Cognitive Waveform Design Research Articles

A General Evaluation System for Optimal Selection Performance of Radar Clutter Model

The optimal selection of radar clutter model is the premise of target detection, tracking, recognition, and cognitive waveform design in clutter background. Clutter characterization models are usually derived by mathematical simplification or empirical data fitting. However, the lack of standard model labels is a challenge in the optimal selection process. To solve this problem, a general three-level evaluation system for the model selection performance is proposed, including model selection accuracy index based on simulation data, fit goodness indexs based on the optimally selected model, and evaluation index based on the supporting performance to its third-party. The three-level evaluation system can more comprehensively and accurately describe the selection performance of the radar clutter model in different ways, and can be popularized and applied to the evaluation of other similar characterization model selection.

Enhanced target detection using a new cognitive sonar waveform design in shallow water

A cognitive waveform design is proposed for use in SONARs (Sound Operated Navigation and Ranging) for detecting targets in undersurface environment. The study adopts a waveform design procedure exploiting prior knowledge gained from environmental reverberations and target signals based on maximizing signal- to- interference-plus- noise ratio (SINR) in the receiver. A genetic algorithm is set forth to work out the optimal specifications in a wideband waveform design. Using initial Linear Frequency Modulated (LFM) waveform as input to the proposed optimization algorithm, an optimal waveform is obtained showing significant improvements in SINR. A number of experiments are conducted to simulate Probability of Detection (POD) versus Signal-to-Reverberation Ratio (SRR) in underwater environments. Analyzing simulation results reveals considerable improvements in target detection task utilizing optimal waveform design compared to initial LFM waveform. Also, in comparison with the initial LFM waveform, it was noticed that there was a substantial reduction in the bandwidth of the optimized design waveform.Our proposed method solves the problem of finding the optimal frequency for designing a cognitive waveform appropriate to the conditions of the environment and the target. We proposed the genetic algorithm method to solve the waveform design problem that maximize the SINR. This paper provides a general methodology for extracting the optimal frequency parameters of the waveform and can be extended to other parameters in cognitive design.

Cognitive waveform design with desired spectrum-autocorrelation properties

This paper proposes a novel cognitive waveform design methodology aimed at shaping a specified spectrum and achieving good autocorrelation properties in terms of the spectrum band notch and local autocorrelation peak sidelobe level (A-PSL). To this end, the Pareto optimization framework is introduced that minimizes the lp-norm of multi-objective functions both encompassing the stopband energy and local A-PSL. In addition, along with energy and peak-to-average power ratio constraints to ensure hardware compatibility, the out-of-band (OOB) spectrum energy and the rest autocorrelation sidelobe level are restricted to resist the OOB strong interference (also prevent spectrum leakage) and enhance multiple target detectability, respectively. To tackle the resultant nonconvex nonsmooth (in general NP-hard) design problem, an iterative majorization minimization proximal method of multipliers (MM-PMM) algorithm is developed along with each iteration involving multiple subproblems which are solved through a majorization method or closed-form solutions. In particular, resorting to Wiener-Khintchine theorem, the developed algorithm is efficiently implemented via the fast fourier transform and element-wise arithmetic. Extensive numerical examples highlight that the synthesized waveform shares the desired spectrum-autocorrelation properties and is capable of enhancing the weak target detection and anti-jamming performance in comparison with some counterparts.

Cognitive Frequency-Hopping Waveform Design for Dual-Function MIMO Radar-Communications System.

A frequency-hopping (FH)-based dual-function multiple-input multiple-output (MIMO) radar communications system enables implementation of a primary radar operation and a secondary communication function simultaneously. The set of transmit waveforms employed to perform the MIMO radar task is generated using FH codes. For each transmit antenna, the communication operation can be realized by embedding one phase symbol during each FH interval. However, as the radar channel is time-variant, it is necessary for a successive waveform optimization scheme to continually obtain target feature information. This research work aims at enhancing the target detection and feature estimation performance by maximizing the mutual information (MI) between the target response and the target returns, and then minimizing the MI between successive target-scattering signals. The two-step cognitive waveform design strategy is based upon continuous learning from the radar scene. The dynamic information about the target feature is utilized to design FH codes. Simulation results show an improvement in target response extraction, target detection probability and delay-Doppler resolution as the number of iterations increases, while still maintaining high data rate with low bit error rates between the proposed system nodes.

Adaptive extended binary phase-shift keying waveform design algorithm for extended target detection

We discuss the problem of extended binary phase-shift keying (EBPSK)–modulated radar waveform design in the presence of colored Gaussian disturbance subject to peak-to-average-power ratio and power constraints. The research work aims at improving the target detection and parameter estimation performance with respect to Doppler shifts. We propose an EBPSK coding design algorithm by optimizing the output signal-to-noise ratio (SNR) in correspondence of the unknown target Doppler frequency. Under the same constraints as in the previous problem, we present EBPSK coding cognitive waveform design algorithm by estimating the target scattering coefficients (TSC) based on maximum output SNR criterion. The simulation experimental results showed that extended target detection performances of optimized EBSPK signals are better than those of the unoptimized EBSPK signal. The designed EBPSK signal attains better TSC estimation performance with relatively lower complexity than the EBPSK signal with arbitrary parameters in the cognitive radar system.

Cognitive Transmitting Frequency Codewords Design for Sparse Stepped Frequency Radar Based on Sensing Matrix Optimization

Cognitive waveform design is of great significance for radar transmitting system to better adapt to the changing electromagnetic environment and target scene. In this paper, a novel cognitive waveform design method of sparse stepped frequency based on sensing matrix optimization is proposed. The proposed cognitive waveform exploits the information returned from the target scene to continuously optimize the transmitted frequency and sensing matrix. Compressive sensing (CS) technique is applied for range-Doppler reconstruction due to the sparse property of the target to be estimated. Comparing with random stepped frequency, this optimized cognitive waveform could further improve the upper limit of the number of detection targets; also increase the accuracy and robustness.

Cognitive Waveform Design for Radar-Communication Transceiver Networks

The system architecture for cognitive radar-communication (CRC) transceiver is proposed. A cognitive waveforms design approach, which is suitable for simultaneously performing both data communication and target detection, is presented. This approach aims at estimating target scattering coefficient (TSC) from the radar scene and facilitating high data rate communications. In order to minimize the mean square error (MSE) of the TSC, a convex cost function is established. The peak to average power ratio- (PAPR-) constrained optimal solution is achieved by applying the Kalman filtering-based strategy to design the set of ultra-wideband (UWB) transmission pulses and embed into them the information data with the M-ary position phase shift keying modulation technique. In addition to theoretical considerations, the simulation results show an improvement in target scattering coefficient (TSC) estimation and target detection probability as the number of iterations increases, while still transmitting data rates in the range of several Mbps with low bit error rates between CRC transceivers.

A generic cognitive framework for supervising the radio dynamic reconfiguration

Cognitive radio is a technological concept pushing for the introduction of intelligent radio operation that goes beyond traditional system adaptation. So far, a rather limited amount of work has been published on the cognitive mechanisms that should be embedded into communicating equipments to achieve such an intelligent behavior. This paper presents a generic cognitive framework for autonomous decision making with regard to multiple, possibly conflicting, operational objectives in a time-varying environment. The framework is based on the definition of two scales introducing order relationships between the configurations that help the reasoning and learning processes. The resulting cognitive engine learns to progressively identify the optimal configurations for the design objectives imposed given the current radio environment. The proposed approach is illustrated for a case of cognitive waveform design and extensive simulation results validate the cognitive engine behavior.