In this paper, we optimize a Wireless Information and Power Transfer (WIPT) system including multiple pairs of users, where transmitters employ a single-antenna to transmit their information and power to their receivers with the help of one multiple-antenna Amplify-and-Forward (AF) relay or an active Intelligent Reflecting Surface (IRS). We propose a joint Time Switching (TS) scheme in which transmitters, receivers, and the relay/IRS are either in their energy or information transmission/reception modes. The transmitted multi-carrier unmodulated and modulated waveforms are used for Energy Harvesting (EH) and Information Decoding (ID) modes, respectively. In order to design an optimal fair system, we maximize the minimum rate of all pairs for both relay and IRS systems through a unified framework. This framework allows us to simultaneously design energy waveforms, find optimal relay/IRS amplification/reflection matrices, allocate powers for information waveforms, and allocate time durations for various phases. This problem turns out to be non-convex. Thus, we propose an iterative algorithm by using the Minorization-Maximization (MM) technique, which efficiently converges to the optimized solution, i.e., stationary point of the problem (under mild conditions). Numerical examples show that the proposed method improves the performance of the multi-pair WIPT relay/IRS system under various setups.

In this paper, we consider an integrated sensing and communication (ISAC) system with wireless power transfer (WPT) where an unmanned aerial vehicle (UAV)-based radar serves a group of energy-limited communication users in addition to its sensing functionality. In this architecture, the radar senses the environment in phase 1 (namely sensing phase) and mean-while, the communications users (nodes) harvest and store the energy from the radar transmit signal. The stored energy is then used for information transmission from the nodes to UAV in phase 2, i.e., uplink phase. Performance of the radar system depends on the transmit signal as well as the receive filter; the energy of the transmit signal also affects the communication network because it serves as the source of uplink powers. Therefore, we cast a multi-objective design problem addressing performance of both radar and communication systems via optimizing radar transmit waveform, radar receive filter, time scheduling and uplink powers. We further take into account optimization of UAV trajectory for covering a given coverage area. The design problem is non-convex and hence, we devise a method based on alternating optimization followed by concepts of fractional programming as well as tricky majorization-minimization technique to tackle it. Numerical examples illustrate the effectiveness of the pro-posed method for performance improvement of the considered ISAC system.

Abstract This paper examines we aim to improve the performance of target detection in the presence of signal‐dependent interference in multiple‐input multiple‐output radar through the joint design of the receiver spatial filter and the covariance matrix of waveforms. For this purpose, the signal‐to‐interference‐plus‐noise ratio is considered as the design criterion. The proposed method is a sequential algorithm that calculates the receiver filter coefficients through a closed form in each step of the algorithm. The correlation of transmit waveforms is also calculated utilizing the coordinate descent method. The covariance matrix is designed in two forms. In the first form, it is assumed that the optimal matrix is a Toeplitz matrix which leads to a simpler design. In the second form, the covariance matrix is calculated by combining orthogonal waveforms, which yields better performance for interfere reduction. Simulation results show that the proposed method has more appropriate performance in comparison with other methods.

In this study, two iterative design algorithms for multiple‐input multiple‐output (MIMO) radars with co‐located antennas with point targets are proposed. In these algorithms, by joint design of the receiver filter and transmit waveform using linear combination of orthogonal waveforms, the authors aim to maximise the signal‐to‐interference plus‐noise ratio (SINR) in the presence of signal dependent interference. In the first proposed algorithm, in each iteration, transmit waveforms and receive filter are designed in closed form to decrease the computational complexity. In the second method, by adding constant envelope criteria, the final waveform would be a linear combination of orthogonal waveforms and because of using a constant envelope, combining coefficients have equal magnitudes and different phases. Therefore, it is more practical for hardware implementation in comparison to the first proposed method. Simulation results show that both proposed methods have better SINR performances compared with other methods proposed in MIMO radar literature. The outperformance of the second proposed method with respect to phased arrays shows that by only using phase shifted combination of orthogonal waveforms, better performance in comparison to phased array radars can be achieved.

Multiple‐input multiple‐output (MIMO) radars have attracted much attention for their superior ability to enhance a system's performance. In this study, the authors’ goal was the study of the spatial multiplexing gain of MIMO radars with widely separated antennas (WS‐MIMO), which the authors showed that is equal to the number of unambiguously detectable targets. They obtained this number from two different aspects: first, by defining the ambiguity function of a WS‐MIMO radar in the case of multiple targets, suitable for such purpose; Second, by modelling the MIMO radar system with a MIMO wireless channel. They showed that a MIMO radar is indeed a MIMO wireless system communicating the information about the existence of the targets. By such modelling, they could easily make the relation between dual concepts of MIMO radar and MIMO communication, one of which is the multiplexing gain.

It is shown that the detection performance can be significantly improved using the recent technology of multiple‐input multiple‐output (MIMO) radar systems. This is a result of the spatial diversity in such systems due to the viewing of the target from different angles. On the other hand, the moving target indication (MTI) processing has long been known and applied in the traditional pulse radars to detect weak moving targets in the presence of strong clutter signals. The authors propose a procedure based on the compressive sensing idea, in order to apply the MTI processing in a MIMO radar with widely separated antennas. Although a clutter is included in the signal model and a different radar cross‐section value for each transmitter–receiver pair is considered which makes the problem more complex, the complexity dimension is preserved as low as possible by converting the block sparse problem into a regular sparse problem.

This letter considers joint design of transmit waveform and receive filter in colocated multiple-input multiple-output (MIMO) radars in order to enhance target detection performance in the presence of signal-dependent interference. Here, signal-to-interference-plus-noise ratio (SINR) is maximized under the practical constraints of integrated sidelobe level (ISL) and peak sidelobe level (PSL) at the pulse compression output. We have also shown that the joint transmit signal and receive filter design can be formulated as a convex optimization problem, which can be efficiently solved using optimization toolbox (CVX). Simulation results show that our proposed algorithm is able to suppress interference efficiently under ISL and PSL constraints and get close to the SINR efficiency of unconstrained methods.

By employing the MIMO (multiple‐input–multiple‐output) technology in radar, some new problems emerged, that, in order to benefit the MIMO gains in radar, it was necessary to solve them suitably. One of such obstacles is determining the positions of receive antennas in a MIMO radar system with widely separated antennas (WS MIMO radar), since it is shown that the antennas’ positions affect the whole system's performance considerably. In this study, a proper receivers’ positioning procedure is proposed. To do this end, four criteria are developed based on the proposed MIMO detector and the MIMO ambiguity function. The simulations verify that the proposed positioning procedure improves the radar's performance in many aspects, such as the overall probability of detection.

High resolution range profile (HRRP) is a widely noted tool in radar target recognition. However, its high sensitivity to the target's aspect angle makes it necessary to seek solutions for this problem. One alternative is to assume consecutive samples of HRRP identically and independently distributed in small frames of aspect angles, an assumption which is not true in reality. Based on this simplifying assumption, some models, such as the hidden Markov model, have been developed to characterise the sequential information contained in multi‐aspect radar echoes. As a result, these models consider only the short dependency between consecutive samples. Considering such issues, in this study, the authors propose an alternative polynomial segment model. In addition, using a Markov chain Monte–Carlo based Gibbs sampler as an iterative approach to estimate the parameters of the segment model, the authors will show that the results are quite satisfactory.

The authors propose invariant tests for the detection of a complex signal with unknown constant amplitude and unknown phase variation in additive white Gaussian noise (AWGN). The authors show that in this problem, the uniformly most powerful invariant (UMPI) detector does exist only if the number of samples N is two. For more than two samples N≥3, the authors derive the most powerful invariant (MPI) detector in known signal-to-noise ratio (SNR) and use its performance as the upper bound benchmark for any invariant test. In addition, the authors derive the generalised likelihood ratio (GLR) detector and evaluate its performance against the MPI performance bound. This detector is very simple and represents the ratio of the L1-norm to the L2-norm of the data. Simulation results illustrate the close performances of the two detectors even at low SNRs, whereas in contrast to the MPI test the SNR is not required in the proposed GLR test. In order to understand why the knowledge of SNR is not so important in this detection problem, the authors also derive the GLR test for the case of known SNR. Interestingly, the resulting GLR detector (derived for the case of known SNR) turns out to be equivalent to the one derived for unknown SNR, i.e. a knowledge of the SNR is not used in any of the GLR tests. This reveals why the knowledge of the SNR is not so useful in this detection problem.

Polarity coincidence correlator (PCC), when used to estimate the covariance matrix on an element-by-element basis, may not yield a positive semi-definite (PSD) estimate. Devlin et al. (Devlin, 1975) claimed that element-wise PCC is not guaranteed to be PSD in dimensions p > 3 for real signals. However, no justification or proof was available on this issue. In this letter, it is proved that for real signals with p les 3 and for complex signals with p les 2, a PSD estimate is guaranteed. Counterexamples are presented for higher dimensions which yield invalid covariance estimates.

We propose computationally inexpensive and efficient solutions for signal activity detection of phase-shift keying (PSK) signals in additive white Gaussian noise. We consider the complex amplitude of the signal as well as the information sequence as the unknown parameters. In addition, the noise variance is assumed unknown. We derive the generalized likelihood ratio test (GLRT) and suggest a computationally efficient implementation thereof. Furthermore, we develop a new inexpensive detector for binary PSK signals, which we will refer to as the generalized energy detector. To evaluate the performance of these detectors, we attempt to derive a uniformly most powerful invariant test (UMPI) as an optimal detector. It turns out that the UMPI test exists only if the signal-to-noise ratio is known. We use this UMPI test in order to obtain an upper-bound performance for the evaluation of invariant detectors, such as the above-mentioned GLRT. Simulation results illustrate and compare the performance and the efficiency of the proposed signal activity detectors.

We propose computationally inexpensive and efficient solutions for signal activity detection of phase-shift keying (PSK) signals in additive white Gaussian noise. We consider the complex amplitude of the signal, as well as the information sequence, as the unknown parameters. In addition, the noise variance is assumed unknown. We derive the generalized likelihood ratio test (GLRT) and suggest a computationally efficient implementation thereof. Furthermore, we develop a new inexpensive detector for binary PSK signals, which we will refer to as the generalized energy detector. To evaluate the performance of these detectors, we attempt to derive a uniformly most powerful invariant (UMPI) test as an optimal detector. It turns out that the UMPI test exists only if the signal-to-noise ratio is known. We use this UMPI test in order to obtain an upper-bound performance for the evaluation of invariant detectors, such as the GLRT. Simulation results illustrate and compare the performance and the efficiency of the proposed signal activity detectors