Waveform Design for Target Recognition Considering Constant Modulus Constraint

In this paper, we address the design of optimal waveform for detecting extended target in signal-dependent interference. A two-step waveform design algorithm is proposed to obtain the optimal phase-modulated (PM) waveform with constant modulus. We first obtain the normally called water-filling waveform in frequency domain and subsequently synthesize the corresponding time-domain waveform using the minimum spectral mean-squares error (MSE) as a cost function. The phase gradient algorithm is employed to seek the optimal phase of the PM waveform, which can monotonously decrease the spectral MSE for each iterative step. Simulations demonstrate that the proposed algorithm can obtain asymptotic optimal waveform and its performance outperforms the iterative-estimate algorithm.

The problems of multiple-input multiple-output (MIMO) radar adaptive waveform design in additive white Gaussian noise channels and multitarget recognition based on sequential likelihood ratio test are jointly addressed in this paper. Two information-theoretic waveform design strategies, namely, the optimal waveform for maximizing the mutual information (MI) between the extended target impulse response and the target echoes and the optimal waveform for maximizing the Kullback-Leibler (KL) divergence (or relative entropy), are applied in the multitarget recognition application. For multitarget case, two adaptive waveform design methods for all possible targets based on the current knowledge of each hypothesis are proposed. Method 1 is the probability weighted waveform method. Method 2 is the probability weighted target signature method. The optimal waveform is transmitted and adaptively changed such that a decision is made based on the likelihood ratio after several illuminations. Numerical results demonstrate that the best waveform is the KL divergence-based optimal waveform using Method 1 as it has the lowest average illumination number and the highest correct decision rate for target recognition. By optimally designing and adaptively changing the transmitted waveform, the average number of illuminations required for multitarget recognition can be much reduced.

Joint radar communication systems have been recently proposed for radar and communication sharing spectrum and hardware, to reduce the resource cost of platforms. In this paper, we consider the waveform design problem for a joint multiuser MIMO communication and MIMO radar system. We propose an optimization problem for waveform design, in which the signal-to-interference-plus-noise ratio (SINR) at users and the transmit beam pattern of radar are jointly optimized. To guarantee the efficiency of power amplifiers, the transmit waveform is constrained to be constant modulus. To deal with the non-convex constant modulus constraint, we propose an alternating descend method to solve the problem. The simulation results indicate that under high transmit signal-to-noise ratio (SNR), the communication quality can be guaranteed, along with a reasonable performance loss of radar. Moreover, the performance gap between the constant modulus constraint and the more relaxed per-antenna power constraint, is not significant.

This paper proposes a novel method of unimodular transmitting waveforms design for multiple-input multiple-output (MIMO) radar to strengthen the detection performance in the presence of clutter and white Gaussian noise. An improved iterative algorithm is put forward to maximize the signal-to-clutter-plus-noise ratio (SCNR) under the constant modulus constraint. During iterations, the optimization of unimodular waveforms with filters fixed is a nonconvex fractional quadratically constrained quadratic program problem, which is NP-hard and not able to be solved in polynomial time. An algorithm based on semidefinite programming relaxation combined with bisection and Gaussian randomization is introduced to provide the high-quality suboptimal solutions with a polynomial time computational complexity. The analysis on the approximation bound is given to prove the tightness of the semidefinite programming relaxation and so the correctness of the proposed algorithm. The simulation results show that the improved method is efficient in designing unimodular waveforms for MIMO radar to achieve a better SCNR performance.

Apart from basic orthogonality, cognitive multiple-input multiple-output (MIMO) radar waveforms should have the capacity of restraining spectral interferences in complex environment. Constant-modulus waveforms are also desired for the radio frequency (RF) amplifiers. Based on above considerations, this paper proposes an optimization method to design cognitive MIMO radar waveforms with constant modulus and spectral interference constraints. The optimal waveform design problem is converted into standard semi-definite programs (SDP), which are resolved with the semi-definite relaxation (SDR) technique. Simulation results indicate that the proposed approach can effectively design the desired cognitive MIMO radar waveforms with constant modulus and restraining spectral interferences.

Animals that navigate and hunt by echolocation, such as some bats and marine mammals, have been observed to change their sonar pulse depending on the environment, as well as during hunting. It has become of interest to incorporate these strategies into man-made sonar waveform and receiver design. We examine the benefits of optimal waveform design versus transmitting a linear FM waveform for detecting elastic objects. Performance loss suffered by assuming a point target is also examined. Our approach utilizes a method recently proposed by Kay to design the optimal power spectrum of the transmit waveform. Because there is an unlimited number of waveforms with the same power spectrum, we further impose a time domain constraint, in terms of the signal duration, to obtain a unique optimal waveform. [Work supported by ONR 321US.]

With recent advances in both algorithm and component technologies, through-the-wall radar imaging (TWRI) is emerging as an affordable sensor technology in civil and military settings. Design of appropriate waveforms is important for improving the performance of TWRI systems. We present optimal waveforms, based on the matched illumination concept, for detection of targets inside enclosed structures using a monostatic multi-antenna radar system. We evaluate the effect of these optimal detection waveforms on the performance of through-the-wall radar imaging. The design flexibility for transmit signal duration and its role in the ultimate range resolution of the TWRI system is investigated. Supporting examples based on numerical electromagnetic modeling are also provided.

In the past decades, there has been an extensive research interest in the areas of both waveform diversity/design and advanced signal processing algorithms departing from the more classical solutions based on Linear Frequency Modulated (LFM) pulses and Matched Filters (MF). In the waveform diversity community, especially within the context of spectrum sharing, MIMO and cognitive radars, several waveform optimization and design methodologies have been studied, see [1], [2] and references therein. In parallel to waveform design, several signal processing techniques have also been proposed which exploit some kind of prior knowledge and/or iterative algorithms to improve the performance of the more classical MF, such as the Adaptive Pulse Compression(APC) [3], MUSIC [4], CLEAN [5] and Sparse Signal Processing (SSP) [6], [7]. In this paper we present some results and examples to show how the combination of waveform design with SSP can lead to improved performance in radar compared to the more classical approach.

Accelerated degradation testing (ADT) technology for long-life and high-reliability products has become one of the key technologies in life and reliability field. The scientific and reasonable testing program can not only provide correct basis for decision making, but also make full use of resources and reduce the cost of product development. Hence, how to make full use of products’ historical information to develop a short-term efficient pilot program has become a key-problem to be solved in ADT technology. This chaptere proposes the Bayesian optimal design of step-stress accelerated degradation testing (SSADT) based on Gamma process and relative entropy. Firstly, we briefly describe the applicability of Gamma process and the relative entropy in ADT, and the degradation model and relative entropy’s application method are given. Secondly, under the framework of Bayesian theory, we study the Gamma degradation process based SSADT optimal design method by using maximize the relative entropy as the optimization goals and test variables as the optimization design constraints. Finally, we use a 3-steps bulb’s SSADT to verify the effectiveness of the proposed method. The example shows that the method of this chapter is fast and efficient which can comprehensively use the prior information to work out the optimal pilot program.KeywordsPosterior DistributionRelative EntropyDegradation ModelOptimization GoalBayesian TheoryThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Multiobjective optimal waveform design for OFDM integrated radar and communication systems

In this paper, the waveform design under constant modulus constraint is investigated for the purpose of minimizing the sidelobes of the auto-correlations and the cross-correlations in FDA-MIMO radar. At the design stage, the problem is converted to an optimization problem to obtain the unimodular sequences with good auto/cross-correlation properties. Specifically, to solve the equivalent non-convex problem, the majorization-minimization (MM) method is utilized, where an approximate objective function is minimized. At the analysis stage, waveforms are embedded in the FDA-MIMO radar to evaluate the performance of the joint transmit-receive beampattern. Numerical results corroborate the performance of the designed waveforms.

We consider the problem of waveform design for Multiple-Input Multiple-Output (MIMO) radar in the presence of signal-dependent interference embedded in white Gaussian disturbance. We present two sequential optimization procedures to maximize the Signal to Interference plus Noise Ratio (SINR), accounting for a constant modulus constraint as well as a similarity constraint involving a known radar waveform with some desired properties (e.g., in terms of pulse compression and ambiguity). The presented sequential optimization algorithms, based on a relaxation method, yield solutions with good accuracy. Their computational complexity is linear in the number of iterations and trials in the randomized procedure and polynomial in the receive filter length. Finally, we evaluate the proposed techniques, by considering their SINR performance, beam pattern as well as pulse compression property, via numerical simulations.

In this paper we describe methodologies for joint design and exploitation of heterogeneous RF Intelligence Surveillance Reconnaissance (ISR) sensor networks. In this work, we provide an algorithm that simultaneously optimizes a waveform set that overtly incorporates the aspect variant nature of the target and clutter signatures along with the mutual interference of the waveforms. The basis of the optimization is a maximization of the composite signal-to-interference-plus-noise ratio. The work reported here generalizes the single optimal waveform design approach of Pillai et. al. to the case of J simultaneous waveforms. Knowledge of the target spectral support for each aspect is assumed and the waveform set is jointly optimized. Simulation results are given that show the waveform characteristics and the increase in signal-to-interference-plus-noise ratio over sets of chirp waveforms and independently optimized waveforms.

We propose an information theoretic waveform design algorithm for target tracking in a low-grazing angle (LGA) scenario. We incorporate realistic physical and statistical effects, such as Earth's curvature, vertical refractivity gradient of lower atmosphere, and compound-Gaussian characteristics of sea-clutter, into our model. We employ a co-located multiple-input-multiple-output (MIMO) radar configuration using wideband orthogonal frequency division multiplexing (OFDM) signalling scheme. The frequency diversity of OFDM provides richer information about the target as different scattering centers resonate at different frequencies. Additionally, we use polarization-sensitive transceivers to resolve the multipath signals with small separation angles. Thus, we track the scattering coefficients of the target at different frequencies along with its position and velocity. We apply a sequential Monte Carlo method (particle filter) to track the target. Our tracker works in a closed-loop fashion with an integrated optimal waveform design technique based on mutual information (MI) criterion. We seek the optimal OFDM waveform at the current pulse duration to maximize the MI between the state and measurement vectors at the next pulse duration utilizing all the measurement history up to the current pulse. Our numerical examples demonstrate the importance of realistic physical modeling, effects of frequency diversity through OFDM MIMO configuration, and achieved performance improvements due to adaptive OFDM waveform design.

Under high gravity loads, steel double-beam floor systems need to be reinforced by beam-end concrete panels to reduce the material quantity since rotational constraints from the concrete panel can decrease the moment demand by inducing a negative moment at the ends of the beams. However, the optimal design process for the material quantity of steel beams requires a time-consuming iterative analysis for the entire floor system while especially keeping in consideration the rotational constraints in composite connections between the concrete panel and steel beams. This study aimed to develop an optimal design method with the LM (Length-Moment) index for the steel double-beam floor system to minimize material quantity without the iterative design process. The LM index is an indicator that can select a minimum cross-section of the steel beams in consideration of the flexural strength by lateral-torsional buckling. To verify the proposed design method, the material quantities between the proposed and code-based design methods were compared at various gravity loads. The proposed design method successfully optimized the material quantity of the steel double-beam floor systems without the iterative analysis by simply choosing the LM index of the steel beams that can minimize objective function while satisfying the safety-related constraint conditions. In particular, under the high gravity loads, the proposed design method was superb at providing a quantity-optimized design option. Thus, the proposed optimal design method can be an alternative for designing the steel double-beam floor system.