Graphical abstractDisplay Omitted HighlightsProposed a new multi-objective Symbiotic Organisms Search algorithm.Performance is validated on 12 unconstrained and 6 constrained problems.The real life applications are demonstrated on constrained truss design problems. Many real world engineering optimization problems are multi-modal and associated with constrains. The multi-modal problems involve presence of local optima and thus conventional derivative based algorithms do not able to effectively determine the global optimum. The complexity of the problem increases when there is requirement to simultaneously optimize two or more objective functions each of which associated with certain constrains. Recently in 2014, Cheng and Prayogo proposed a new metaheuristic optimization algorithm known as Symbiotic Organisms Search (SOS). The algorithm is inspired by the interaction strategies adopted by the living organisms to survive and propagate in the ecosystem. The concept aims to achieve optimal survivability in the ecosystem by considering the harm and benefits received from other organisms. In this manuscript the SOS algorithm is formulated to solve multi-objective problems (termed as MOSOS). The MOSOS is combined with adaptive penalty function to handle equality and inequality constrains associated with problems. Extensive simulation studies are carried out on twelve unconstrained and six constrained benchmark multi-objective functions. The obtained results over fifty independent runs reveal the superior performance of the proposed algorithm over multi-objective colliding bodies optimization (MOCBO), multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm II (NSGA-II) and two gradient based multi-objective algorithms Multi-Gradient Explorer (MGE) and Multi-Gradient Pathfinder (MGP). The engineering applications of the proposed algorithm are demonstrated by solving two constrained truss design problems.

A Symbiotic Organisms Search algorithm with adaptive penalty function to solve multi-objective constrained optimization problems

We propose a multiobjective optimization (MOO) technique to design an orthogonal-frequency-division multiplexing (OFDM) radar signal for detecting a moving target in the presence of multipath reflections. We employ an OFDM signal to increase the frequency diversity of the system, as different scattering centers of a target resonate variably at different frequencies. Moreover, the multipath propagation increases the spatial diversity by providing extra “looks” at the target. First, we develop a parametric OFDM radar model by reformulating the target-detection problem as the task of sparse-signal spectrum estimation. At a particular range cell, we exploit the sparsity of multiple paths and the knowledge of the environment to estimate the paths along which the target responses are received. Then, to estimate the sparse vector, we employ a collection of multiple small Dantzig selectors (DS) that utilizes more prior structures of the sparse vector. We use the l1-constrained minimal singular value (l1-CMSV) of the measurement matrix to analytically evaluate the reconstruction performance and demonstrate that our decomposed DS performs better than the standard DS. In addition, we propose a constrained MOO-based algorithm to optimally design the spectral parameters of the OFDM waveform for the next coherent processing interval by simultaneously optimizing two objective functions: minimizing the upper bound on the estimation error to improve the efficiency of sparse-recovery and maximizing the squared Mahalanobis-distance to increase the performance of the underlying detection problem. We provide a few numerical examples to illustrate the performance characteristics of the sparse recovery and demonstrate the achieved performance improvement due to adaptive OFDM waveform design.

Multiobjective Optimization of OFDM Radar Waveform for Target Detection

Integrating multi-objective optimization with computational fluid dynamics to optimize boiler combustion process of a coal fired power plant

Intensifying competition over the frequency spectrum has driven the research effort into strategies for coexistence between radar and communications. Strategies for achieving this range from spectrum sharing using cognitive radio techniques to co-design where the radar and communications systems are re-imagined to ensure they do not interfere with each other. In this paper we propose a new signaling scheme for Dual-Function Radar Communications (DFRC) that enables frequency-hopped multiple-input-multiple-output (MIMO) orthogonal radar wave-forms to carry communication symbols. The Frequency-hopping (FH) code is changed from subpulse to another such that the index of the selected code carries the desired symbol. Contrary to recent phase-shift keying (PSK)-based schemes which embed one PSK symbol within each subpulse, the proposed scheme does not suffer from phase-discontinuity and is shown to have better spectral efficiency. We show that the data rate that can be achieved using the proposed scheme is proportional to the size of the FH code, the number of transmit antennas, number of subpulses within a pulse repetition interval, and pulse repetition frequency. Simulations examples are provided to evaluate the performance of the proposed method and demonstrate the effectiveness of this information embedding scheme.

Dual-Function MIMO Radar-Communications Via Frequency-Hopping Code Selection

There is a presumed adverse effect on radar operation brought about from embedding communication signals in radar waveforms. Contrary to that presumption, we show in this paper that modulation of the radar pulse can indeed benefit both radar and communications in the dual system paradigm. This is shown by analyzing the impact of phase-shift keying (PSK) symbol embedding on the ambiguity function (AF) of a multiple-input multiple-output (MIMO) radar. Our analysis shows that the embedded PSK symbols yield reduction in the AF sidelobe peaks for a single pulse as well as for a series of radar pulses. To prove these results, we analyze the AF with and without the PSK symbol embedding. It is shown that the embedding of PSK symbols enable increasing the number of orthogonal transmit waveforms that can be used without increasing the sidelobe levels (SLL) of the corresponding AF.

Ambiguity Function Analysis for Dual-Function Radar Communications Using PSK Signaling

Intentionally misguiding a global positioning system (GPS) receiver has become a potential threat to almost all civilian GPS receivers in recent years. GPS spoofing is among the types of intentional interference, in which a spoofing device transmits spoofed signals towards the GPS receiver to alter the GPS positioning information. This paper presents a robust positioning algorithm, followed by a track filter, to mitigate the effects of spoofing. It is proposed to accept the authentic GPS signals and spoofed GPS signals into the positioning algorithm and perform the robust positioning with all possible combinations of authentic and spoofed pseudorange measurements. The pseudorange positioning algorithm is accomplished using an iterative least squares (ILS). Further, to efficiently represent the robust algorithm, the M-best position algorithm is proposed, in which a likelihood-based cost function optimizes the positions and only provides M-best positions at a given epoch. However, during robust positioning, the positions evolved due to spoofed pseudorange measurements are removed to overcome GPS spoofing. In order to remove the fake positions being evolved owing to wrong measurement associations in the ILS, a gating technique is applied within the Kalman filter (KF) framework. The navigation filter is a three-dimensional KF with a constant velocity (CV) model, all the position estimates evolved at a specific epoch are observations. Besides, to enhance this technique’s performance, the track to position association is performed by using two data association algorithms: nearest neighbor (NN) and probabilistic data association (PDA). Simulations are carried out for GPS receiver positioning by injecting different combinations of spoofed signals into the receiver. The proposed algorithm’s efficiency is given by a success rate metric (defined as the navigation track to follow the true trajectory rather than spoofing trajectory) and position root mean square error (PRMSE).

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Navigation in GPS Spoofed Environment Using M-Best Positioning Algorithm and Data Association

Track breakages are common in target tracking due to highly maneuvering targets, association with false alarms or incorrect target-originated measurements, low detection probability, close target formations, large measurement errors, and long sampling intervals, among other causes. Existing track segment association (TSA) algorithms solve this breakage problem by predicting old track segments and retrodicting young track segments to a common time followed by two-dimensional (2-D) assignment. This approach presents two disadvantages. First, these algorithms predict or retrodict from the actual point of termination or beginning of their respective tracks, that is, they neither check if the cause of a track termination was incorrect association nor redress such an erroneous association. Second, these algorithms do not utilize the measurement information during the breakage period. Often, track terminations are due to incorrect measurement association. To solve the first problem, this paper proposes a 2-D assignment-based TSA algorithm that releases incorrectly associated measurements by going backward and forward in time along old and young track segments, respectively, and then performing prediction and retrodiction. Furthermore, to address both shortcomings in existing TSA algorithms simultaneously, we propose a novel multiframe assignment-based TSA algorithm that estimates the track during the breakage period, utilizing both unassociated and released measurements simultaneously. Moreover, the proposed algorithms can handle target maneuvers subject to a single turn during the breakage period. In the proposed solution, model parameters, such as starting time of the turn, ending time of the turn, and turn rate are obtained by maximizing the likelihood that a given measurement-tuple originated from the track couple under consideration. Simulation results demonstrate that the proposed TSA algorithm is superior to existing ones in terms of association accuracy and computational cost.

Comprehensive Track Segment Association for Improved Track Continuity

Global navigation satellite system (GNSS) based navigation is omnipresent in today’s world, providing position, velocity, and time (PVT) information with inexpensive GPS receivers. These receivers are highly vulnerable to intentional interference like GPS spoofing and meaconing. The spoofing of a single GPS receiver using a spoofer setup is widespread, and the concept of spoofing multiple targets with multiple distributed spoofers is also equally adaptable. Traditionally, in distributed spoofers, the multiple spoofers in the surveillance region work independently without knowing other spoofers being installed. Multiple spoofers deployment and its management are optimal for misguiding the multiple GPS receivers in the given surveillance. This paper presents a generalized mathematical model for the multi-spoofer multi-target (MSMT) scenario, spoofer management, and spoofer-to-target association. The received power of spoofed signals is considered as an evaluating parameter for locking the spoofed signals onto the GPS receivers. Three novel centralized networking-based spoofing techniques are proposed to overcome spoofer-to-target association in distributed networking. Firstly, the global nearest neighbor (GNN) based centralized spoofing is proposed. The overall cost of the function is minimized by assigning a unique spoofer-ID to a unique target-ID. In GNN-based centralized spoofing, the overall global cost minimizes, but it does not ensure that every target-to-spoofer assignment is minimum. Secondly, the spoofers of opportunity-based centralized spoofing with the GNN association is proposed to resolve the spoofer-to-target association and to increase the hit ratio. However, it is hard to install more spoofers; therefore, a tunable transmitting power-based centralized spoofing with the GNN association is presented to accomplish efficient spoofer-to-target association and higher hit-ratio. The spoofing efficiency is evaluated using spoofer-to-target association, hit ratio, and position root mean square error (PRMSE). All the proposed algorithms outperform the distributed spoofing. We also observe that the tunable power-based spoofing is an optimal solution in MSMT scenario.

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Spoofer-to-Target Association in Multi-Spoofer Multi-Target Scenario for Stealthy GPS Spoofing

The accelerators are gaining predominant attention in the HW/SW designs and embedded designs due to the less power consumption and parallel data processing capabilities compared to standard microprocessors and FPGA’s. In this paper, MSSKF (Multi-sensor Schmidt–Kalman filter)-based coupled bias estimation problem is considered for single target multiple sensors case. Here MSSKF augments the state vector and bias vector for bias estimation, results in computationally expensive as the dimensions of the state and sensors increases. Hence to address the computational complexity, digital signal processing (DSP) architectures are proposed and accelerated the algorithm to meet the real-time constraints. In the MSSKF algorithm, the overload of the algorithm is due to state covariance prediction and innovation covariance prediction. To realize the state covariance and innovation covariance, a folded DSP architecture and parallel processing based folded DSP architecture are proposed, respectively. The matrix multiplications are addressed with systolic arrays to gain the advantage of latency and parallel processing. Moreover, MSSKF using systolic array architectures simulated and synthesized in Vivado 2018.1 using Verilog and implemented on FPGA-Zynq-7000 board. The performance of the systolic-based accelerator realization was compared with normal matrix multiplication.

Systolic-Architecture-Based Matrix Multiplications and Its Realization for Multi-Sensor Bias Estimation Algorithms

Passive radar detects targets using the reflections of electromagnetic signals illuminated by unintended sources of opportunity in the given surveillance region. The illuminators of opportunity (IOO) like FM, DVB, DAB, LTE, WiMax, and radio frequency signals are used for the passive radar depending on the availability, frequency of operation and, type of application. This paper proposes the upcoming 5G New Radio waveform (5G NR) as an IOO for passive bistatic radar. The 5G NR waveform is used to perform parametric analysis of passive bistatic radar. The radar parameters like range resolution, velocity resolution, range product, maximum unambiguous PRF, and Cassini's ovals are investigated. Further, the 5G NR IOO is compared against existing LTE and other IOOs. Simulation results reveals that all the radar parameters are outperforming for the 5G NR waveform, claiming that 5G NR is a potential candidate for the future IOO.

Pathipati Srihari

Papers

Navigation in GPS Spoofed Environment Using M-Best Positioning Algorithm and Data Association

Comprehensive Track Segment Association for Improved Track Continuity

Spoofer-to-Target Association in Multi-Spoofer Multi-Target Scenario for Stealthy GPS Spoofing

Systolic-Architecture-Based Matrix Multiplications and Its Realization for Multi-Sensor Bias Estimation Algorithms

Analysis of 5G New Radio Waveform as an Illuminator of Opportunity for Passive Bistatic Radar