radiated waveforms and of the receive filters employed by the radar and the users. The approach is communication-centric, in the sense that a radar-oriented objective is optimized under constraints on the average transmit power, the power leakage towards specific directions, and the error rate of each user, thus safeguarding the communication quality of service (QoS). We adopt a unified design approach allowing a broad family of radar objectives, including both estimation- and detection-oriented merit functions. We devise a suboptimal solution based on alternating optimization of the involved variables, a convex restriction of the feasible search set, and minorization-maximization, offering a single algorithm for all of the radar merit functions in the considered family. Finally, the performance is inspected through numerical examples.

Active reconfigurable intelligent surfaces (RISs) are a novel and promising technology that allows controlling the radio propagation environment while compensating for the product path loss along the RIS-assisted path. In this letter, we consider the classical radar detection problem and propose to use an active RIS to get a second independent look at a prospective target illuminated by the radar transmitter. At the design stage, we select the power emitted by the radar, the number of RIS elements, and their amplification factor in order to maximize the detection probability for a fixed probability of false alarm and a common (among radar and RIS) power budget. An illustrative example is provided to assess the achievable detection performance, also in comparison with that of a radar operating alone or with the help of a passive RIS.

In this work, we consider the target detection problem in a sensing architecture where the radar is aided by a reconfigurable intelligent surface (RIS), that can be modeled as an array of sub-wavelength small reflective elements capable of imposing a tunable phase shift to the impinging waves and, ultimately, of providing the radar with an additional echo of the target. A theoretical analysis is carried out for closely- and widely-spaced (with respect to the target) radar and RIS and for different beampattern configurations, and some examples are provided to show that large gains can be achieved by the considered detection architecture.

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 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 echoes and estimating their parameters (namely, amplitude, delay, and Doppler shift). 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) reverberations from the environment. Examples are provided to assess the system performance, also in comparison with other detectors/estimators and the Cramér-Rao bounds on the localization accuracy. Results indicate that the proposed method is robust against the interference induced by the imperfect ambiguity function and can achieve a delay resolution approximately equal to the inverse of the signal bandwidth, corresponding to a range offset of about 17 cm. Instead, the Doppler resolution is inherently limited by the short duration of the data packet and, in the most favorable case, is approximately 3.26 KHz, corresponding to a relative radial velocity off-set of about 8.16 m/s for a wavelength of 5 mm.

In this work, we study the fundamental tradeoff between integration time and scan rate in radar systems. The contrasting needs for a large probability of detection and a short scan time are carefully balanced by optimizing the detection rate, defined as the average number of detections from a target per unit of time. A closed-form solution for the optimum pulse train length is provided in the relevant case of Gaussian observations with unknown noise variance. Some examples are given to show the possible tradeoffs among the principal system parameters.

We consider a surveillance radar that requests a second look of a cell under inspection whenever a reliable decision cannot be made after the first observation. The available degrees of freedom are exploited to maximize the detection rate, defined as the average number of detections from a target per unit of time, under a constraint on the false alarm rate, which is the average number of false alarms per unit of time from the inspected area. A performance comparison with fixed-sample-size detection and alert-and-confirm detection is provided.

This work investigates the feasibility of opportunistic target detection in mmWaves vehicular networks employing the IEEE 802.11ad standard. The beacon signals transmitted during the network discovery phase are exploited to detect the presence of possible obstacles in the surrounding environment and to estimate their position and velocity. The detection problem is formulated as a composite hypothesis test and two detectors are derived and investigated.

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 tradeoffs can be obtained with respect to the single-frame detector and with respect to other strategies already present in the literature.

We consider here the two-stage, multi-frame detection architecture proposed in in the context of radar systems, wherein the Detector and Plot Extractor provides a list of candidate detections (or plots) on a scan-by-scan basis to a Track-before-detect (TBD) Processor, which correlates data over multiple scans before taking the final decision as to the target presence. We propose a heuristic algorithm to implement the TBD processor, and we show that its computational complexity is smaller than that of the algorithm proposed in . Numerical examples demonstrate that the detection and estimation performance obtained with the new algorithm is almost coincident with that obtained with the algorithm in .

In this letter, we consider the detection architecture in , where a track-before-detect processor elaborates the plot-lists provided on a scan-by-scan basis by the detector and plot-extractor of a radar system. We derive a novel track formation procedure in order to provide improved performance in the presence of multiple, closely-spaced targets. Numerical examples are provided to assess the detection capabilities and the accuracy in the estimation of the target position.

In this work, we consider the problem of detecting the presence of a prospective moving target from a set of censored measurements produced by a generic sensor and propose a novel track-before-detect algorithm which exploits the inherent sparse nature of the measurement data to achieve complexity efficiency. The impact of the censoring stage on the system complexity and performance is investigated.

The focus of the paper is on the design of space-time codes for a general multiple-input, multiple-output detection problem, when multiple observations are available at the receiver. The figure of merit used for optimization purposes is the convex combination of the Kullback-Leibler divergences between the densities of the observations under the two hypotheses, and different system constraints are considered. This approach permits to control the average sample number (i.e., the time for taking a decision) in a sequential probability ratio test and to asymptotically minimize the probability of miss in a likelihood ratio test: the solutions offer an interesting insight in the optimal transmit policies, encapsulated in the rank of the code matrix, which rules the amount of diversity to be generated, as well as in the power allocation policy along the active eigenmodes. A study of the region of achievable divergence pairs, whose availability permits optimization of a wide range of merit figures, is also undertaken. A set of numerical results is finally given, in order to analyze and discuss the performance and validate the theoretical results.

The problem of robust waveform design for multiple-input, multiple-output radars equipped with widely-spaced antennas is addressed here. Robust design is needed as a number of parameters are unknown, e.g., the target scattering covariance matrix and, possibly, the clutter covariance matrix. A min-max approach is proposed, so that the code matrix is designed to minimize the worst-case cost (or equivalently maximize the corresponding figure of merit) under all possible target (or target and clutter) covariance matrices. Surprisingly, the same min-max solution applies to many commonly adopted performance measures, such as the average signal-to-clutter-plus-noise ratio, the mutual information between the received signal echoes and the unknown target response, and the approximation of the detection probability in the high- and low-signal regimes.

The problem of joint detection and state estimation of a Markov signal when a variable number of noisy measurements can be taken is here considered. In particular, the signal-observation sequence {Xi, Zi}iisinNis a hidden Markov process (HMP) while, if the signal is absent, the measurement {Zi}i2isinNis an i.i.d. process. In this framework, two coupled detection and estimation procedures are introduced for the cases of discrete and continuous state space. Bounds on the performance of the proposed procedures in terms of the thresholds are derived, similar to the classical bounds for the sequential probability ratio test (SPRT). Moreover, it is shown that, under a set of rather mild conditions, the procedures end with probability one and the stopping time is almost surely minimized in the class of tests with the same or smaller error probabilities.

We study the minimum mean square error (MMSE) and the multiuser efficiency eta of large dynamic multiple access communication systems in which optimal multiuser detection is performed at the receiver as the number and the identities of active users is allowed to change at each transmission time. The system dynamics are ruled by a Markov model describing the evolution of the channel occupancy and a large-system analysis is performed when the number of observations grow large. Starting on the equivalent scalar channel and the fixed-point equation tying multiuser efficiency and MMSE, we extend it to the case of a dynamic channel, and derive lower and upper bounds for the MMSE (and, thus, for eta as well) holding true in the limit of large signal-to-noise ratios and increasingly large observation time T.

The problem of detecting a Markov signal when a variable number of noisy measurements can be taken is here considered. In particular, the signal-observation sequence {Xi, Zi}iisinNopfis a hidden Markov model (HMM) and a sequential probability ratio test (SPRT) is used to detect {Xi, Zi}iisinNopf. It is known that the SPRT for testing simple hypotheses based on independent and identically distributed (i.i.d.) observations has a number of remarkable properties, the most appealing being the fact that it simultaneously minimizes the expected sample size under both hypotheses. These properties, however, may fail to hold as the observations {Zi}iisinNopfare not independent. In this paper sufficient conditions for the validity of these properties are stated. In particular, it is shown that under a set of rather mild conditions the test ends with probability one and its stopping time is almost surely minimized in the class of tests with the same or smaller error probabilities. Furthermore, reinforcing one of such conditions, it is also shown that any moment of the stopping time distribution is first-order asymptotically minimized in the same class of tests.

The problem of joint detection and estimation when a variable number of noisy measurements can be taken is here considered in the case that the signal to be detected is generated by a dynamic system with a Markov evolution and the parameter to be estimated is the trajectory of the state evolution of the system itself and/or it final state (position). Starting from previous sequential rules, different sequential strategies are proposed and assessed: they are aimed at maximizing the performance of either the detection or the track estimation or the position estimation. Bounds on the performances of the proposed procedures in terms of the system parameters are derived and computational complexity is examined. Also, numerical experiments are provided to elicit the interplay between parameters and system performances and to quantify the gain with respect to other fixed-sample-size procedures.

The problem of detection and possible estimation of a signal generated by a dynamic system when a variable number of noisy measurements can be taken is here considered. Assuming a Markov evolution of the system (in particular, the pair signal-observation forms a hidden Markov model (HMM)), a sequential procedure is proposed, wherein the detection part is a sequential probability ratio test (SPRT) and the estimation part relies upon a maximumaposterioriprobability (MAP) criterion, gated by the detection stage (the parameter to be estimated is the trajectory of the state evolution of the system itself). A thorough analysis of the asymptotic behavior of the test in this new scenario is given, and sufficient conditions for its asymptotic optimality are stated, i.e., for almost sure minimization of the stopping time and for (first-order) minimization of any moment of its distribution. An application to radar surveillance problems is also examined.

The problem of blind detection in a dispersive multiple-input multiple-output (MIMO) code division multiple access (CDMA) channel is considered in this paper. Unlike previous studies, each user is assigned one spreading code to be employed on all of the transmit antennas, which poses a problem of data re-association at the receiver-end in the absence of prior information as to the channel state. Focusing on the differential Alamouti scheme, we propose a two-stage receive structure. The first stage performs a linear interference-blocking transformation, which allows user separation and inter-symbol interference (ISI) suppression. The second stage is, instead, a novel differential space-time block (STB) decoder suitable for frequency-selective channels. Interestingly, the proposed detector allows decoupling of the decisions on the transmitted symbols, while its blind implementation only requires a cubic (in the processing gain) complexity. A thorough performance assessment is undertaken to investigate, on one hand, the capability of acquiring the missing information, such as the system and the encoder timings, on the other hand, the interplay between the diversity gain provided by the MIMO structure of the communication system, and the additional co-channel interference that multiple transmit antennas produce in multi-path, multiple-access channels.

In this paper we consider a general asynchronous frequency-selective multiuser CDMA system with transmit and receive antennae, where the information data stream is space-time encoded. Sufficient conditions for fully blind detection are established and several detection strategies are presented. The proposed detection strategy consists of three different stages. The first block exploits the deterministic structure imposed by the spreading code to jointly estimate the symbol timing. The second stage consists of a sequence of observable consisting of the two useful signals and residual interference plus white noise. The last stage is about space-time decoding of the ordered sequence.