Research on Adaptive Waveform Selection Algorithm in Cognitive Radar

Cognitive radar is a newly emerging intelligent radar that can adaptively change the transmitted signal waveform according to changes in the target and environment to improve the accuracy of target state estimation. In this paper, the running process of cognitive radar adaptive transmission is analyzed, the tracking waveform parameter selection is correlated with the target state estimation and the reinforcement learning model is established. The problem of unknown target state space is solved by the “prioritized sweeping” method and the computational efficiency is improved by replacing “eligibility trace”. The simulation results show that the indirect reinforcement learning method is better than the fixed waveform and the waveform selection algorithm based on the minimum mean square error for the tracking accuracy and state estimation error of the target.

Cognitive radar has the ability to perceive the environment and target information, and complete the work state optimization through analysis and decision. Radar's behavior is defined as all the characteristics of a radar's performance and the process of changing its state according to the law of change and the external influences. In this paper, the adaptive selection behavior principle of radar emission waveform is firstly described. Then, for the waveform adaptive selection behavior, a black box model is constructed by using neural network. The environment, target characteristics and waveform parameters are used as input training data, and the waveform selection result is taken as output. By learning the training waveform selection process of the radar, we will obtain a neural network model with the same capabilities as the radar adaptive waveform selection system, thus completing the identification of the radar adaptive waveform selection behavior. Finally, through simulation analysis, the method has good results.

The natural flow regime can sustain the ecological integrity of riverine ecosystems. Different reservoir operation polices differ in their effects on the degree of alteration of natural flow regimes. Dynamic programming plays an important role in developing operation policies. When using dynamic programming models to develop operation policies, the discrete number of storage states (DNSS), which is a key factor affecting the reservoirs operation policies, is always been determined based on computational efficiency and economic benefits. Little consideration has been given to the ecological disturbance caused by different DNSS‐based operation policies. To analyze the impact of DNSS, we built a deterministic dynamic programming model to explore the relationship among DNSS, the flow regime alteration (ecological disturbance), and the cumulative annual power generation (economic benefits) by setting a range of DNSS scenarios. We used three reservoirs with different storage coefficients (ratios of usable storage to annual average runoff) as examples and used the range of variability approach to assess the ecological disturbance under these scenarios. We compared the results with those of a stochastic dynamic programming (SDP) model and a Bayesian SDP (BSDP) model. We found that when DNSS is low, increasing DNSS improves economic benefits but causes a more severe ecological disturbance; when DNSS is high, increasing DNSS improves the economic benefits only slightly, without exacerbating the ecological disturbance; for a given DNSS, the BSDP model provides higher economic benefits than the SDP model and a similar disturbance of the riverine ecosystem; and larger reservoirs more often cause more severe disturbance of riverine ecosystems because monthly mean flows and annual extreme flows change more drastically. Our results will help to protect the riverine ecosystems and improve economic benefits if reservoir operation managers consider DNSS using dynamic programming models.

With the continuous progress of science and technology, more extensive use of electromagnetic spectrum will make environment become more and more complex. So radar should develop towards intelligent direction and combat with complex and changeable working environment. In this paper, based on the analysis of monopulse radar, we study the relationships of detection probability versus SNR under four Swerling models. Simulations results show the importance of adaptive waveform selection. It can be concluded that adaptive waveform selection is very necessary in intelligent radar. Finally, the whole paper is summarized.

A comprehensive Genetic Algorithm (GA) model has been developed and applied to derive optimal operational strategies of a multi-purpose reservoir, namely Perunchani Reservoir, in Kodaiyar Basin in Tamil Nadu, India. Most of the water resources problem involves uncertainty, in order to see that the GA model takes care of uncertainty in the input variable, the result of the GA model is compared with the performance of a detailed Stochastic Dynamic Programming (SDP) model. The SDP models are well established and proved that it takes care of uncertainty in-terms of either implicit or explicit approach. In the present study, the objective function of the models is set to minimize the annual sum of squared deviation from desired target release and desired storage volume. In the SDP model the optimal policies are derived by varying the state variables from 3 to 9 representative class intervals, and then the cases are evaluated for their performance using a simulation model for longer length of inflow data, generated using a Thomas–Fiering model. From the performance of the SDP model policies, it is found that the system encountered irrigation deficit, whereas GA model satisfied the demand to a greater extent. The sensitivity analysis of the GA model in selecting optimal population, optimal crossover probability and the optimal number of generations showed the values of 150, 0.76 and 175 respectively. On comparing the performance of SDP model policy with GA model, it is found that GA model has resulted in a lesser irrigation deficit. Thus based on the present case study, it may be concluded that the GA model performs better than the SDP model.

In this paper we extend the idea of adaptive waveform selection for radar target tracking to interacting multiple model (IMM) trackers to permit the modelling of maneuvering targets by allowing multiple possible dynamical models. We develop a one step ahead solution to the problem of waveform selection, which is designed to decrease dynamic model uncertainty for the target of interest. It is based on maximization of the expected information obtained about the dynamical model of the target from the next measurement. We also discuss the design of waveform libraries for target tracking applications.

Keywords Invasive species, Economics, Californian thistle, New Zealand, Stochastic, Dynamic programming, Biological control, Extinction risk, Herbivory, Dispersal, Competition Invasive species are one of the most significant threats to biodiversity and agricultural production systems leading to huge worldwide economic damages. This thesis has two main aims. The first aim is to analyse the control of an invasive plant in an agricultural system, using the case study of the Californian thistle in New Zealand. The second aim is to study the negative externalities that controlling invasion in agriculture can pose to ecosystems. To achieve the first aim, both deterministic and stochastic dynamic programming models are set up to find cost effective methods to tackle the problem of Californian thistle. I make a contribution to the literature by performing a dynamic and stochastic programming analysis in which two different categories of control strategies are considered, each with different dynamics. Models are set up with a discrete decision variable consisting of 62 feasible combinations of integrated control strategies. For the second aim I introduce a novel modelling approach in which two compartments are distinguished: a managed compartment where locally a herbivore is introduced to control a weed, and a natural compartment where the same herbivore species can attack a wild plant species. The main processes are herbivory, competition, dispersal and control. I conclude that bioeconomic modelling is an important tool in analysing optimal management strategies for the control of invasive species and that annual and once and for all choices need to be integrated in the analysis. A stochastic approach is appropriate but does not necessarily lead to different results, depending on the parameter values and the setup of the model. Finally, the method illustrates that an integrated analysis of the economic system and the ecological system is required to assess the risk of extinction of natural plant species. This risk depends on species interactions which in this thesis are competition, dispersal and herbivory. I conclude that a control measure can protect the desirable wild plant species and increase benefits obtained from the ecosystem. For the policy implications, I conclude that there are several strategies to control invasive species, which can be integrated combinations of control options. The optimal strategy depends on the costs and benefits of the control options. In the case study for the Californian thistle I found that the optimal strategy is a combination of methods. For the interaction between agricultural and natural system I conclude that introducing a biological agent to the agricultural system can cause extinction of a desirable plant in the natural system. The main processes are competition, herbivory and dispersal. These processes are important and need to be analysed in detail before introducing the biological agent. I conclude that the optimal strategy to control the introduced biological agent also depends on interaction of species through competition, dispersal and herbivory.

Although there have been numerous studies on the economics of weed control, relatively few of these studies have focused on natural ecosystems. The aims of this papers are: A) to identify the combination of control options that is optimal for blackberry (Rubus anglocandicans) in Australian natural ecosystems, B) to assess whether an integrated control strategy is superior to chemical-only strategies, C) to evaluate the economic net benefits of specific biologically oriented control methods (a rust and grazing by goats), D) to determine how changes in model parameters affect the optimal control strategy. To address these aims, a stochastic dynamic simulation model and a stochastic dynamic programming model are developed. The results indicate that, while an integrated strategy combining chemical and non-chemical control methods may be optimal in certain circumstances, it is not necessarily superior to a chemical-dominant strategy in all cases. Results show that in most cases a combination of herbicides and one physical control method (mowing) is optimal in the base-case scenario. However, if chemical efficacy is greater than in the base-case, the optimal strategy involves chemicals only. The results indicate that grazing goats for control of blackberry can be optimal despite its stochastic effectiveness and relatively low efficacy.

In this paper, we present a scheme to improve immune cloning selection algorithm. The improved algorithm, which is referred to as adaptive cloning selection algorithm (ACSA), is proposed and then applied to function optimization. At first, we present adaptive gene mutation which decides mutation probability of each code point (locus) based on the quality of antibodies and the number of evolution iterations. Secondly, we present an iteratively increasing method from one locus to the all ones, which can be used in function optimization. Then, the cloning selection process of evolution is divided into two-stages. The first step is to increase locus of antibodies. In the other one, the Baldwin effect learning operator is employed. And finally, an experiment is carried out to verify the theoretical analyses on several testing functions.

Optimal Waveform Selection in Range-Doppler Imaging

Gains in processing power and advances in radar signal processing enable waveform diversity. It has been demonstrated that much can be gained with waveform diversity when previous knowledge about the environment is available, or through adaptive clutter estimation. For tracking systems, it has been also demonstrated that waveform variations in response to tracker outputs can also be beneficial. The paper focuses on the latter line of research, presenting a survey and new results pertaining to adaptation for waveform selection in tracking systems. In particular, we introduce a golden standard for adaptive methods, and show that the golden standard is satisfied for a simple adaptive law. Numerical simulations are presented to illustrate the main results in the paper.

Modern phased array radars are able to adaptively modify their performance to the environment. To make full use of this capability, scheduling algorithms need to be designed. This paper poses the problem of adaptive waveform scheduling for detecting new targets in the context of finite horizon stochastic dynamic programming. The result is a scheduling algorithm that minimises the time taken to detect new targets, detecting these targets in accordance with importance, while minimising the use of radar resources.

The previous two chapters of the book have focused on the basic issue: perception of the environment from two different viewpoints. Specifically, Chapter 3 focused on perception viewed as power-spectrum estimation, which is applicable to applications such as cognitive radio, where spectrum sensing of the radio environment is of paramount importance. In contrast, Chapter 4 focused on Bayesian filtering for state estimation, which is applicable to another class of applications exemplified by cognitive radar, where estimating the parameters of a target in a radar environment is the issue of interest. Although those two chapters address entirely different topics, they also have a common perspective: they both pertain to the perceptor of a cognitive dynamic system, the basic function of which is to account for perception in the perception–action cycle. In this chapter, we move on to the action part of this cycle, which is naturally the responsibility of the actuator of the cognitive dynamic system. Just as the Bayesian framework provides the general formalism for the perceptor to perceive the environment by estimating the hidden state of the environment, Bellman's dynamic programming provides the general formalism for the actuator to act in the environment through control or decision-making. Dynamic programming is a technique that deals with situations where decisions are made in stages (i.e. different time steps), with the outcome of each decision being predictable to some extent before the next decision is made.

ABSTRACT: A stochastic dynamic programming model is applied to a small hydroelectric system. The variation in number of stage iterations and the computer time required to reach steady state conditions with changes in the number of storage states is investigated. The increase in computer time required to develop the storage probability distributions with increase in the number of storage states is reviewed. It is found that for an average of seven inflow states, the largest number of storage states for which it is computationally feasible to develop the storage probability distributions is nine. It is shown that use of the dynamic program results based on a small number of storage states results in unrealistically skewed storage probability distributions. These skewed distributions are attributed to “trapping” states at the low end of the storage range.

The integration of natural gas and electricity sectors has increased sharply in the last decade as a consequence of combined cycle natural gas thermal power plants. In some countries such as Brazil, gas-fired generation has been a major factor in the overall growth of natural gas consumption. When related to the operations planning, in some hydrothermal systems, a national system operator dispatches these gas-fired plants (along with other thermal sources such as coal, oil, and nuclear) in conjunction with the country’s hydroelectric plants by using a production-costing model based on stochastic programming. The algorithm determines the optimal hydro-to-thermal energy production ratio on the basis of the expected benefit of reducing thermal plant generation over a large number of hydrological scenarios, along a planning horizon of some years. This means that the optimal scheduling decision today depends on the assumptions about future load growth and future entrance of new generation capacity. Stochastic dynamic programming models are extensively used. However, the hydrothermal scheduling models usually do not take into account the possibility of future fuel supply constraints, either in production or in transportation. The assumption of fuel supply adequacy is felt to be reasonable for the more mature markets such as coal and oil. However, because of the fast growth of the natural gas market, it is possible that demand outpaces supply or transportation investments. Indications that gas-related constraints could be relevant were observed in New England, in the US, and in Brazil in 2004, where several megawatt of combined-cycle generation could not be dispatched when needed due to constraints in pipeline capacity. The objective of this work is to present a methodology for representing the natural gas supply, demand, and transportation network in the stochastic hydrothermal power scheduling model. Application of the integrated electricity–gas scheduling model is illustrated in case studies, with realistic configurations of the 90GW Brazilian system.KeywordsHydroelectric-thermal power generationNatural gas industryStochastic dual dynamic programming