6 research outputs found

    Joint Design of Space-Time Transmit and Receive Weights for Colocated MIMO Radar

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    Compared with single-input multiple-output (SIMO) radar, colocated multiple-input multiple-output (MIMO) radar can detect moving targets better by adopting waveform diversity. When the colocated MIMO radar transmits a set of orthogonal waveforms, the transmit weights are usually set equal to one, and the receive weights are adaptively adjusted to suppress clutter based on space-time adaptive processing technology. This paper proposes the joint design of space-time transmit and receive weights for colocated MIMO radar. The approach is based on the premise that all possible moving targets are detected by setting a lower threshold. In each direction where there may be moving targets, the space-time transmit and receive weights can be iteratively updated by using the proposed approach to improve the output signal-to-interference-plus-noise ratio (SINR), which is helpful to improve the precision of target detection. Simulation results demonstrate that the proposed method improves the output SINR by greater than 13 dB

    Space-Time Transmit-Receive Design for Colocated MIMO Radar

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    This chapter deals with the design of multiple input multiple-output (MIMO) radar space-time transmit code (STTC) and space-time receive filter (STRF) to enhance moving targets detection in the presence of signal-dependent interferences, where we assume that some knowledge of target and clutter statistics are available for MIMO radar system according to a cognitive paradigm by using a site-specific (possible dynamic) environment database. Thus, an iterative sequential optimization algorithm with ensuring the convergence is proposed to maximize the signal to interference plus noise ratio (SINR) under the similarity and constant modulus constraints on the probing waveform. In particular, each iteration of the proposed algorithm requires to solve the hidden convex problems. The computational complexity is linear with the number of iterations and polynomial with the sizes of the STTW and the STRF. Finally, the gain and the computation time of the proposed algorithm also compared with the available methods are evaluated

    Complexity Reduction in Beamforming of Uniform Array Antennas for MIMO Radars

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    Covariance matrix design and beamforming in multiple-input multiple-output (MIMO) radar systems have always been a time-consuming task with a substantial number of unknown variables in the optimization problem to be solved. Based on the radar and target conditions, beamforming can be a dynamic process and in real-time scenarios, it is critical to have a fast beamforming. In this paper, we propose a beampattern matching design technique that is much faster compared to the well-known traditional semidefinite quadratic programming (SQP) counterpart. We show how to calculate the covariance matrix of the probing transmitted signal to obtain the MIMO radar desired beampattern, using a facilitator library. While the proposed technique inherently satisfies the required practical constraints in covariance matrix design, it significantly reduces the number of unknown variables used in the minimum square error (MSE) optimization problem, and therefore reduces the computational complexity considerably. Simulation results show the superiority of the proposed technique in terms of complexity and speed, compared with existing methods. This superiority is enhanced by increasing the number of antennas

    Waveform Design and Processing for Joint Wireless Communications and Sensing

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    Since the advent of radar/sensing systems, they have always had fixed frequencies for operation. Due to the exponential growth of communications systems, the need for dedicated spectrum for them also increased, causing spectrum scarcity for both communications and sensing. It was obvious that some form of flexible spectrum sharing was necessary between these two functionalities. Soon enough, this led the researchers to focus on joint communications and sensing (JCAS) systems that share spectral resources efficiently. The hardware convergence due to the similar functioning of the two systems complemented the frequency convergence of JCAS systems. In fact, JCAS is one of the prominent requirements in future sixth-generation (6G) communications systems. This thesis focuses on integrating the sensing functionality on top of wireless mobile communications systems, such as in fifth-generation (5G). To facilitate effective JCAS, the thesis provides signal processing techniques for designing waveforms that optimally share the spectral resources, for single-input single-output (SISO) as well as multiple-input multiple-output (MIMO) systems. In addition, novel radar processing techniques are investigated for MIMO systems to better detect the targets in the environment. The standard waveform in 5G, that is, orthogonal frequency-division multiplexing (OFDM), is also considered for joint waveform design. In such a communications system, the resources are usually not fully utilized and there exist unused subcarriers within the OFDM waveform. These subcarriers are filled with optimized samples to minimize the lower bounds of delay and velocity estimates’ error variances of sensing, for SISO JCAS systems. The simulations with standard-compliant 5G waveforms illustrate the improvements possible in sensing, while also helping to maximize the efficiency in the transmit power amplification process, along the same optimization scheme. The simulation results are complemented through practical radio-frequency measurements of an outdoor environment depicting the significant gains that can be obtained in the range–angle map of sensing, due to the waveform optimization. For MIMO JCAS systems, apart from conventional communications streams, separate transmit (TX) streams are used to improve sensing performance through two separate schemes. One scheme involves optimizing the sensing streams to minimize the lower bounds of delay and angle estimates’ error variances of sensing. Simulation results indicate that the errors of sensing can be minimized while striking a good balance with the communications capacity. The other scheme depicts that the target detection can be enhanced using sensing streams on top of a communications stream. Specifically, the number of false targets detected can be significantly reduced in comparison to single-stream communication. The antenna arrays in MIMO communications systems nowadays are a combination of analog and digital architectures, i.e., hybrid, instead of consisting of a fullydigital architecture, for reduced costs and power consumption. Radar processing in such a hybrid architecture with multiple TX streams is not straightforward in comparison to the conventional fully-digital MIMO radar. Hence, this thesis also provides novel radar processing techniques to obtain the range–angle and range–velocity maps of the sensed environment. The simulation results illustrate that the targets can be reliably detected through the proposed MIMO processing, while also providing super-resolution in the angular domain
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