Joint waveform optimization and adaptive processing for random phase radar signals

Over the last decade, research on multistatic radar waveform design has attracted significant consideration among the radar system designers.Among the waveform design techniques, linear frequency modulated waveforms are widely used, but the problem associated with it is range Doppler coupling and complexity in achieving adequate pulse-to-pulse diversity. In this letter, an orthogonal linear frequency modulated (OLFM) waveform has been used as the transmitted waveforms, which has good correlation properties and large time bandwidth product. The ambiguity function (AF), which is the output of the matched filter, is used to analyze the target resolution capabilities of the OLFM waveforms. The mathematical expressions of the AF with OLFM waveforms for the multistatic architecture are represented based on the target fluctuations observed at the receiver. These expressions allow the ambiguity in resolving target position and velocity vectors for any transmitted waveform in the multistatic topology. The normalized cross-correlation value of the LFM waveforms is compared with the OLFM waveforms, which signify the isolation factor between two transmitted waveforms at the receiver. A performance comparison has been made based on the probability of target detection for various multistatic scenarios under numerous signal-to-noise ratio (SNR) conditions.

We investigate waveform design for coexistence between a multiple-input multiple-output (MIMO) radar and MIMO communications (MRMC), with a radar-centric criterion that leads to a minimal interference in the communications system. The communications use the traditional mode of operation in Long Term Evolution (LTE)/Advanced (FDD), where we formulate the design problem based on information-theoretic criterion with the discrete phase constraint at the design stage. The optimization problem, is non-convex, multi-objective and multi-variable, where we propose an efficient algorithm based on the coordinate descent (CD) framework to simultaneously improve radar target detection performance and the communications rate. The numerical results indicate the effectiveness of the proposed algorithm in designing discrete phase set of sequences, potentially binary.

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 article addresses the problem of radar waveform design for imaging targets in a cluttered environment. A multistatic radar scenario is considered for sparse and random sensor positions. The target impulse response is modeled using the simulated frequency response of a buried metallic mine, and the clutter is modeled using the compound Gaussian (CG) distribution. We explore novel waveform design techniques with respect to the mutual information (MI) criterion based on the CG clutter. The first method presents a waveform that exploits the CG distribution of the scene reflectivity function when projected onto a sparse basis. This is compared to the second method, called the target-specific approach that uses knowledge of the target and clutter frequency responses to optimize a matched illumination waveform. In both cases, the Taguchi and particle swarm optimization solvers are employed for MI-based waveform design optimization. To validate and compare the effectiveness of the optimized waveforms, the resulting scene reflectivity function is estimated using the sparsity-driven regularization radar imaging method. Our experimental results demonstrate that both waveform optimization techniques result in significantly better image reconstruction performance than the traditional linear-frequency-modulated waveform, and that the target-specific approach additionally suppresses clutter information in the scene.

Airborne radar system performance primarily depends on the target characteristics and the type of waveform used. Based on the type of waveform used, target detection, range resolution, doppler resolution and peak to side lobe level ratio (PSLR) can be improved in a radar system. Hence for a radar engineer choosing a proper waveform is critical in order to meet the system performance in different target environment conditions. This paper focuses on designing a suitable radar waveform for sea environment considering Sea clutter, Sea states, doppler and Swerling target models in X Band. In order to design a waveform under different Sea environments, an end to end radar system will be modelled, simulated and verified the system performance in Matlab. PSLR, Integrated Side Lobe ratio (ISLR), range resolution and target detection will be considered as performance metrics for the design of the waveform.

Multiple input multiple output (MIMO) radars have been intensively studied for their superior performance merits compared to conventional radars including improved spatial resolution and parameter estimation. To achieve these merits, the transmitted waveforms from the MIMO transmitting antennas should be mutually orthogonal. Herein, the orthogonality is attained in the Doppler domain, namely Doppler division multiple access (DDMA), as it enjoys the simplest structure among other orthogonality schemes. Heretofore, the research in MIMO radars has focused on optimizing MIMO radar performance from different aspects including efficient waveform design, antenna beam-forming, and adaptive processing techniques. However, MIMO radars have been also a target of deliberate jamming scenarios. In this paper, the focus is turned to the evaluation of the detection performance of DDMA MIMO radars in jamming environments by creating a two-sided engagement scenario. On the jammer side, a jamming technique is proposed, Doppler-range block jamming (DRBJ), to deny the detection of a DDMA MIMO radar equipped with a moving target detector (MTD). On the radar side, a comparative detection performance analysis of two different MIMO radar detectors, normalized adaptive matched filter (NAMF) and fast 3D constant false alarm rate (CFAR), is conducted using Monte Carlo simulations. This analysis unveils divergent design philosophies of the two detectors, with each prioritizing distinct performance objectives.

In downlink multiple streams multiple-input multiple-output (MIMO) system, adaptive modulation and coding (AMC) is used to increase spectrum efficiency (SE) due to selecting the modulation and coding rate scheme (MCS) dynamically. However, there is a gap between UE's signal to interference plus noise ratio (SINR) and that required by the MCS. In this paper, we propose a novel power loading algorithm using the SINR gap. The algorithm converts the SINR gap into the transmit power gap among different streams. Thus BS adjusts transmit power allocation for different streams and selects higher MCS than that without adjusting power. The results demonstrate that this proposed algorithm contributes large gain of SE for single user (SU-) MIMO based on singular value decomposition (SVD) preceding.

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.

This study addresses the challenge of cognitive waveform design for multiple-input–multiple-output (MIMO) radar systems operating in cluttered environments. It focuses on the key practical requirements for transmitting time-domain waveforms and proposes a novel approach. This method first determines the optimal frequency-domain waveform and then designs a time-domain waveform that closely approximates the frequency-domain solution. The primary objective is to enable MIMO radar systems to transmit orthogonal waveforms while accommodating various constraints. A frequency-domain waveform optimization model was initially developed using the principle of maximizing dual mutual information (DMI), and the energy spectral density (ESD) of the optimal waveform was derived using the water-filling method. Next, a time-domain waveform approximation model is constructed based on the minimum mean square error (MMSE) criterion, which incorporates constant modulus and peak-to-average power ratio (PAPR) constraints. To minimize the performance degradation of the waveform, an improved adaptive gradient descent genetic algorithm (GD-AGA) was proposed to synthesize multichannel orthogonal time-domain waveforms for MIMO radars. The simulation results demonstrate the effectiveness of the proposed model for enhancing the performance of MIMO radar. Compared with traditional genetic algorithms (GA) and two enhanced GA alternatives, the proposed algorithm achieves a lower ESD loss and better orthogonal performance.

This paper deals with the problem of waveform design for through-the-wall radar with the presence of signal-dependent interference embedded in white Gaussian noise. Based on the target matched illumination theory, we present a linear combination method (LCM) to design the transmitted waveform for target detection. The LCM divides the waveform design problem into two independent optimization problems, and combines the solutions of them linearly. Simulation results depict that the LCM has better convergence performance and computational efficiency comparing with the alterative algorithm, while getting a signal to interference plus noise ratio (SINR) improvement.

The aim of this paper is to present a non‐linear statistical model to fit and forecast the signal‐to‐interference plus noise ratio (SINR) in two‐tier heterogeneous cellular networks which consist of macrocells and femtocells. Since in these networks the number and locations of femtocell base stations (FBS) are variable, SINR forecasting can be useful in some areas such as power control and handover management. So far, linear autoregressive (AR) models have commonly been used in forecasting the received signal strength (rss) in macrocellular networks. However, AR modelling results in high mean square error (MSE) when data are non‐linear. This paper focuses on SINR which takes into account signal strength, interference and noise effects. Moreover, macro‐femto cellular network is considered. The F ‐test results show that the SINR data are non‐linear, leading to use non‐linear models instead of AR model. A non‐linear logistic smooth threshold AR (LSTAR) model is utilised to model and forecast the SINR data. Kolmogorov–Smirnov (K‐S) test demonstrates that LSTAR provides good fitness to the SINR samples. The results indicate that LSTAR model achieves much better performance in modelling and forecasting of SINR data than the AR model.

We consider the problem of directional space-time waveform design for proactive interference avoidance in multiple-input multiple-output (MIMO) link configurations. We propose to instruct the transmitter to communicate with the intended receiver at a given code sequence and angle-of-arrival that are jointly optimized to maximize the maximum attainable pre-detection signal-to-interference-plus-noise ratio (SINR). More specifically, directional transmissions to the intended receiver are achieved by designing space-time waveforms that maximize SINR at the output of the max-SINR receiver filter. Simulation of the proposed adaptive interference-avoiding space-time waveform designs in MIMO link configurations show superior performance in terms of post-filtering SINR and bit-error-rate (BER) when compared to interference-suppressive non-adaptive receiver designs. Joint optimization of the code sequence and array response vector at the transmitter demonstrate that the proposed spacetime waveform designs perform within 2 dB from the maximum attainable SINR at the intended receiver.

In closed-loop multiple input and multiple output (MIMO) systems, using a transmit power allocation scheme can increase capacity. However, the use of a space division multiplexing (SDM) scheme, which does not separate the MIMO channel into orthogonal channels, causes nonlinearity between the transmit power and the corresponding signal to interference plus noise ratio (SINR) of each data stream at the output of the detector (e.g. utilizing a MMSE detector with parallel interference canceller at the receiver). This makes it difficult to obtain the optimal power allocation value for each data stream. This paper proposes a novel power allocation scheme with an iterative algorithm for MIMO systems to solve the above problem. Furthermore, we incorporate transmit antenna selection into our proposed power allocation scheme in order to attain further capacity enhancement. Several computer simulation results are provided to show the effectiveness of our proposed techniques.

In this chapter we discuss design techniques and constraints that facilitate a waveform that is useful for radar. While signal-to-noise ratio (SNR) and signal-to-interference-plus-noise ratio (SINR) have historically been the primary drivers of waveform design and are certainly discussed here, the growing need for spectral coexistence is eliciting new metrics and design approaches that address a broader set of considerations. This chapter also examines the use of performance prediction models as a means to establish relationships between waveform constraints and SINR performance, which are critical for detection in spectrally crowded environments. An empirical approach is discussed along with models that provide an intuitive way to understand the complex relationships between constraints and SINR performance in dynamic situations.

In this paper, a novel radar waveform phase function design is presented that constructs a radar waveform that spectrally manuevers around in-band co-channel interference (CCI) from active communications users that are co-channel with the radar system. The design approach treats the CCI problem as the physical interpretable problem of optical refraction where the time-frequency (TF) signature of the radar waveform is made to be analogous to a light ray propagating from a start time and position (frequency) to an end time and position (frequency) and will naturally accelerate through the CCI as if the equivalent light ray accelerated in through different mediums (with an index of refraction related to CCI power spectral density (PSD)). Using the Euler-Lagrange equations, we design the radar phase function that is used to modulate a constant modulus (CM) waveform. The radar waveform design will generally result in a nonlinear-frequency modulated (NLFM) waveform that provides improved signal-to-interference-plus-noise-ratio (SINR) on a potential target in the presence of CCI leading to improved probability of detection performance. We provide theoretical analysis and simulation results for the proposed waveform design.