## 2 Concepts and Architecture of Multi-Sensor Signal Processing
### Multi-objective Design of Intelligent Multi-Sensor Systems  
As became clear from the previous discussion, the automated design of intelligent multi-sensor system commonly is a multi-objective design problem. In addition to the primary goal of excellent decision-making performance, numerous other goals must be met. This is illustrated in detail in Figure 4. Appropriate assessment and optimization methods are required to support the demanded multi-objective decision-making. Agglomerative or Pareto-approach [21] could be employed here. In particular, some of the goals or constraints shown in Figure 4 relate to hardware properties. In particular, sensor and analog electronics impose static and dynamic constraints that should be known at design time. Even digital implementations, if design and deployment platform are substantially different, will constrain the implementation. In part, these constraints could be modeled _a priori_ and incorporated into the optimization activities of the design to find a solution with acceptable discrimination honoring the given constraints. In terms of evolutionary computation, this approach is commonly denoted as extrinsic evolution. Figure 5 shows the feedback of target hardware platform constraints to the design system running on the resources of an unconstrained computational platform, e.g., PC or workstation, to be incorporated in the design process (top arrow, case 1). Figure 4: Objectives and constraints to be considered in designing of intelligent multi-sensor systems. This kind of constraint modeling and honoring will work, if indeed at design time the actual constraints can be accurately predicted, _i.e._, modeled accurately, and no dynamic changes occur. This can be assumed for, e.g., reduced accuracy of a fixed-point architecture and its computational implications. However, moving forward from the deployment of a single prototype to real-world deployment of a potentially large volume of instances, additionally effects have to be needed for successful system design. Figure 6 shows three phases of system design and applications, that is, design time so far covered by the preceeding discussion, as well as deployment time and operation time. In deployment time, the system solution evolved so far is duplicated or copied to numerous hardware instances. These will all show instance specific static variations or deviations, due to well-known manufacturing tolerances. Thus, a tuning of the exisiting system solution in the light of the hardware deviations is required for compensation and restoration of a uniform performance over the  
Figure 5: Feedback of hardware platform related constraints in the design process. Figure 6: Three different phases of the system design and application cycle. systems lot. Generally, this requires feedback of hardware-level system performance, as in industrial circuit trimming. Figure 5 already depicts the mapping of the system solution to the hardware platform (bottom arrow) and the result or performance retrieval for optimization (top arrow, case 2.). This kind of optimization loop, taking hardware in the loop, is denoted as intrinsic evolution or self-trimming, or, in the history of neural computation, as chip-in-the-loop-learning [52]. It must be mentioned here, that in particular, putting the sensors in the loop, as suggested also by researchers from NASA in evolvable hardware [47], requires significant physical effort or sophisticated alternative solutions. Finally, at operation time, the trimmed system instances face aging and drift due to environmental changes. This could also comprise both hard or soft defects encountered in microelectronic circuits. Continuous self-monitoring of the achieved system, followed by continued self-trimming or self-repair/healing is required. For digital processing reconfigurable circuits, e.g., FPGA, are commonplace and widely available to render such self-x functionality. For the crucial case of sensor front-ends and electronics, reconfigurable techniques are also gaining more and more ground.