Multiple-Input Multiple-Output (MIMO) radars provide various advantages as compared to conventional radars. Among these advantages, improved angular diversity feature is being explored for future fully autonomous vehicles. Improved angular diversity requires use of orthogonal waveforms at transmit as well as receive sides. This orthogonality between waveforms is critical as the cross-correlation between signals can inhibit the detection of weaker targets due to sidelobes of stronger targets. This paper investigates the Reiterative Minimum Mean Squared Error (RMMSE) mismatch filter design for range sidelobes reduction for a Slow-Time Phase-Coded (ST-PC) Frequency Modulated Continuous Wave (FMCW) MIMO radar. Initially, the performance degradation of RMMSE filter is analyzed for improperly decoded received pulses. It is then shown mathematically that proper decoding of received pulses requires phase compensation related to any phase distortions caused due to doppler and spatial locations of targets. To cater for these phase distortions, it is proposed to re-adjust the traditional order of operations in radar signal processing to doppler, angle and range. Additionally, it is also proposed to incorporate sidelobes decoherence for further suppression of sidelobes. This is achieved by modification of the structured covariance matrix of baseline single-input RMMSE mismatch filter. The modified structured covariance matrix is proposed to include the range estimates corresponding to each transmitter. These proposed modifications provide additional sidelobes suppression while it also provides additional fidelity for target peaks. The proposed approach is demonstrated through simulations as well as field experiments. Superior performance in terms of range sidelobes suppression is observed when compared with baseline RMMSE and traditional Hanning windowed range response.
In this paper, we propose a novel empirical power allocation algorithm based on individual term normalization of N th order Fibonacci polynomial for multi user Non Orthogonal Multiple Access (NOMA), providing a characteristic solution to allocation problem for n-users. A deterministic mathematical expression for same predetermined power allocation scheme for both superposition coding (SC) at base station domain and successive interference cancellation (SIC) at user domain has been formulated. Fibonacci polynomial has been defined and linked to power allocation algorithm corresponding to radial distance of users from BS with sole requirement of second order statistics (SOS). Holistic analytical reasoning has been carried out for generic NOMA system and an exact closed-form expression for bit error rate (BER) has been derived using probabilistic models, which has been applied to proposed allocation algorithm. Detailed analysis validates that proposed power allocation algorithm is independent of user channel state information (CSI) which averts cumbersome computations and no prior information regarding allocation is required to be relayed to user domain for successful SIC. Numerical and simulation results (upto 7 user NOMA) have been provided to demonstrate superior performance in terms of reliability and latency of the proposed algorithm in comparison to channel inversion, a CSI based algorithm.
Modulation recognition plays a crucial role in noncooperative communication in which receivers have no prior information regarding transmitter modulation scheme. This paper presents a novel convolutional neural network with single Stem and Inception module for autonomous modulation classification from raw I/Q received channels. This addresses the computationally intensive problem of conversion of I/Q channels to constellation image processing. The proposed system is capable of classifying 11 standard modulation schemes with both 2D and 3D input array configurations for varying SNR conditions. The performance of proposed system has been evaluated for a realtime communication system simulated with Rician fading channel and AWGN noise model, providing realistic distortion effects. The proposed CNN design achieves an average accuracy of 90% at 10 dB and 99% at 20 dB SNR with reduced network learnables and lower training and testing time, which makes it computationally efficient. These attributes make Inception module based CNN a viable solution for modulation classification in practical low cost and portable yet reliable communication systems.
Multiple-Input Multiple-Output (MIMO) radars provide various advantages as compared to conventional radars. Among these advantages, improved angular diversity feature is being explored for future fully autonomous vehicles. Improved angular diversity requires use of orthogonal waveforms at transmit as well as receive sides. This orthogonality between waveforms is critical as the cross-correlation between signals can inhibit the detection of weaker targets due to sidelobes of stronger targets. This paper investigates the Reiterative Minimum Mean Squared Error (RMMSE) mismatch filter design for range sidelobes reduction for a Slow-Time Phase-Coded (ST-PC) Frequency Modulated Continuous Wave (FMCW) MIMO radar. Initially, the performance degradation of RMMSE filter is analyzed for improperly decoded received pulses. It is then shown mathematically that proper decoding of received pulses requires phase compensation related to any phase distortions caused due to doppler and spatial locations of targets. To cater for these phase distortions, it is proposed to re-adjust the traditional order of operations in radar signal processing to doppler, angle and range. Additionally, it is also proposed to incorporate sidelobes decoherence for further suppression of sidelobes. This is achieved by modification of the structured covariance matrix of baseline single-input RMMSE mismatch filter. The modified structured covariance matrix is proposed to include the range estimates corresponding to each transmitter. These proposed modifications provide additional sidelobes suppression while it also provides additional fidelity for target peaks. The proposed approach is demonstrated through simulations as well as field experiments. Superior performance in terms of range sidelobes suppression is observed when compared with baseline RMMSE and traditional Hanning windowed range response.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.